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Appropriate technology implementation in K-12 classrooms

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Appropriate technology implementation in K-12 classrooms

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Content i Appropriate Technology Implementation in K-12 Classrooms by Danielle Weinstein Rossier School of Education University of Southern California A dissertation submitted to the faculty in partial fulfillment of the requirements for the degree of Doctor of Education May 2022 © Copyright by Danielle Weinstein 2022 All Rights Reserved The Committee for Danielle Weinstein certifies the approval of this Dissertation Ruth Chung, Committee Chair Sandra Kaplan Kimberly Cabrera Rossier School of Education University of Southern California 2022 i Abstract This study sought to provide insight into the current status of technology knowledge and application of technology in K-12 classrooms. Specifically, this study explored the demographic factors such as: gender, race, education level, grade taught, rigor, years of experience, and socioeconomic status that contribute to differences in teachers' technology knowledge and ways technology is implemented. This study also explored the relationship TPACK and years of experience has with technology implementation. Finally, this study addressed the role COVID- 19 played in accelerating the use of technology as students and teachers were required to meet virtually for a year. Technology was added as a third paradigm to Shulman’s (1986) Pedagogical Content Knowledge model to make Technological Pedagogical Content Knowledge (TPACK). Much of the literature focused on demographic factors of TPACK individually, but not comprehensively. Moreover, the majority of the studies focused primarily on pre-service teachers. Very few studies explored the TPACK of in-service teachers. Furthermore, very few studies have focused on the adoption of technology standards and no studies that examine quantitatively in-service teachers technology implementation practices with the 2017 International Society for Technology in Education (ISTE) Standards for Educators. Participants included 425 teachers from a large school district in Southern California that has 31 schools that are economically diverse. Participants completed an online survey consisting of demographic information, familiarity with and use of the 2017 ISTE Standards for Educators, Technological Pedagogical Content Knowledge (TPACK), Teacher Preparation Technology Inventory (TPTI), and the role COVID-19 played in technology implementation. Results revealed that there is an inverse relationship between teacher experience and knowledge and ii implementation of technology, both lower elementary (K-3) teachers, and White/Caucasian teachers have significantly less TPACK and less appropriate technology implementation practices than all other grade levels and races, respectively, finally although Physical Education teachers do not differ significantly than their peers in TPACK knowledge, they implement technology less appropriately than teachers of other content areas. This study also found no significant difference in knowledge or implementation practices across socioeconomic status. These results suggest there is a lot of room for improvement with respect to technology implementation and accountability in K-12 classrooms in order to best prepare students to be college and career ready. iii Dedication To the memory of my daddy, Jason Eric Weinstein. iv Acknowledgments I would not be in the position I am today without the dedication, love and support from each person in my life. I want to take this opportunity to share from the bottom of my heart my deepest and most sincere gratitude to the following people: Dr. Ruth Chung, my chair. My study would not have been possible without your devotion to your practice and constant guidance and support. Thank you for your invaluable advice, your continuous feedback, especially for the late nights and long hours. Your consistency and transparency made this process a lot less daunting and enabled me to create work that I am proud to share. Dr. Sandra Kaplan and Dr. Kimberly Cabrera, my committee members. When I applied to USC, Dr. Kaplan, you were the professor I was most excited to study under and with. You have built a legacy that one can only strive to emulate. Thank you so much for your insight and for mentorship. Dr. Cabrera, thank you for your vision, your leadership and your confidence in me as a learner, an educator and a leader. Thank you for pushing me to grow and pursue bigger dreams. To the both of you, thank you so much for your time, your support and your commitment to my academic and professional goals. Dr. Caitlin Riegel, an unsuspecting mentor. Thank you for your willingness to answer a stranger’s email embracing me as a student and co-creator of knowledge. I really appreciate your insight, your passion and for paving the way with the development of a tool that fills a huge gap in order to support best practices. Jason and Amelia, my parents. You have instilled in me the importance of passion, commitment, and ambition in all that I do. You have also emphasized the role that compassion, love, and understanding plays in everyday life. I appreciate how you were able to bring the v classroom to life through our travels. Seeing peoples, cultures, and environments outside of ours cultivated tolerance, acceptance, and an emphasis on learning. This learning has allowed me to grow and has most importantly allowed me to nurture these same ideals in my own students. There are not enough words or ways to say thank you. I love you. Sissy Poo, the more accomplished sibling. I will just leave it at that. Grandma Joyce, Aunt Stacey, Uncle Marshall and Uncle Carl. Thank you for being in my corner. Aunt Stacey thank you for knowing when I need you and for being there at the exact moment and exact time. Uncle Carl thank you for whispering in my ear to pursue a career in education. My teachers—preschool to UCLA—Ms. Caren Bautista, Ms. D, Mrs. Vaughn, Mrs. Susan Ledbetter, Mr. DiGiovani, Mrs. Krista Landgraf, Mrs. Judy Watson, Mrs. Michelle Buck, Mrs. Tamie Beeuwsaert, Mrs. Lisa Lista, Mrs. Valerie Vera-Mineer, Mr. Kalamar, Mrs. Maureen Bromley, Mrs. Jeffie Mackey, Mrs. Debbie Simon, Mrs. Durban, Mr. Maley, Ms. Lonnie Lewis, Mr. Scott Trautz, Ms. Sally Hartley, Ms. Daphne Hammer, Mrs. Rhonda Fellows, Mrs. Marcia Burrows, Mr. Scott Iverson, Mr. Jeff Chandler, Mrs. Schaefer, Mrs. Peterson, Mr. Massoum, Mrs. Williams, Mr. Fountaine, Mr. Harrison, Mr. Kenny Donovan, Mr. Kliebacher, Mr. Osbourne, Mrs. Valdez, Mrs. Elaine Maxwell, Mrs. Susan Taja, Mr. Jeff Allen, Mrs. Debi Weiss, Mrs. Susan DeVillez, Dr. Zsuzsa Berend, Dr. Anita Yuan, and Dr. Danielle Wondra, Each of you individually and all of you collectively have shaped me as a person and as a lifelong learner. Each of my successes are your successes. All of my current colleagues, especially, Julie Donoho, Dr. Debra Letcher-Boeve, Monica Hyland, Dr. Isabel Brenes, Dr. Patty Salazar, Dr. Bryan Wierzchucki, Dr. Lindsey Vorndran, and Dr. Praisy Poluan. Julie, thank you for being an exemplary teacher in showing me vi what it means to be a passionate dynamic teacher that meets the needs of all students, and for instilling in me the importance of integrity to my profession. Debra thank you for always saying yes to my ideas and holding space for me to learn and grow as an educator. Monica, thank you for your positivity, humor and endless belief in me. Brenes, I am so grateful for our friendship and our carpool conversations. Patty—I will be forever grateful for our trips to Traditions and standing by my side. Bryan, Lindsey, and Praisy thank you for being a strong support system in this program and for our beautiful friendship that has evolved. Future students and colleagues. Thank you for what I have yet to learn and that you will be on my journey to teach me. Alexandria Craig, Jessica Jones Marquardt, Cyndi Boyd, Danice Akiyoshi, Jessica Salgado and Janet Salgado-Leveratto. You all have been my greatest cheerleaders and have been guiding lights in my personal and ongoing development. vii Table of Contents Abstract ............................................................................................................................................ i Dedication ...................................................................................................................................... iii Acknowledgements ........................................................................................................................ iv List of Tables ................................................................................................................................. ix List of Figures ................................................................................................................................ xi Chapter One: Overview of the Study .............................................................................................. 1 Statement of the Problem .................................................................................................... 4 Background of the Problem ................................................................................................ 5 Purpose of the Study ........................................................................................................... 9 Conceptual Framework ....................................................................................................... 9 Significance of the Study .................................................................................................. 12 Definition of Terms........................................................................................................... 14 Chapter Two: Review of the Literature ........................................................................................ 16 Technological Pedagogical Content Knowledge (TPACK) ............................................. 16 International Society for Technology in Education Standards for Educators 2017 ......... 22 Demographic Groups ....................................................................................................... 25 Purpose, Research Questions & Hypotheses ................................................................... 34 Chapter Three: Methodology ........................................................................................................ 38 Participants ........................................................................................................................ 38 Instrumentation ................................................................................................................. 43 Data Collection Procedures ............................................................................................... 47 Chapter Four: Results or Findings ................................................................................................ 49 Results Research Question One ........................................................................................ 49 Results Research Question Two ....................................................................................... 62 viii Results Research Question Three ..................................................................................... 64 Results Research Question Four ....................................................................................... 79 Results Research Question Five........................................................................................ 85 Chapter Five: Discussion .............................................................................................................. 86 Summary and Discussion of Main Findings ..................................................................... 86 Implications for Practice ................................................................................................... 96 Limitations ........................................................................................................................ 99 Conclusions ..................................................................................................................... 101 References ................................................................................................................................... 103 Appendix A: Consent Form ........................................................................................................ 117 Appendix B: Demographics (Self-created) ................................................................................. 118 Appendix C: Prior Knowledge (Self-created) ............................................................................. 119 Appendix D: Knowledge-TPACK Survey (Seven subscales 0.75-0.92) .................................... 120 Appendix E: Skills-TPTI (Seven subscales 0.88-0.96) .............................................................. 122 Appendix F: Role of COVID-19 (Self-created).......................................................................... 125 ix List of Tables Table 1: Definitions of TPACK by Subscale 17 Table 2: Quick Facts—Comparison of Cities A, B and C 39 Table 3: Demographic Frequencies of Teacher Participants 41 able 4: Descriptive Statistics of TPACK 50 Table 5: Univariate Analysis of Variance for Race in TPACK (seven subscales) 52 Table 6: Means and Standard Deviations of TPACK by Race 52 Table 7: Univariate Analysis of Variance for Grade Level in TPACK (seven subscales) 55 Table 8: Means and Standard Deviations of TPACK by Grade Level 55 Table 9: Univariate Analysis of Variance for Rigor in TPACK (seven subscales) 57 Table 10: Means and Standard Deviations of TPACK by Rigor 58 Table 11: Univariate Analysis of Variance for Years of Experience in TPACK (seven subscales) 60 Table 12: Means and Standard Deviations of TPACK by Years of Experience 60 Table 13: Teachers’ Familiarity of ISTE 2017 (mean = 1.96) 64 Table 14: Descriptive Statistics of TPTI 65 Table 15: Univariate Analysis of Variance for Race in TPTI (seven subscales) 67 Table 16: Means and Standard Deviations of TPTI by Race 67 Table 17: Univariate Analysis of Variance for Grade Level in TPTI (seven subscales) 70 Table 18: Means and Standard Deviations of TPTI by Grade Level 70 Table 19: Univariate Analysis of Variance for Subjects in TPTI (seven subscales) 73 x Table 20: Means and Standard Deviations of TPTI by Subjects 74 Table 21: Univariate Analysis of Variance for Years of Experience in TPTI (seven subscales) 76 Table 22: Means and Standard Deviations of TPTI by Years of Experience 77 Table 23: Summary of Simultaneous Regression Analysis for TPACK and TPTI 87 xi List of Figures Figure 1: Pedagogical Content Knowledge 10 Figure 2: Technological Pedagogical Content Knowledge (TPACK) 11 Figure 3: Graphic Distribution of TPACK Means 50 Figure 4: Teachers’ Familiarity of ISTE 2017 63 Figure 5: MEANS of TPTI 65 Figure 6: Role of COVID-19 on Technology Implementation 85 1 1 Chapter One: Overview of the Study There has been a technological boom in the last 20 years—from mass distribution in terms of access to personal computers and cell phones, to the Internet and countless applications, to the emerging technologies in artificial intelligence, virtual reality and the like. Technological advances and inventions have revolutionized the way humanity interacts in the world personally, socially and professionally that have local, national and global economic consequences. This proliferation of technology extends to education—with its power yet to be fully understood or harnessed. Approximately 30% of California teachers earned their teaching credential before 2003, the year Google was founded, and a little less than 50% of California teachers earned their credential before 2007, the year the first iPhone was released (EdSource, 2020). This means that right now, between one third and half of teachers in the workforce earned their credentials before the technological boom. Thus, unless in-service teachers have actively sought to learn about how to operate and incorporate technology into the classroom on their own or through district professional learning, then nearly half of current teachers in California are underprepared to most effectively leverage technology in the classroom to prepare students to compete in the 21st century marketplace. The current educational system was modelled after a factory assembly line—separate subjects, ringing bells, grouping students by age, etc. in order to prepare students to work in factories (Robinson, 2010). This model is now antiquated as a majority of students now need to be prepared to work in STEM related fields (National Science Board, 2015). The United States Department of Education’s mission statement reads, “Our mission is to promote student achievement and preparation for global competitiveness by fostering educational excellence and 2 2 ensuring equal access” (US Department of Education, Footer, 2021). Our current educational system is failing our students and our country in both preparation for global competitiveness and in ensuring equal access. In addition to the majority of teachers being underprepared to appropriately implement technology within their curricula, there is no accountability system in place to ensure equitable, consistent, and high-quality technology practices. Technology is implemented inconsistently across socioeconomic status classrooms. Access and use are two institutional practices that disproportionately advantage the dominant group over historically marginalized groups in terms of access to technology. This gap, inequitable access of technology among students by socioeconomic status, is known as the digital divide (Van Dijk & Hacker, 2003). The discrepancy in access to hardware between students of high socioeconomic status and low socioeconomic status was the primary definition of the digital divide. However, as more equitable access to hardware was addressed, access was further categorized into four branches: mental access, material access, skills access and usage access (Van Dijk, 1999). Without material access, students' skill, access and usage access are insurmountably hindered. Therefore, as previously mentioned, there has been a political and community push for equitable access of more hardware for students in low-income areas. To close the material access gap, in addition to private donations from major technology companies, low-income schools use money from the Elementary and Secondary Education Act Title I to fund the purchase of more devices (O’Hanlon, 2009). As material access became more balanced in the number of devices per school/student, the shift in the digital divide began to focus on the skill deficit as facilitated through inequitable usage practices. Privileged students are using technology in more advanced and creative ways 3 3 than their underprivileged peers. For example, students from low socioeconomic status areas have been observed to mainly use technology for drill and practice whereas students from high socioeconomic areas are using technology for research, investigations and creations (DiMaggio, et al., 2004; Warschauer, et al., 2002; Wayne, et al., 2002). This usage difference is widening the gap in students’ technology skills, and later their ability to compete for jobs in the workplace which in turn perpetuates the oppression of historically disadvantaged groups. In the early 2010s Common Core (CCSS) and the Next Generation Science Standards (NGSS) were introduced and implemented in English, mathematics, and science across the country in efforts to help students be college and career ready (Duncan, 2010; Obama, 2011). Yet, technology is not provided the same level of vitality as other core subjects. Technology does not fit in a neat, segmented box like the factory model—educational technology transcends all subject areas. Therefore, no one teacher is responsible for appropriate technology implementation, instead all teachers should collectively be accountable for effective and appropriate technology use. However, at the present moment educators are not held accountable to the quality or appropriateness of technology use in the classroom. As a result, technological practices vary greatly in classrooms across the country. Inequitable implementation practices contribute to historically marginalized populations being grossly underrepresented in STEM careers (Andrews, 2002; Andriole et al., 2008; Fealing, 2015; Maton et al., 2006; U.S. General Accounting Office, 2005; and Varki and Rosenberg, 2002). This underrepresentation of historically marginalized populations in STEM careers is important because it further contributes to the gap in wealth between the dominant and minority groups. Those in STEM careers make approximately $11,000 more than people in non-STEM careers (PEW Research Center, 2018). 4 4 Recognizing the need for improvement, many leading tech giants in the private sector have stepped up in attempts to fill the gap in technological preparedness. The Partnership for 21st Century Skills (funded by industry leaders such as: TimeWarner, Ford, Microsoft, Verizon, Cisco Systems, Dell, Pearson, and many more) have not only advocated for the infusion of technology in the K-12 classrooms, but have also provided tools and resources to facilitate that effort (National Education Association, 2019). For example, The Bill and Melinda Gates Foundation has privately funded charter schools like High Tech High in San Diego, California with over seventeen million dollars since its inception in 2000 (The Bill and Melinda Gates Foundation, 2020). Noticing the gap in students’ skills and industry needs, these companies have taken an active role in ensuring quality technology education (Casner-Lotto & Barrington, 2006). Even with the support of industry leaders, changes have not occurred at large enough scale to make a systemic impact. The education system itself needs to match the private sector's level of concern and garner it to systemically educate and implement technology appropriately in all K-12 classrooms. Without intervention, nearly 50% teachers will remain underprepared to appropriately use technology in the classroom, that coupled with lack of accountability will continue to widen the gap of knowledge and skills which directly impacts students’ college and career readiness and perpetuates inequities. Statement of the Problem Use of technology in the classroom is often executed ineffectively and inconsistently across classrooms due to the fact that most teachers lack formal training and because a national set of technology standards has not been adopted. It is important to study what current educators know about technology and how they are currently using it in their classrooms. 5 5 Background of the Problem According to the Department of Education (2020), academic standards are “benchmark measures that define what students should know and be able to do at specified grade levels beginning in kindergarten and progressing through grade twelve” (para. 1). Standards are used by educators to drive instruction and make instructional decisions in order to meet the needs of the students. Standards serve to help ensure equity for all students. Accountability in Education Even after more than 66 years, since the passage of Brown v. the Board of Education, schools in lower income areas have fewer resources than schools in more affluent areas (Levine & Levine, 2012). Over time, the achievement between students from lower socioeconomic areas and higher socioeconomic status areas have widened (Ladson-Billings, 2006). Politicians and presidents since Ronald Reagan to Bill Clinton, George W. Bush and Barack Obama have placed high importance on implementing initiatives to try and improve public education for students in the United States. In 1981, the National Commission on Excellence in Education, chaired by David Gardner, was charged with reporting on the state of the quality of public education in the United States and their report, “A Nation at Risk” spotlighted our failing school system (National Commission on Excellence in Education, 1983). In 2002, when George W. Bush was president, he introduced No Child Left Behind (NCLB), an initiative that went into law that reauthorized the Elementary and Secondary Act that made Title I funds available to low-income schools. NCLB was rooted in neoliberal ideology. At that time, it was believed that students would succeed if all students were taught uniformly (Davies & Bansel, 2007; Hill & Kumar, 2012; and Ross & Gibson, 2007). NCLB intended to increase accountability of school performance by 6 6 requiring states to create state specific standards and requiring schools to make Adequate Yearly Progress (AYP) in order to continue to receive funding. This framework was replaced by the ideology of differentiation and student-centered learning—meeting the individual needs of students so that all can succeed. Seven years later, in 2009, under the leadership of President Barack Obama, he challenged the educational system to evolve and center on preparing students to be college and career ready (Duncan, 2010; Obama, 2011). Together, the National Governors Association and the Council of Chief State School Officers, created a national set of kindergarten through twelfth grade standards known as Common Core State Standards (CCSS). Aligned with President Obama’s mission, the standards focused on a 21st century curriculum and expectations that would facilitate student achievement in order to be college and career ready by high school graduation. In an effort to accelerate the national adoption of CCSS, states competed to receive additional funding from the federal Race to the Top grants. Race to the Top had four requirements in order to apply for part of the 4.35 billion dollars: ● Adopting standards and assessments that prepare students to succeed in college and the workplace and to compete in the global economy; ● Building data systems that measure student growth and success, and inform teachers and principals about how they can improve instruction; ● Recruiting, developing, rewarding, and retaining effective teachers and principals, especially where they are needed most; and ● Turning around our lowest-achieving schools. (United States Department of Education, 2009). 7 7 CCSS only addresses English and Mathematics. In 2012, the Next Generation Science Standards (NGSS) were introduced in regards to science with the same philosophy—standards aimed to develop students to be college and career ready. The transition from NCLB with the scripted curriculum is fundamentally and philosophically very different from CCSS and NGSS where teachers are charged with often creating their own curriculum in order to better differentiate for the needs of their students. To clearly present and sustain his vision, President Obama signed the Every Student Succeeds Act (ESSA) on December 10, 2015, that included provisions relating to 1) the adoption of standards; 2) assessments; 3) school accountability; 4) improving struggling schools; and 5) state and local report cards. The ESSA replaced NCLB. Obama’s effort accelerated the adoptions of these new national standards. International Society for Technology in Education (ISTE) Unlike core subjects such as English/Language Arts, mathematics, and science there are no nationally adopted technology standards. Several stakeholders have written standards for technology in education including: InTASC (created by Council of Chief State School Officers), CAEP (Council for the Accreditation of Educator Preparation), NBPTS (National Board for Professional Teaching Standards) and ISTE (International Society for Technology Education). However, ISTE’s technology standards are written in such a way that they “can serve as a concrete indicator to measure teacher technology-competency” (Kimm, et al., 2020, p. 4). The International Society for Technology in Education (ISTE), a non-profit company, has created several iterations of technology standards (ISTE, n.d.b). The iterations of these standards have been randomly adopted across the country. For example, California has officially adopted the 2007 standards, whereas Nevada has adopted the 2016 ISTE Standards for Students, but Utah is still using the 1998 ISTE standards (ISTE, n.d.a). Furthermore, the adoption varies at the 8 8 district level—even though the state of California has adopted the 2007 ISTE standards, Los Angeles County Unified School District has adopted the most recent version of the standards, the 2016 ISTE Standards for Students, 2017 ISTE Standards for Educators and 2017 ISTE Standards for Leaders (LAUSD, 2017). This study will focus on the 2017 ISTE Standards for Educators. Committed to “empower[ing] learners to flourish in a connected world by cultivating a passionate professional learning community, linking educators and partners, leveraging knowledge and expertise, advocating for strategic policies, and continually improving learning and teaching,” (ISTE, n.d.a., para. 1). ISTE recognized the need to scaffold educators to be able to leverage technology in order to further develop students’ skills so that they are college and career ready. Shortly after the introduction of CCSS and NGSS, the ISTE released their 2017 iteration of technology standards that complement CCSS and NGSS and are aligned with Obama’s mission of college and career readiness. Technology is evolving at a rapid pace, and the standards should reflect this evolution. To date, ISTE has had three iterations of standards 1998, 2007 and 2017. As capacity changed so have the standards; ISTE has acknowledged the ever-changing nature of technology and designed the standards to only serve the field for 5-10 years ISTE, 2017, Riegel, 2018). In 1998, ISTE’s first iteration of standards were teacher-centered and focused on students learning to use technology (ISTE, 2017; Riegel, 2018). The second iteration of technology standards in 2008 were student-centered and focused on supporting learning with technology (Smith, 2017; ISTE, 2017; Riegel, 2018). The current iteration, ISTE 2017’s goal is to transform pedagogy with technology (ISTE, 2017). ISTE has released standards specific to students, educators, education leaders and coaches that all complement and support the mission to empower students. 9 9 Purpose of the Study The overall goal of this study is to investigate teachers' knowledge and how teachers use technology in the classroom across demographic groups to ensure equitable and high-quality technology implementation in K-12 classrooms. In order to do this, the study will first explore demographic groups differences of teacher knowledge (TPACK), familiarity with the ISTE 2017 Standards for Educators and the application of technology, respectively. Then, this study will examine the relationship between knowledge (TPACK) and skills (TPTI). Finally, the study will conclude with a discussion of the role COVID-19 played in the implementation of technology this year. Theoretical and Conceptual Frameworks This study will use two main theoretical and conceptual frameworks, a progressivist worldview and the theoretical framework of Technological Pedagogical Content Knowledge (TPACK). Through a progressivist lens, the researcher seeks to understand how teachers use their TPACK knowledge to create conditions in the classroom for diverse students to develop 21st century skills that will enable them to be successful post high school graduation. Progressivists believe that knowledge is constructed by students developing critical thinking skills as they actively explore and discover the content through real-world contexts (Zilversmit, 1993). Other tenets of progressivism include individualism and innovation, and practices include student-centered learning (active over passive learning), the teacher as the facilitator, and student autonomy/choice. Historical progressivists include, but are not limited to John Locke, Jean- Jacques Rousseau, John Pestalozzi, Friedrich Froebel, John Dewey, Jerome Bruner, and Ralph Tyler. The progressivist philosophy underlies the ideology in CCSS, NGSS and the 2017 ISTE 10 10 Standards for Educators and the mission of education to prepare students to be college and career ready. Evolution of the Systems of Teaching: CPK to TPACK Until 2006, there were two main paradigms of education: pedagogy knowledge and content knowledge (Shulman, 1986). Education is the combination of pedagogy—teaching practices, with content knowledge—the subject matter. In 1986, Shulman wrote, “What distinguishes mere craft from profession is the indeterminacy of rules when applied to particular cases. The professional holds knowledge, not only of how the capacity for skilled performance— but of what and why” (p. 13). In his article, “Those Who Understand: Knowledge Growth in Teaching”, Shulman introduced the concept of Pedagogical Content Knowledge (PCK) (See Figure 1). That is where the two systems, content and pedagogy intersect—knowing what pedagogical strategy to use, and why its use would best support students understanding the content. Figure 1: Pedagogical Content Knowledge 11 11 Amidst the technological boom, in 2006, Mishra and Koehler, added a third foundational paradigm, technology, to Schulman’s (1986) model and called it Technological Pedagogical Content Knowledge (TPCK) [Later abbreviated to TPACK for pronunciation (Thompson, 2008)] (See Figure 2). Mishra and Koehler (2006) stated, “Teachers need to have “pedagogical techniques that use technologies in constructive ways to teach content” (p. 1029). When the three systems are woven together, they produce seven components of teacher knowledge: content knowledge (CK), pedagogical knowledge (PK), technology knowledge (TK), pedagogical content knowledge (PCK), technological pedagogical knowledge (TPK), technological content knowledge (TCK), and technological pedagogical content knowledge (TPACK). Chapter two will cover each construct in further detail. Figure 2: Technological Pedagogical Content Knowledge (TPACK) 12 12 Evolved from Schulman’s PCK, TPACK describes the relationship among the three foundational components of education: pedagogical knowledge, content knowledge and technological knowledge. For the purposes of this study, the researcher is interested in how teachers use TPACK to implement ambitious teaching strategies. Kazemi, Franke and Lampert (2009) described, “Ambitious teaching requires that teachers teach in response to what students do as they engage in problem solving performances, all while holding students accountable to learning goals that include procedural fluency, strategic competence, adaptive reasoning, and productive dispositions.” (p. 11). The use of ambitious teaching practices requires that teachers know and understand the profession in such a way that they design critically relevant lessons that maximize student development. Importance of the Study It is important that students graduating high school leave prepared to be successful in college and/or in a career that helps them compete in the global workforce. Currently, there is a large gap in academic achievement, and there is also a gap between historically marginalized students’ experience with technology in the classroom as compared to their privileged counterparts. Existing research already demonstrates a history of accessibility issues (digital divide). There is very limited research in quantifying technology implementation in the classroom. Most research in technology implementation has primarily focused on preservice teachers use of technology. Preservice teachers and in-service teachers in ABC Valley Unified School District who received their credential after the introduction of TPACK only represent 51.1% of in-service teachers, therefore, unless the other 48.1% of teachers received professional development then nearly half of the workforce has not been formally trained how to pedagogically implement technology in the classroom. Additionally, this study will examine the 13 13 relationship between teacher knowledge of TPACK and the appropriateness of technology implementation. This will add understanding and explore the role in-service teachers’ knowledge plays in the implementation of technology. Moreover, because there is limited accountability to applying ISTE 2017 standards this study will describe teachers’ current knowledge and application of the technology standards. Filling this gap in the literature would be beneficial for several reasons. First, expose areas of weakness in both a general quality of technology implementation as well as expose equity issues in our educational system that need to be addressed to best serve the needs of all students, particularly the equity gap for our historically marginalized students. For instance, if lack of knowledge is found this study may inform administrators (both school and district) that it is necessary to have explicit training to equip teachers with tools to serve the students. Therefore, the purpose of this study is to better understand how teachers apply ISTE standards and how factors such as knowledge and group differences affect how teacher’s pedagogical choices leverage technology to maximize student development. This study will inform all stakeholders from policymakers to students. Outcomes of this study will better support policymakers in further developing accountability systems of technology use in the classrooms as well as potentially the distribution of funding. This study will further support preservice teacher’s need to focus on technology from the lens of pedagogy rather than a tool in and of itself. Furthermore, this study will impact the district's focus on the in-service teachers’ need for explicit training. This study will help administrators help hold teachers accountable to and support teachers with ambitious teaching practices. Finally, this study will help teachers become more aware of their pedagogical choices and intentionality in planning lessons with technology to better prepare students to develop skills to be successful in the 21st century workforce. 14 14 Key Terms and Definitions In education there are a myriad of acronyms whose often make it difficult to understand concepts. The researcher included a list of major acronyms with their definitions that will be used throughout the study in order to reference as needed. a. ITEA - International Technology Education Association b. ESEA - Elementary and Secondary Education Act c. NCLB - No Child Left Behind (2001) d. EETT - Enhancing Education Through Technology e. ISTE - International Society for Technology Education f. NETS-S - National Educational Technology Standards for Students g. NETS-T - National Education Technology Standards for Teachers h. SETDA - State Educational Technology Directors Association i. P21 - Partnership for 21st Century Skills j. NAGB - National Assessment Governing Board k. NAEP - National Assessment of Educational Progress l. NAE - National Academy of Engineering m. NRC - National Research Council n. CCSS - Common Core State Standards o. NGSS - Next Generation Science Standards p. TPCK (TPACK) - Technological Pedagogical Content Knowledge q. PCK - Pedagogical Content Knowledge r. TPK - Technological Pedagogical Knowledge s. TCK - Technological Content Knowledge 15 15 t. PK - Pedagogical Knowledge u. TK - Technological Knowledge v. CK - Content Knowledge 16 16 Chapter Two: Literature Review This chapter provides a comprehensive review of the literature related to how technology is implemented in the classroom. This section will begin with a detailed background of TPACK and the 2017 ISTE Standards for educators followed by a literature review of the relationship between demographic groups’ technology knowledge and implementation. Lastly, specific research questions are stated with their respective hypotheses for this study. TPACK and 2017 ISTE Standards for Educators Technological Pedagogical Content Knowledge (TPACK) As previously introduced, TPACK evolved from Shulman’s (1986) theoretical framework Pedagogical Knowledge (PCK). The major difference being the addition of Technology to the existing Pedagogy and Content paradigms. Mishra and Koehler (2006) recognized the proliferation of technology and its need in educational practices. Adding a third dimension to the model inherently added additional intersections for a total of seven components in TPACK—Content Knowledge (CK), Pedagogy Knowledge (PK), Technology Knowledge (TK), Pedagogical Content Knowledge (PCK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK), and the intersection of all three TPACK (See Figure 2 and Table 1 for definitions of the components). 17 17 Table 1: Definitions of TPACK by Subscale Component Abbrev. Definition Content Knowledge CK Subject Matter Competency Pedagogy Knowledge PK Teaching practices/Core practices Technology Knowledge TK Use of hardwares and softwares Pedagogical Content Knowledge PCK How to integrate teaching practices to foster content knowledge development Technological Pedagogical Knowledge TPK How to use technology in an educational context Technological Content Knowledge TCK Technology to access content/subject matter Technological Pedagogical Content Knowledge TPCK/ TPACK Culmination, to use technology in the educational context in order to foster content development Content Knowledge (CK) Content knowledge (CK) is known as the subject matter competency. These are the central concepts and ideas that are organized within particular frameworks or fields of study. For example, information related to Mathematics, Physics, English, etc. (Mishra & Koehler, 2006; Shulman, 1986). Thus, content knowledge is the information and skills of what is studied. In order to effectively share in the creation of knowledge, Shulman (1986) described the depth of content knowledge that a teacher needs to “not only understand that something is so; the teacher must further understand why it is so, on what grounds its warrant can be asserted, and under what circumstances our belief in its justification can be weakened and even denied” (p. 9). In California, created by the Commission on Teacher Credentialing (CTC), the California Subject Examinations for Teachers (CSET) is currently used to measure that an individual has sufficient content knowledge to hold a credential as a teacher. 18 18 Pedagogical Knowledge (PK) Pedagogical Knowledge (PK) is the knowledge of learning theories, instructional approaches and methods of assessment independent of content. For example, motivation, self- regulation, grouping strategies, types of formative and summative assessments, etc. Mishra and Koehler (2006) stated, “A teacher with deep pedagogical knowledge understands how students construct knowledge, acquire skills, and develop habits of mind and positive dispositions toward learning” (p. 1027). These pedagogical strategies, sometimes called high leverage practices or core practices lead to ambitious teaching (Grossman, et al., 2009; Lampert, et al., 2013). Pedagogical knowledge is important in order to create an engaging and culturally responsive learning environment. In pre-service, this knowledge is measured in classes, and through the edTPA or PACT and is measured in the K-12 classroom through evaluations via the California Standards for the Teaching Profession. Technology Knowledge (TK) Technology knowledge (TK) is the knowledge of how to use tools (books, chalk, blackboard, etc.) and also various hardware and software (televisions, computers, search engines, spreadsheets, documents, applications, etc). These are the skills to operate varying accessories. According to Mishra and Koehler (2006), “The ability to learn and adapt to new technologies (irrespective of what the specific technologies are) will still be important” (p. 1028). In the digital age, technology is rapidly evolving and changing, and it is important that teachers are able to keep up with these changes in order to best prepare students for competition in the 21st century. Teachers are not currently required to demonstrate technology competency in order to hold a teaching credential. 19 19 Pedagogical Content Knowledge (PCK) Pedagogical content knowledge (PCK) is the intersection of content knowledge and pedagogical knowledge––the knowledge or use of specific strategies in order to help students access content. For example, knowing when to use collaborative groups, when to check for understanding, knowing what tools will inform the teacher of the students’ needs, etc. Schulman (1986) stated, “Since there are no single most powerful forms of representation, the teacher must have at hand a veritable armamentarium of alternative forms of representation, some of which derive from research whereas others originate in the wisdom of practice” (p. 9). These strategies are organized in Tyler’s (1949) four questions to help plan curriculum: 1) What educational purpose should be attained?; 2) What types of learning experiences would accomplish these purposes?; 3) How should the learning experiences be organized?; and 4) How will the learning experiences/purposes be evaluated? Philosophical lenses of teaching. Teacher’s philosophical lens guides their pedagogical decisions (Moss, 2007). There are many educational philosophical frameworks including, but not limited to perennialism, essentialism, progressivism, and reconstructionism. Perennialists emphasize the classics, they believe that “all students are supposed to pursue the same curriculum regardless of individual differences” (Moss & Lee, 2010). Similarly, essentialism emphasizes the teacher as the knowledgeable one, thus students are passive recipients of knowledge. Essentialism is centered on providing students with tools to be successful in society and believes that standardized testing is a good benchmark for assessing students’ knowledge as well as for teacher accountability (Moss & Lee, 2010). In contrast, progressivism emphasizes creating understanding through active learning and experiences. In this philosophical stance the teacher is viewed as a facilitator, and not the only knowledgeable one, instead, the students’ have 20 20 their own funds of knowledge (Dewey, 1966; Gee, 1996 and Moll, et al., 1992;). Therefore, learning is centered in collaboration and solving authentic, real-world problems. Finally, reconstructionism is similar to progressivism in that it focuses on authentic real-world problems, however, reconstructionism focuses more specifically on using education as a way to achieve social reform and improve human conditions. It is possible for teachers or curriculum to have essences of the various philosophical lenses, however, typically there is one lens that is more dominant and is demonstrated in the intentionality or purpose of the activity. It is clear then, that as teachers anchor themselves in a philosophical approach their pedagogical choices will be influenced by their worldview. Thus, teachers’ philosophical lens interfaces with how teachers choose to engage students with the content. Students’ needs. Students have diverse needs. Pre-service programs typically help teachers identify four areas of students' needs: general students performing at grade level, gifted learners, English language learners, and students with special needs. Each student has a “Zone of Proximal Development”, a place where they can be challenged to grow and achieve with support (Vygotsky, 1978, p. 86). Each of these students’ needs need to be taken into account when choosing pedagogical strategies to best access the content. Pedagogical content knowledge is assessed in pre-service through assessments such as edTPA and PACT, and in-service teachers’ pedagogical content knowledge is evaluated through evaluations based on the California Standards for the Teaching Profession and indirectly through value added models and students’ test scores on standards-based assessments. Technological Pedagogical Knowledge (TPK) Technological pedagogical knowledge (TPK) is the ability to use technology in an educational context. For example, knowing the different ways that Google Slides can be used for 21 21 presentations, group collaborations, or creating a “choose your own adventure” with hyperlinks, etc. Each of these requires different technological knowledge as it applies to different pedagogical needs. According to Mishra and Koehler (2006), “Teachers will have to do more than simply learn to use currently available tools; they also will have to learn new techniques and skills as current technologies become obsolete” (p. 1023). Teachers need to know the range of available tools and choose the best one for these given goals of the activity. Therefore, teachers need to not only have the technical knowledge, but also have a conceptual understanding of technology from a pedagogical perspective in order to keep up with the rapidly changing software options and capabilities. Teachers are not officially evaluated on their ability to integrate technology in the classroom, they are however, unofficially assessed through the use of computer-based standardized tests. Students’ inability to navigate the technology will inhibit their scores (Thurlow, et al.,, 2010). Technological Content Knowledge (TCK) Technological content knowledge (TCK) is the ability to use technology to access content. For example, knowing about various YouTube Videos, PhET Simulations, and other technologies that can be used to help students access the content. Mishra and Koehler (2006) argued, “Teachers need to know not just the subject matter they teach but also the manner in which the subject matter can be changed by the application of technology” (p. 1028). As technology evolves there are new ways of accessing content. Currently, teachers are not assessed on their knowledge of ways technology can be used to engage with the content. Technological Pedagogical Content Knowledge (TPACK). Technological pedagogical content knowledge (TPACK) is the intersection of technology with pedagogy and content knowledge. This construct requires teachers to know which technology 22 22 would be best utilized to access the content for their students. Mishra and Koehler (2006) argued, “there is no single technological solution that applies for every teacher, every course or every view of teaching” (p. 1029). This requires the teacher to be a professional and have a strong knowledge base in the three systems: technology, pedagogy, and content. Mishra and Koehler (2006) continue with, “The incorporation of a new technology or new medium for teaching suddenly forces us to confront basic educational issues because this new technology or medium reconstructs the dynamic equilibrium among all three elements” (p. 1030). It is imperative that teachers are lifelong learners and are able to adapt their TPACK to serve the best interests of the students. 2017 ISTE Standards for Educators The 2017 ISTE standards include seven categories: learner, leader, citizen, collaborator, designer, facilitator, and analyst. These seven categories are separated into two groups: empowered professional and learning catalyst (ISTE, 2017). As part of the first group, empowered professionals, which includes learner, leader, and citizen, which aims to evaluate teaching practices outside of the classroom (ISTE, 2017). The second group, learning catalyst, includes collaborator, designer, facilitator, and analyst, focuses on practicing in a digital age (ISTE, 2017). Together, these standards provide insight into how educators use technology. Empowered Professional Learner The 2017 ISTE Standards for Educators define learners as, “educators [who] continually improve their practice by learning from and with others and exploring proven and promising practices that leverage technology to improve student learning” (ISTE, 2017, Standard 2.1). Learners are committed to staying current with educational practices, especially in respect with 23 23 educational technology and best ways to leverage technology to increase student achievement. Learning practices include but are not limited to participation in professional learning networks PLNs), attending professional development, seeking out and taking continuing education courses, participating in professional learning communities (PLCs), and partaking in critical reflection. Embracing these practices as a Learner will help educators remain current in best practices and will be better prepared to effectively leverage technology to increase student achievement and prepare students to be 21st century learners. Leader The 2017 ISTE Standards for Educators define leaders as, “educators [who] seek out opportunities for leadership to support student empowerment and success and to improve teaching and learning” (ISTE, 2017, standard 2.2). Being a Leader entails embracing the constant change in technology advances==being willing to try new things with the potential of failing and advocating for what is needed to effectively implement technology as tools to empower students’ learning. Embodying leadership humanizes the process of learning and implementing new technologies and serves to lower affective filters and encourage others to implement new strategies. This helps advance technological pedagogical content practices and can help to shrink the gap between socioeconomic classes (Smith, 2017). Citizen The 2017 ISTE Standards for Educators define citizens as, “educators [who] inspire students to positively contribute to and responsibly participate in the digital world” (ISTE, 2017). Educators that embrace practices of a Citizen contribute to society by evaluating sources of information, actively work to protect the privacy of themselves and others, engage in ethical and 24 24 legal habits. Acting as a Citizen helps role model for others appropriate use of technology to minimize risk and foster positive relationships. Learning Catalyst Collaborator The 2017 ISTE Standards for Educators define collaborators as, “educators [who] dedicate time to collaborate with both colleagues and students to improve practice, discover and share resources and ideas, and solve problems” (ISTE, 2017). Educators as Collaborators work with stakeholders (students, parents, colleagues, administrators, etc.) as co-creators in student learning. They encourage active participation in the learning process. For example, educators as Collaborators may use software like G Suite, Padlet, Zoom, and social media to increase interaction between stakeholders. In doing this, Collaborators are able to include more voices and increase buy-in, which results in advancement of knowledge and skills for all. Designer The 2017 ISTE Standards for Educators define designers as, “educators [who] design authentic, learner driven activities and environments that recognize and accommodate learner variability” (ISTE, 2017). Designers utilize TPACK knowledge and Tyler’s four questions to plan and design curricula to effectively meet the needs of their students. Designing is the foundation of education and carries out the purpose of education—this is the plan for what students will do to learn the intended knowledge/skills. Facilitator The 2017 ISTE Standards for Educators define facilitators as, “educators [who] facilitate learning with technology to support student achievement of the ISTE Standards for Students” (ISTE, 2017). Facilitators follow through and carry-out the lessons as designed. Facilitators must 25 25 leverage skills to scaffold student learning in order to attain achievement. Practices facilitators engage in are: modeling, checking for understanding, troubleshooting, grouping strategies, etc. Executing these systems will aid students’ development of knowledge and skills required to be successful in the 21st century. Analyst The 2017 ISTE Standards for Educators define analysts as, “educators [who] understand and use data to drive their instruction and support students in achieving their learning goals” (ISTE, 2017). Analysts use data to monitor academic progress and provide insight as to where students need more practice or have mastered the content to move forward—using data to drive instruction. Formative and summative assessments are used to provide feedback to both the students and the teachers about student progress and the data can be analyzed to create goals that in turn help an educator design and facilitate lessons. The analysis of data ensures that educators are accountable to students achieving standards that have been determined to underpin the knowledge and skills necessary to be successful in the 21st century workplace. Demographic Groups of Educators in Relation to Teaching and Technology This study will examine educators from eight perspectives—gender, race, teachers’ education, grade taught, rigor, subject taught, years of experience, and socioeconomic status of students served in relation to knowledge (TPACK) and technology implementation (TPTI) Gender According to EdSource (2020), in the 2018-2019 school year, there were approximately 307,000 credentialed teachers teaching in the state of California. Females made up a majority of the teacher population at 73% female and 27% male. Previous research has repeatedly shown that male teachers have higher TPACK knowledge than female teachers (Erdogan & Sahin, 26 26 2010; Jang & Tsai, 2012; Jang & Tsai, 2013; Jordan, 2013; Koh, et al., 2010; Lin, et al., 2013; and Teo, 2008). Internationally, gender differences have been studied in Taiwan and Greece and both found that male teachers had higher TPACK than female teachers (Jang & Tsai, 2012; Roussinos & Jimoyiannis, 2019). However, a recent study found no differences between genders (Castéra, Marre, et al., 2020). A gender gap in technology has been long standing and has implications of underrepresentation of females in mathematics intensive and computer related STEM careers (AAUW, 1992; AAUW, 1998; Wang & Degol, 2017). Studies examining computer-related behavior have shown that males were more positive attitudes towards technology, higher abilities and used computers more often than females (Kay, 1994). Later, Kay (2006) studied preservice teachers involved in an eight-month laptop program where there was no difference observed in actual use of technology. There is very limited research on gender and alignment of implementation of technology in the classroom according to the 2017 ISTE Standards for Educators. One study attempted to qualify the influence of teachers’ TPACK on student performance by examining the relationship between TPACK and the Value-Added Model (Farrell & Hamed, 2017). Farrell and Hamed (2017) found that female teachers had CK and PCK than male teachers, but did not find a significant difference in TPACK. Burge (2001) was the only researcher that mentioned gender and ISTE standards. She used the previous iteration of the ISTE standards, specific to the students called NETS, in a program that examined fifth and sixth grade students engaged in the creation of a PowerPoint presentation. She found that females and males engaged differently: females preferred productivity software, cooperative groupings, and constructivist methods, whereas males preferred to actively manipulate the hardware and lost interest when they could 27 27 not do the actions. While this does not directly dictate teacher gender differences, it serves to demonstrate 1) teachers were once students themselves and may have followed similar patterns that affect adult behavior and 2) gender differences are important to keep in mind as teachers design- lessons so as to meet the needs of all students. Current and past research show conflicting findings as to gender differences in TPACK and technology usage. This study will address teacher differences by gender in order to further the discussion as well as uniquely fill a gap by exploring the relationship between gender and the alignment of 2017 ISTE Standards for Educators. Race Research on teacher’s race has largely been focused on teacher’s practices in other countries. The researcher was unable to find any studies conducted in the United States that explored the relationship between teachers’ races and their knowledge of technology and implementation practices. This study will be the first to contribute knowledge regarding the role teachers’ race may play in TPACK and appropriate technology use in the classroom. Education Level As part of the 2001 No Child Left Behind Act schools must be composed of ‘highly qualified’ teachers. A highly qualified teacher is a teacher who holds a Bachelor of Arts or science degree, a teaching license, and has demonstrated competency in the subject matter in which he or she teaches (Darling-Hammond, 2004). Schools are rated and evaluated on how well they adhere to staffing highly qualified teachers to match the needs of the students. What is interesting to note about the requirements to become a teacher is that they largely cover just CK and PK of the TPACK model. CK is measured by a passing score on the CSET subject matter competency exam, and PK is measured by the completion of a preservice credential program. TK 28 28 is not explicitly measured or accounted for. Research has highlighted the inconsistencies how TPACK is taught in preservice programs—sometimes it is a stand-alone course and other programs embed TPACK throughout, and the use of the 2017 ISTE Standards for Educators is also inconsistent (Mouza, Yet al., 2017). There is very little research that examines teachers’ education level and TPACK. It would stand to make sense that the more education a teacher has the more opportunities he or she may have had to learn and practice integrating technology. One study, Liang, Chai, Koh, Yang and Tsai (2013) found that university teachers had significantly higher levels of TPACK than teachers who did not have a university degree. Farrell and Hamed (2017) also found a positive relationship between more education a teacher attained and higher levels of TPACK. Specifically, they found that there is a 6% increase in TPACK from teachers who hold a Bachelor’s degree to those who hold a Master’s degree, and a 17% increase between teachers who hold a Bachelor’s degree and those who hold a Doctorate. Cheng and Xie (2018) explain that teachers with more education may have more opportunities to learn and use technology over their educational programs that inherently increase their TPACK. Teachers’ experience with TPACK can be influenced first by their pre-service programs and then secondarily through professional development as in-service teachers. The Office of Education Technology (2017) provided the National Education Technology Plan (NETP) with their recommendations for pre-service programs: “First, educators should have pre- and in- service professional learning opportunities powered with technology to improve their digital literacy and to create effective learning activities that improve learning, teaching, assessment, and instructional practices. Second, educators should have access to the latest information on research-supported practices and understand how to leverage emerging online technologies to 29 29 support learning in online and blended environments. Lastly, a common set of technology competency expectations should be established for teacher-candidates in teacher preparation programs to design and implement technology-enabled learning environments effectively”. Further studies have found that when in-service teachers perceive support in implementation from their institutions, they have higher levels of TPACK (Nelson, et al., 2019). This study will further contribute to the discussion of the role teachers’ education level plays in TPACK and be the first study to explore teachers’ level of education and technology implementation with respect to the 2017 ISTE Standards for Educators. Grade Taught Credential programs provide the same training to multiple subject candidates—teachers who teach elementary school grades K-6 and very similar training with the exception of content specific courses for secondary teachers—grades 7-12. In review of the literature there is no literature to the researcher’s knowledge that compares the TPACK and use of technology between grades and between primary and secondary school. Many studies examine technology implementation in one or two grade levels only. Of the very few, Koh et. al (2014) found that because secondary teachers only had to plan and prepare for a single subject that they had more time and energy to focus on integrating technology than did primary teachers who had to plan and prepare for multiple subjects. Further, Farrell and Hamed (2017) did not find a relationship between grade level and TPACK. This study will significantly add to the literature through the exploration of bands of grade levels’ TPACK and use: lower elementary (K-3), upper elementary (4-6), junior high (7-8) and high school (9-12). 30 30 Rigor Rigor in this study is used to describe type of challenge or differentiation of classes— General Education, Special Education and Advanced Placement/Honors/GATE, respectively. According to Michaels and McDermott (2003), “the appropriate application of AT (assistive technology) may be one of the greatest equalizing forces in the education and meaningful inclusion of students with disabilities both in terms of promoting access to the general curriculum and in facilitating the ability of students to demonstrate mastery of that knowledge”. Much of the literature regarding TPACK and use of technology in types of classes is qualitative and/or based on case studies of exemplary integration of technology in special education classrooms (Courduff, et al., 2017; and Edyburd, 2000). Anderson et. al (2017) examined the intentionality and planning process of teachers as they selected technology that was aligned with the standards and would meet the instructional needs of the students with special needs. Kimm, Kim, Baek, and Chen (2020) found that pre-service teachers enrolled in Special Education courses scored higher in a self-report of ISTE readiness than their general education counterparts. This study will contribute to the literature by exploring the TPACK and implementation practices of general education teachers, gifted and honors teachers, and teachers who teach Special Education. This is the first study to quantify teachers of various types of classes uses of technology according to the 2017 ISTE Standards for Educators. Subject Elementary teachers are credentialed to teach multiple subjects, and typically teach English/Language Arts, mathematics, science, history, and physical education. Secondary teachers are credentialed to teach one subject (unless they have passed additional CSET exams to 31 31 teach additional subjects), but typically teach courses within one core subject. There is very limited research on comparing teachers’ TPACK within all subjects. Of the studies that have been conducted they have been limited to within individual subjects (Farrell & Hamed, 2017; Smith & Mader, 2017) or between subjects like Jang and Tsai’s (2012) study that found that Taiwanese elementary science teachers had significantly higher TK and TPCK than Taiwanese elementary mathematics teachers. Contrary to Jang and Tsai’s (2012) findings, Farrell and Hamed (2017) found according to the value-added model that mathematics teachers had the lowest TPACK, but did not mention which subjects these teachers were compared against. In terms of subjects and ISTE, Smith and Mader (2017) noted that once it is recognized that the NGSS and the ISTE standards are complementary then technology can be more appropriately utilized in the classroom to transform student learning activities. Nelson, Volthofer, and Cheng (2019) examined factors that influence technology integration practices and found that English teachers were less likely to adopt the 2017 ISTE Standards for Educators. However, they did find in their study that, “teacher educators with stronger perceptions of institutional support for themselves and their students reported higher TK and TPACK, leading to a positive effect on ISTE standard alignment”. Meaning, teachers who felt supported by their administrators reported higher levels of technology knowledge and TPACK knowledge that increased appropriate use of technology. No other information was found regarding the other subjects and ISTE. The current study will contribute to literature through a comprehensive comparison of TPACK and appropriate use of technology among single subjects: English/Language Arts, foreign languages, history, mathematics, physical education, science and other electives. This 32 32 will be the first study to explore technology use as it relates to the 2017 ISTE Standards for Educators. Years of Experience According to EdSource (2020), in the 2018-2019 school year, the average age of teachers in California was 45 years old. The majority of teachers are between 30 and 39 (15%), 40 and 49 (24%) and 50 and 59 (21%). The average length of service in a district is 14 years (EdSource, 2020). Previous research has demonstrated a negative association between teachers' years of experience with technology aspects of TPACK and a positive association between years of experience and teaching practices (Cheng & Xie, 2018; Koh, et al., 2014; Liu, et al., 2015). This makes sense as teachers have more experience they will have higher levels of PK, CK, and PCK. However, teachers who have more experience may have received their credential prior to the technological boom or inception of TPACK have lower levels of TK, TCK, TPK and TPACK. As previously mentioned, roughly 30% of teachers earned their credential before 2003 when Google’s Search Engine was invented, a little less than 50% of teachers earned their credential before 2007 when smartphones were invented—meaning almost half of teachers began teaching before the third paradigm, technology, was introduced to the pedagogical bases of content and pedagogy to form TPACK. The research was unable to find any information regarding teachers’ years of experience and the 2017 ISTE Standards. This study will further contribute to the discussion of the role teachers’ experience plays in TPACK and be the first study to explore teachers’ years of experience and technology implementation with respect to the 2017 ISTE Standards for Educators. 33 33 Socioeconomic Status The lack of teacher capital in technology implementation and inequitable usages of technology across socioeconomic statuses in the classroom contributes to the perpetuation of historically marginalized populations. This oppression widens the already present gap between privileged and unprivileged students which then affects minority students’ ability to compete for better job positions due to lack of skills, and practice. Because access to hardware is similar, two major contributors to technology implementation are students’ and teachers’ cultural and social capital. Cultural capital is related to an individual’s knowledge and skills and social capital is related to the resources and relationships an individual has that can be leveraged to achieve social mobility (Becker, 1964; Bourdieu, 1986). Students in higher income areas have more social and cultural capital than students in low socioeconomic status areas (Lareau, 2003). In a 20-year longitudinal study, Lareau (2003) found that parents across socioeconomic statuses do not love their children less or more, and all want their children to succeed. However, the difference she found between students from low-income families and high income families is the parenting style–parents from high socioeconomic status backgrounds engage in practices that foster the growth of their children’s social and cultural capital. For example, parents from high socioeconomic status backgrounds value: enrolling students in structured activities like sports, music lessons, etc; having conversations with their children and asking follow up questions; and encouraging their children to question authority when necessary. In comparison, parents of students from low socioeconomic status backgrounds have been observed to value: unstructured free play with family and neighborhood children, clearly defining the role of authority between parents and children and raising their children to not question authority figures (Lareau, 2003). The contrast 34 34 in parenting practices result in students with varying levels of social and cultural capital, which is often demonstrated in the level of students’ skills and abilities in the classroom. While the school system does not have control over parenting practices, the system does have control over what is taught in the classrooms. First, not only is there inconsistency in teacher quality in low-income schools due to less experience, less education, and less skill (Peske & Haycock, 2006), The researcher was unable to find any studies that directly compared the TPACK of teachers across socioeconomic status. Furthermore, the researcher was unable to find any studies that examined the implementation of 2017 ISTE Standards for Educators. However, previous research has discussed the second digital divide where hardware devices were equitable, the use of technology in low-income areas relied on lower thinking skills like repetition activities whereas use of technology in more affluent areas utilized higher level thinking skills such as creation and analysis (DiMaggio, et al., 2004; Warschauer, et al., 2002; Wayne, et al., 2002). This study will contribute to the literature by providing a comparison of teachers TPACK across socioeconomic status holding external factors like district protocols and professional learning constant. Teachers in this study were all within the same district and as such conducted within the same parameters of district policies and practices. Moreover, this study will be the first study to compare technology implementation according to the 2017 ISTE Standards for Educators between Title I schools and non-Title I schools. Purpose of the Study and Research Questions The purpose of this study is to examine the demographic factors (gender, race, education level, grade taught, rigor, subject, years of experience and SES of the school) that may influence 35 35 knowledge and skills and then explore the relationship between knowledge and how teachers implement technology in their K-12 classrooms. Research Question 1: Are there demographic group differences among CVUSD teachers (by gender, race, education level, grade taught, rigor, subject, years of experience and SES of school) in TPACK? Hypothesis 1a: Males have higher TPACK. Hypothesis 1b: There are racial differences in TPACK. Hypothesis 1c: Teachers with Master’s Degrees have higher TPACK. Hypothesis 1d: Secondary grade levels have higher TPACK. Hypothesis 1e: Advanced/Honors/GATE have higher TPACK Hypothesis 1f: Science has higher TPACK. Hypothesis 1g: Teachers with more experience have higher PCK, and lower TPACK. Hypothesis 1h: Teachers in non-Title 1 schools have higher TPACK. Research Question 2: Are there demographic group differences among CVUSD teachers (by gender, race, education level, grade taught, rigor, subject, years of experience and SES of school) in teacher familiarity of 2017 ISTE Standards for Educators? Hypothesis 2a: Males have more knowledge of 2017 ISTE Standards for Educators. Hypothesis 2b: There are racial differences in knowledge of 2017 ISTE Standards for Educators. Hypothesis 2c: Teachers with Master’s Degrees have more knowledge of 2017 ISTE Standards for Educators. 36 36 Hypothesis 2d: Secondary grade levels have more knowledge of 2017 ISTE Standards for Educators. Hypothesis 2e: Advanced/Honors/GATE have more knowledge of 2017 ISTE Standards for Educators. Hypothesis 2f: Science has more knowledge of 2017 ISTE Standards for Educators. Hypothesis 2g: Teachers with less experience have more knowledge of 2017 ISTE Standards for Educators. Hypothesis 2h: Teachers in non-Title 1 schools have more knowledge of 2017 ISTE Standards for Educators. Research Question 3: Are there demographic group differences among CVUSD teachers (by gender, race, education level, grade taught, rigor, subject, years of experience and SES of school) in teacher technology implementation? Hypothesis 3a: Males implement technology more appropriately to the 2017 ISTE Standards for Educators. Hypothesis 3b: There are racial differences in implementation of technology according to the 2017 ISTE Standards for Educators. Hypothesis 3c: Teachers with Master’s Degrees implement technology more appropriately to the 2017 ISTE Standards for Educators. Hypothesis 3d: Secondary grade levels implement technology more appropriately to the 2017 ISTE Standards for Educators. Hypothesis 3e: Advanced/Honors/GATE implement technology more appropriately to the 2017 ISTE Standards for Educators. 37 37 Hypothesis 3f: Science implements technology more appropriately to the 2017 ISTE Standards for Educators. Hypothesis 3g: Teachers with less experience implement technology more appropriately to the 2017 ISTE Standards for Educators. Hypothesis 3h: Teachers in non-Title 1 schools implement technology more appropriately to the 2017 ISTE Standards for Educators. Research Question 4: Which factors (TPACK and years of experience) contribute to teachers’ implementation of technology in the classroom? Research Question 5: To what extent has COVID-19 influenced technology implementation in the K-12 classrooms? 38 38 Chapter Three: Methodology This quantitative correlational study explores the relationship between TPACK and how teachers implement technology according to the Teacher Preparation Technology Inventory (TPTI). This chapter contains information on the study participants, instruments used, and procedures for data collection and analysis. Participants The entire population of teachers (1,109) in ABC Valley Unified School District had the opportunity to participate in this study. ABC Valley Unified School District is one of the largest districts in San Bernardino County. ABC Valley Unified serves approximately 28,000 students annually (EdData, 2020). The district is composed of 36 schools: 20 kindergarten through sixth grade schools, two kindergarten through eighth grade schools, five junior high schools, four high schools, and five specialty schools. The five specialty schools (Alternative Education or Virtual Schools, Continuation School, Adult Education, and the Learning Academy) will be excluded from the study because they have many differing environmental considerations that may skew the data. ABC Valley Unified encompasses three cities: City A, City B, and parts of City C (only three of 36 schools). Sixteen of the 31 (51.6%) schools in this study qualify as Title I schools. Therefore, ABC Valley Unified is an ideal school district to study because the large school district (governed by the same rules and regulations) serves vastly different populations, City B and City C largely serves low-income students, while City A largely serves middle and upper- middle class students (see Table 2 for a comparison of the three cities).. 39 39 Table 2: Quick Facts—Comparison of Cities A, B and C City C City B City A Median Income $61,602 $79,477 $104,590 Demographics Hispanic (70.8%) Hispanic (52.0%) Asian (34.0%) Caucasian (15.6%) Caucasian (24.5%) Caucasian (24.6%) Asian (6.3%) Asian (13.1%) Hispanic (24.1%) Education: High School+ 72% 77.9% 93.5% Education: Bachelor’s+ 16% 21.1% 46.0% Number of Schools 3 (9.7%) 14 (45.2%) 14 (45.2%) Title I Schools 3 (100% in Ontario); (18.8% of Title I in CVUSD) 11 (78.6% in ABC); (68.8% of Title I in CVUSD) 2 (14.3% in ABC); (12.5% of Title I in CVUSD) A total of 1,109 surveys were sent to all teachers in ABC Valley Unified School District. A total of 506 participants responded. Of the 506 responses, 81 could not be included in the study as they indicated they were not teachers (n = 80) or taught at the specialty schools (n = 1) that were excluded from this study. Respondents, n = 425, represented all 31 schools in the district. Socioeconomic status was distributed evenly between Title I (n = 213, 51.1%) and non- Title I (n = 212, 49.9%). There were 342 females (80.9%) and 81 males (19.1%). The mean years of experience of the participants was 15.8 years with a standard deviation of 9.58 years. The minimum years of experience was 1 year, and the maximum was 45 years. 82 teachers (19.3%) were in their first five years of teaching, 127 teachers (29.9%) were in years 6-15, 73 teachers (17.2%) were in 40 40 years 16-20, and 143 teachers (33.6%) have been a teacher 21 years or more. The categorical groups were created intentionally. Grouping teachers in years 1-5 is important because it aligns with when the time frame 2017 ISTE Standards for Educators was released as well as the ideology that teachers who remain in the profession longer than five years typically remain as teachers, whereas there is a high turnover rate for teachers in their first five years of teaching (Ingersoll, 2001; Smith & Ingersoll, 2004). The second break of 6-15 years of experience, represents the time period between 2017 ISTE and 2006 the introduction of TPACK and the boom of social media as well as the proliferation of smartphones. The third break of 16-20 years of experience coincides with the inception of Google’s search engine. The racial demographics represented in this study are almost identical to the most current racial demographics available on California Department of Education (2018-2019) with Asian/Pacific Islander represented 7.3% (n = 31), Hispanic represented 21.9% (n = 92), White/Caucasian represented 58.6% (n = 249), and other race(s) represented 12.2% (n = 52). In terms of level of education 32% (n = 136) held a Bachelor’s degree and 68% (n = 289) held a Master’s degree. The majority of the participants (n = 322, 76.8%) taught general education classes, 12.6% (n = 53) taught advanced/honors or GATE classes, and 10.5% (n = 44) taught special education classes. Grades were grouped in Lower Elementary (K-3), Upper Elementary (4-6), Junior High (7-8) and High School. Elementary was divided into two categories because they are often treated very differently, for example K-2 does not take state standardized tests and are collectively called “primary” and have a different schedule than the upper elementary classes on the same campuses. Lower Elementary teachers comprised of 25.9% (n = 110) of the responses, Upper Elementary teachers comprised of 22.1% (n = 94) of the responses, Junior High teachers 41 41 comprised of 21.2% (n = 90) of the responses, and High school teachers comprised of 30.8% (n = 131) of the responses. Elementary teachers teach multiple subjects, while both Junior High and High School teachers teach a single subject. For this study, 25.3% (n = 56) of the respondents taught English/Language Arts, 5.0% (n = 11) of the respondents taught a foreign language, 14.9% (n = 33) of the respondents taught history/social science, 17.2% (n = 38) of the respondents taught mathematics, 5.4% (n = 12) of the respondents taught physical education, 19.0% (n = 42) of the respondents taught science, and 13.1% (n = 29) taught another elective (see Table 3 below for the demographic frequencies). Table 3: Demographic Frequencies of Teacher Participants N % Gender Male 81 19.1 Female 342 80.9 Race Asian/Pacific Islander 31 7.3 Hispanic 92 21.9 White/Caucasian 249 58.6 Other Race(s) 52 12.2 Education Level Bachelor’s Degree 136 32.0 Master’s Degree 289 68.0 Grade Taught Lower Elementary (K-3) 110 25.9 42 42 Upper Elementary (4-6) 94 22.1 Junior High (7-8) 90 21.2 High School (9-12) 131 30.8 Rigor General Education 322 76.8 Honors/Advanced 53 12.6 Special Education 44 10.5 Subject English/Language Arts 56 25.3 Forgein Language 11 5.0 History/Social Science 33 14.9 Mathematics 38 17.2 Physical Education 12 5.4 Science 42 19.0 Other Elective 29 13.1 Years of Experience 1-5 Years 82 19.3 6-15 Years 127 29.9 16-20 Years 73 17.2 21+ Years 143 33.6 Socioeconomic Status of School Title I 213 50.1 Not Title I 212 49.9 43 43 Instruments A quantitative survey was used to collect data for this study. It consisted of the following five sections to represent knowledge for, in and of practice. These sections include 1) demographic information (Appendix B); 2) familiarity and use of 2017 ISTE Standards for Educators (Appendix C); 3) Technological Pedagogical Content Knowledge (TPACK) (Appendix D); 4) Teacher Preparation Technology Inventory (Appendix E); 5) Role of COVID- 19 in technology implementation (Appendix F). Prior to participating in the survey teachers were provided an informed consent form and were notified that all survey responses would remain confidential (Appendix A). Detailed information regarding the instruments used for this study are described below. Demographics Demographics (Appendix B) were subdivided into eight nominal questions: school name, position, grade taught, subject taught, rigor of class, gender, race, level of education and number years taught. The school’s name was used in order to distinguish whether the teacher teaches at a Title I school or not. This helped the researcher to observe trends in relation to SES. The grade taught was coded into primary versus secondary to observe trends between single multiple- subject teachers and multiple single-subject teachers. Rigor of class demonstrated group differences between Special Education classes, general classes and advanced classes. This showed trends in how types of students were engaged with technology in the classroom. Number of years were collected as an ordinal variable and later grouped and coded as a categorical variable in order to explore differences in teachers by groups. These responses were collected in order to further desegregate the data. Familiarity and Use of 2017 ISTE Standards for Educators 44 44 Teachers were asked 2 questions regarding their familiarity and use of the 2017 ISTE Standards for Educators (Appendix C). teachers were asked about their knowledge of the ISTE 2017 standards and the extent to which they utilize the standards when planning lessons. Both questions were written by the researcher and self-reported by the participant. TPACK The TPACK subset of the survey was used to measure teachers’ self-assessment of their Technological Pedagogical Content Knowledge (TPACK) (Appendix F). According to Schmidt et. al (2014), “TPACK is a term used increasingly to describe what teachers need to know to effectively integrate technology into their teaching practices” (p. 123). TPACK explores the connection between technology, pedagogy, and content (Mishra & Koehler, 2006; Koehler & Mishra, 2008; Schmidt, et. al, 2014). There are a total of 29 items on the instrument with a total of seven subsets. The subsets are technological knowledge (TK), content knowledge (CK), pedagogical knowledge (PK), pedagogical content knowledge (PCK), technological content knowledge (TCK), technological pedagogical knowledge (TPK), and technological pedagogical content knowledge (TPACK). All questions are on a six-point Likert scale: strongly disagree, disagree, neither agree/disagree, agree and strongly agree. Sample questions include: “I know how to solve my own technical problems” and “I can teach lessons that appropriately combine (SUBJECT), technologies, and teaching approaches.” The TPACK instrument has both high reliability and high validity. All subsets of the TPACK had internal consistency reliability between .75 and .92. The TPACK instrument was evaluated specifically in the areas of content validity and construct validity. The questions were analyzed for content validity by three nationally known experts in TPACK. Construct validity was conducted on each knowledge domain subscale using “principal components factor analysis 45 45 with varimax rotation within each knowledge domain and Kaiser normalization” (Schmidt, et. al, 2014, p. 130). This TPACK survey is built off previous primarily used surveys and is more comprehensive survey that includes multiple content areas and approaches to professional development (Schmidt et. al, 2014). This instrument will gauge teachers’ knowledge of TPACK. Teacher Preparation Technology Inventory The next section of the survey, the Teacher Preparation Technology Inventory (TPTI), measured how teachers actually implemented technology (Riegel, 2018). The instrument has a total of 81 questions that span the seven topics of the 2017 ISTE standards: 1) learner; 2) leader; 3) citizen; 4) collaborator; 5) designer; 6) facilitator; 7) analyst. The survey questions are on a six-point Likert scale: never, rarely, sometimes, frequently, usually, always and N/A. Sample questions include “Use technology to design a variety of formative assessments that accommodate learner needs?” and “Use collaborative tools to expand students’ authentic, real- world learning experiences by engaging virtually with local experts?” and “Create experiences for learners to make positive, socially responsible contributions online?” The initial instrument intended to measure teacher preparation programs, however, without changing the intention of the questions the parts of the questions that ask pre-service practices will be modified to in- service practices. The Teacher Preparation Technology Inventory (TPTI) has high reliability and high validity. High level of reliability was measured with a Cronbach’s alpha score of .88-.96 on all seven subsets. TPTI has both high content validity as well as high face validity. According to Riegel (2018), “Three rounds of the Delphi technique provided evidence towards content validity through a judgmental review of items by experts” (p. 215). Furthermore, the TPTI has high face 46 46 validity as it is based on the 2017 ISTE Standards and intends to measure how those same standards are implemented by teachers. TPTI was chosen because it is the only known instrument to measure modelling and application of the 2017 ISTE Standards for Educators (Riegel, 2018). This instrument serves to demonstrate a way to quantify teacher’s technology practices in order to analyze the extent of differences within ABC Valley Unified District. The TPTI takes the TPACK instrument one step further, by describing what teachers actually do with their knowledge. The Role of COVID-19 in Technology Implementation As this is a unique time, and the most technology is implemented in the history of education as most students and teachers spent almost an entire year or more on Zoom/Google Meets/Microsoft Teams it was important to ask teachers how COVID-19 shaped their implementation of technology. Two questions were asked relating to COVID-19, one was quantitative and the other was a qualitative follow-up for the respondents to explain their answer to the previous question. The question was, “How has COVID-19 affected the way you choose to implement technology in your classroom?” Qualitative Question Finally, as there were many more questions to ask and not enough space and time to give them. The researcher asked a final open-ended question, “Please share any thoughts you have related to technology implementation in the classroom that may not have been captured by this survey” in order to extract any pressing information that the respondents wanted to share in response to the survey. 47 47 Procedure The process for getting the survey to all teachers in the district required multiple steps. First, the researcher contacted the cabinet, including the Superintendent, Assistant Superintendent and Director of Curriculum and Instruction permission to contact the teachers to participate in this study. Permission was granted and directives were given to the researcher to contact the administrators of each site to forward the survey. This required additional buy-in from the principals to forward to their teachers and then teachers to choose to participate. The researcher wrote a personal email to each administrator individually with the survey link to forward to their teachers. The researcher then followed up by visiting each of the 31 school sites in the district to introduce herself to the administrator and thank them for forwarding the survey to their teachers. The survey was created and administered on Qualtrics. The initial email to administrators was the week prior to spring break. During spring break, the researcher emailed the President of the teacher’s union and asked if she would post the survey on the teacher’s private Facebook group. After returning from spring break, the researcher sent a follow-up email to the administrators asking for the survey to be forwarded for a last time. For confidentiality purposes and to protect the identities of the respondents, only the researcher had access to the data and no personal identifying data was collected. At the completion of the survey, teachers had the choice to be entered into a raffle for one of 30 $25 Amazon gift cards in appreciation for their support. Most participants, n=276, provided an email address in order to be eligible for the gift card raffle. Participants were provided with an external link (for confidentiality purposes) to an online form, where they could provide their contact information which was stored separately from the responses to the survey. At the completion of the data collection time period, the information was loaded into a Microsoft 48 48 Excel spreadsheet and a random number generator was used to select the winners of the gift certificates. Gift cards were awarded via email, and the names of the winners were never revealed to the researcher. Once the Qualtrics survey was closed, the data was loaded into SPSS to be cleaned. The researcher went through each participant’s responses and if the participant failed to respond to more than one question in a subscale then the entire subscale was coded as missing. If the participant only missed one question in the subscale, then the researcher calculated the mean score for that subscale and replaced the missing value with the mean score. 49 49 Chapter Four: Results This chapter reviews the findings of the study including preliminary descriptive statistics and results of the five research questions. The purpose of this study was to first investigate group differences in knowledge of TPACK, knowledge of ISTE 2017, and technology implementation (TPTI) in order to identify gaps in knowledge and how it is or is not related to technology implementation in the classroom. Research Question One: Demographic Group Differences in TPACK The TPACK instrument was based on a five- point Likert scale—strongly disagree (1), disagree (2), neither agree nor disagree (3), agree (4) and strongly agree (5). Overall, on the TPACK, generally teachers scored lowest in the area of technology knowledge, 3.65, and the highest in content knowledge, 4.48, and pedagogy knowledge, 4.47. See the graph below (Figure 3) to see the distribution including a line that represents the standard of knowledge. Additionally see Table 4 for a summary of means. Research question one explored if there were differences in teacher knowledge (TPACK) by gender, race, teacher education, grade taught, rigor, subject, years of experience or socioeconomic status of the school. Eight one-way MANOVAs were conducted, one for each of the independent variables, with TPACK (seven subscales) due to insufficient and uneven cell sizes to conduct a combined 8-way analysis. Where there was significance in the MANOVA test, follow-up ANOVAs were conducted with post hocs and means. 50 50 Figure 3: Graphic Distribution of TPACK Means Note: 1-Strongly Disagree, 2-Disagree, 3-Neither Agree nor Disagree, 4-Agree, 5-Strongly Agree Table 4: Descriptive statistics of TPACK Subscale Mean SD N Technology Knowledge (TK) 3.65 0.79 425 Content Knowledge (CK) 4.48 0.46 414 Pedagogy Knowledge (PK) 4.47 0.46 413 Pedagogical Knowledge (PCK) 4.36 0.56 403 Technology Content Knowledge (TCK) 3.97 0.73 403 Technological Pedagogical Knowledge (TPK) 3.76 0.66 403 Technological Pedagogical Content Knowledge (TPACK) 3.76 0.69 395 Note: 1-Strongly Disagree, 2-Disagree, 3-Neither Agree nor Disagree, 4-Agree, 5-Strongly Agree 51 51 Gender Differences The MANOVA revealed an overall significant effect for gender (male and female) in the seven TPACK subscales [F(7, 384) = 3.32, p<.002, Wilks Lambda = 0.943]. However, subsequent univariate analysis of variance did not show significance between genders in TPACK. Race Differences The overall MANOVA model was significant for the main effect of race (Asian/Pacific Islander, Hispanic, White/Caucasian and Other) in the seven TPACK subscales, [F(21, 1103) = 1.95, p<.006, Wilks Lambda = 0.900]. Subsequent ANOVAS revealed significant racial group differences in six of the seven subscales (see table 5). For the subscale of Technology Knowledge (TK), Asian/Pacific Islanders scored higher than White/Caucasians. For the subscale of Content Knowledge (CK), Hispanics scored lower than both White/Caucasian & Other. For the subscale of Pedagogy Knowledge (PK), Hispanics scored significantly lower than White/Caucasian. For the subscale of Technological Content Knowledge (TCK), although the ANOVA was significant, subsequent post hocs did not yield specific group differences. For the subscale of Technological Pedagogical Knowledge (TPK), White/Caucasian scored less than Other races. Finally, for the subscale of Technological Pedagogical Content Knowledge (TPACK), although the ANOVA was significant, subsequent post hocs did not yield specific group differences. The means for the subscales by race are in Table 6. 52 52 Table 5: Univariate Analysis of Variance for Race in TPACK (seven subscales) Subscale df F p Racial Group Differences Technology Knowledge (TK) 3 4.106 .007 White/Caucasian < Asian/Pacific Islander Content Knowledge (CK) 3 5.993 .001 Hispanic < White/Caucasian, Hispanic < Other Pedagogy Knowledge (PK) 3 3.862 .010 Hispanic < White/Caucasian Pedagogical Knowledge (PCK) 3 1.844 .139 Technology Content Knowledge (TCK) 3 2.909 .034 Technological Pedagogical Knowledge (TPK) 3 4.613 .003 White/Caucasian < Other Technological Pedagogical Content Knowledge (TPACK) 3 3.301 .020 Table 6: Means and Standard Deviations of TPACK by Race Subscale Race Mean SD N Technology Knowledge (TK) Asian/Pacific Islander 4.01 0.71 31 Hispanic 3.66 0.74 93 White/Caucasian 3.58 0.80 249 Other Race 3.86 0.78 52 Content Knowledge (CK) Asian/Pacific Islander 4.38 0.45 31 Hispanic 4.30 0.51 88 White/Caucasian 4.53 0.46 243 53 53 Other Race 4.58 0.54 52 Pedagogy Knowledge (PK) Asian/Pacific Islander 4.38 0.44 31 Hispanic 4.33 0.47 88 White/Caucasian 4.52 0.43 243 Other Race 4.47 0.50 51 Technological Content Knowledge (TCK) Asian/Pacific Islander 4.21 0.68 29 Hispanic 3.98 0.62 83 White/Caucasian 3.90 0.75 239 Other Race 4.15 0.75 52 Technological Pedagogical Knowledge (TPK) Asian/Pacific Islander 4.01 0.63 29 Hispanic 3.73 0.57 83 White/Caucasian 3.69 0.70 239 Other Race 3.99 0.53 52 Technological Pedagogical Content Knowledge (TPACK) Asian/Pacific Islander 3.96 0.68 29 Hispanic 3.79 0.63 81 54 54 White/Caucasian 3.69 0.72 236 Other Race 3.97 0.58 49 Note. All scores were scaled scores. Teacher Education Differences The overall MANOVA was not found to be significant (approaching significance [F(7, 386) = 1.89, p<.071, Wilks Lambda 0.967]) for the independent variable of level of teacher education (bachelor’s degree and master’s degree) in the seven TPACK subscales. Grade Level Differences The overall MANOVA model was significant for the main effect of grade level (lower elementary (K-3), upper elementary (4-6), junior high and high school) in the seven TPACK subscales, [F(21, 1103) = 4.34, p<.001, Wilks Lambda = 0.796]. Subsequent ANOVAS revealed significant grade level group differences in four of the seven subscales (see table 7). For the subscale of Technology Knowledge (TK), lower elementary teachers scored lower than upper elementary and high school teachers. For the subscale of Content Knowledge (CK), primary teachers (both lower elementary and upper elementary) scored lower than secondary teachers (both junior high and high school). For the subscales of Pedagogy Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK), lower elementary scored lower than the other three categories—upper elementary, junior high and high school. The means for the subscales by grade level are in Table 8. 55 55 Table 7: Univariate Analysis of Variance for Grade Level in TPACK (seven subscales) Subscale df F p Grade Level Group Differences Technology Knowledge (TK) 3 4.79 .003 Lower Elementary < Upper Elementary Lower Elementary < High School Content Knowledge (CK) 3 7.76 .001 Lower Elementary < Junior High Lower Elementary < High School Upper Elementary < Junior High Upper Elementary < High School Pedagogy Knowledge (PK) 3 0.49 .688 Pedagogical Knowledge (PCK) 3 0.63 .596 Technology Content Knowledge (TCK) 3 2.33 .074 Technological Pedagogical Knowledge (TPK) 3 6.14 .001 Lower Elementary < Upper elementary Lower Elementary < Junior High Lower Elementary < High School Technological Pedagogical Content Knowledge (TPACK) 3 7.65 .001 Lower Elementary < Upper elementary Lower Elementary < Junior High Lower Elementary < High School Table 8: Means and Standard Deviations of TPACK by Grade Level Subscale Grade Level Mean SD N Technology Knowledge (TK) Lower Elementary (K-3) 3.45 0.85 110 Upper Elementary (4-6) 3.85 0.74 94 Junior High 3.64 0.75 90 High School 3.73 0.75 131 56 56 Content Knowledge (CK) Lower Elementary (K-3) 4.40 0.48 107 Upper Elementary (4-6) 4.32 0.51 92 Junior High 4.58 0.46 88 High School 4.58 0.45 127 Technological Pedagogical Knowledge (TPK) Lower Elementary (K-3) 3.53 0.70 104 Upper Elementary (4-6) 3.86 0.66 88 Junior High 3.79 0.68 86 High School 3.86 0.55 125 Technological Pedagogical Content Knowledge (TPACK) Lower Elementary (K-3) 3.49 0.78 100 Upper Elementary (4-6) 3.85 0.63 87 Junior High 3.80 0.70 83 High School 3.89 0.60 125 Note. All scores were scaled scores. Rigor Differences The overall MANOVA model was significant for the main effect of rigor (general education, advanced/honors and special education) in the seven TPACK subscales, [F(14, 760) = 2.46, p<.002, Wilks Lambda = 0.915]. Subsequent ANOVAS revealed significant grade level 57 57 group differences in two of the seven subscales (see table 9). For the subscale of Content Knowledge (CK), special education teachers scored lower than general education teachers and advanced/honors teachers. Additionally, for CK, general education teachers scored lower than advanced/honors teachers. Lastly, for the subscale of Pedagogy Content Knowledge (PCK), special education teachers scored significantly lower than advanced/honors teachers. Table 9: Univariate Analysis of Variance for Rigor in TPACK (seven subscales) Subscale df F p Rigor Group Differences Technology Knowledge (TK) 2 0.83 .435 Content Knowledge (CK) 2 12.96 .001 Special Education < General Education Special Education < Honors/Advanced General Education < Honors/Advanced Pedagogy Knowledge (PK) 2 2.13 .120 Pedagogical Knowledge (PCK) 2 3.67 .026 Special Education < Honors/Advanced Technology Content Knowledge (TCK) 2 0.97 .379 Technological Pedagogical Knowledge (TPK) 2 0.76 .467 Technological Pedagogical Content Knowledge (TPACK) 2 2.09 .125 58 58 Table 10: Means and Standard Deviations of TPACK by Rigor Subscale Rigor Mean SD N Content Knowledge (CK) General Education 4.46 0.49 313 Honors/Advanced 4.74 0.36 53 Special Education 4.25 0.47 43 Pedagogical Content Knowledge (PCK) General Education 4.36 0.55 304 Honors/Advanced 4.54 0.50 52 Special Education 4.37 0.56 42 Note. All scores were scaled scores. Subject Differences The overall MANOVA model was not significant for the main effect of subjects (English/Language Arts, Foreign Language, History/Social Science, Mathematics, Physical Education, Science and other electives) in the seven TPACK subscales. Years of Experience Differences The overall MANOVA model was significant for the main effect of years of experience (1-5, 6-15, 16-20 and 21+) in the seven TPACK subscales, [F(21, 1103) = 7.01, p<.001, Wilks Lambda = 0.698]. Subsequent ANOVAS revealed significant grade level group differences in four of the seven subscales (see table 11). For the subscale of Technology Knowledge (TK), teachers in their first five years scored significantly higher than teachers teaching 16-20 & 21+ 59 59 years. Additionally, for TK, teachers who have taught 6-15 years scored significantly higher than teachers teaching for more than 21 years. For the subscale of Content Knowledge (CK), teachers in their first five years scored significantly lower than teachers teaching 6-15, and more than 21 years. For the subscale of Pedagogy Knowledge (PK), teachers in their first five years scored significantly lower than all other teachers (6-15, 16-20, and 21+). For the subscale of Pedagogical Content Knowledge (PCK), teachers in their first five years scored significantly lower than teachers with 21+ years of experience. For the subscale of Technological Content Knowledge (TCK), teachers with 21+ years of experience scored significantly lower than teachers in their first 15 years teaching. For the subscale of Technological Pedagogical Knowledge (TPK), teachers with 21+ years of experience scored significantly lower than teachers in their first 15 years of teaching. Additionally, for TPK, teachers with 16-20 years of teaching scored lower than teachers in their first 15 years of teaching—this score was approaching significance at p=.060 and p=.075, respectively. Finally, for the subscale of Technological Pedagogical Content Knowledge (TPACK), teachers with 21+ years of experience scored significantly lower than teachers in their first 15 years of teaching. The means for the subscales by years of experience are in Table 12. 60 60 Table 11: Univariate Analysis of Variance for Years of Experience in TPACK (seven subscales) Subscale df F p Experience Group Differences Technology Knowledge (TK) 3 17.83 .001 16-20 < 1-5 21+ < 1-5 21+ < 6-15 Content Knowledge (CK) 3 8.57 .001 1-5 < 6-15 1-5 < 21+ Pedagogy Knowledge (PK) 3 15.15 .001 1-5 < 6-15 1-5 < 16-20 1-5 < 21+ Pedagogical Knowledge (PCK) 3 4.40 .005 1-5 < 21+ Technology Content Knowledge (TCK) 3 5.86 .001 21+ < 1-5 21+ < 6-15 Technological Pedagogical Knowledge (TPK) 3 14.25 .001 21+ < 1-5 21+ < 6-15 16-20 < 1-5 (approaching sig .060) 16-20 < 6-15 (approaching sig .075) Technological Pedagogical Content Knowledge (TPACK) 3 8.33 .001 21+ < 1-5 21+ < 6-15 Table 12: Means and Standard Deviations of TPACK by Years of Experience Subscale Years of Experience Mean SD N Technology Knowledge (TK) 1-5 Years 4.04 0.67 82 6-15 Years 3.82 0.78 127 16-20 Years 3.59 0.73 73 21+ 3.35 0.76 143 61 61 Content Knowledge (CK) 1-5 Years 4.26 0.50 79 6-15 Years 4.48 0.48 125 16-20 Years 4.47 0.52 69 21+ 4.60 0.43 141 Pedagogy Knowledge (PK) 1-5 Years 4.19 0.45 78 6-15 Years 4.46 0.44 125 16-20 Years 4.49 0.45 69 21+ 4.46 0.41 141 Pedagogical Content Knowledge (PCK) 1-5 Years 4.18 0.48 77 6-15 Years 4.34 0.54 120 16-20 Years 4.40 0.55 67 21+ 4.46 0.59 139 Technological Content Knowledge (TCK) 1-5 Years 4.16 0.67 77 6-15 Years 4.10 0.63 120 16-20 Years 3.84 0.86 67 62 62 21+ 3.82 0.73 139 Technological Pedagogical Knowledge (TPK) 1-5 Years 3.98 0.60 77 6-15 Years 3.95 0.55 120 16-20 Years 3.70 0.67 67 21+ 3.51 0.68 139 Technological Pedagogical Content Knowledge (TPACK) 1-5 Years 3.93 0.58 76 6-15 Years 3.90 0.64 117 16-20 Years 3.78 0.67 66 21+ 3.54 0.74 136 Note. All scores were scaled scores. Socioeconomic Status of School Differences The overall MANOVA model was not significant (it did approach significance [F(7, 386) = 1.87, p<.073, Wilks Lambda = 0.967]) for the main effect of socioeconomic status of the school (Title I or not) in the seven TPACK subscales. Research Question Two: Demographic Group Differences in Knowledge of ISTE 2017 Across the district, teachers were very unfamiliar with the ISTE 2017 technology standards (see figure 4 & table 13); 47.3% of teachers were not familiar with ISTE 2017 at all. Only, 12.5% of teachers were moderately or largely familiar with ISTE 2017 standards. Research question two explored if there were differences in teacher knowledge of ISTE 2017 by gender, 63 63 race, teacher education, grade taught, rigor, subject, years of experience or socioeconomic status of the school. Eight one-way ANOVAs were conducted for each of the independent variables with knowledge of ISTE 2017. None of the independent variables revealed significance. Only years of experience approached significance with p = .075. Figure 4 Teachers’ Familiarity of ISTE 2017 64 64 Table 13: Teachers Familiarity of ISTE 2017 (mean =1.96) Frequency Percent Cumulative Percent Not at All 201 47.3 47.3 To a Small Extent 102 24.0 71.3 To Some Extent 73 17.2 88.5 To a Moderate Extent 38 8.9 97.4 To a Large Extent 11 2.6 100.0 Total 425 100.0 Research Question Three: Demographic Group Differences in Technology Implementation (TPTI) The TPTI instrument, based on a six-point Likert scale—Never (1), Rarely (2), Sometimes (3), Frequently (4), Usually (5), Always (6) with N/A (0), serves to capture the quality of technology implementation. Overall, on the TPTI, teachers scored significantly below standard in effective technology implementation. The lowest average was Learner, 3.58 and Citizen, 3.58, followed closely by Leader 3.63. The highest average was the Facilitator subscale, 4.15. See the graph below (Figure 5) to see the distribution including a line that represents the standard of technology implementation. Additionally see Table 14 for a summary of means. Research question one explored if there were differences in technology implementation (TPTI) by gender, race, teacher education, grade taught, rigor, subject, years of experience or socioeconomic status of the school. Eight one-way MANOVAs were conducted for each of the independent variables with TPTI (seven subscales) due to insufficient and uneven cell sizes. Upon finding significance in the MANOVA test, follow-up ANOVAs were conducted with post hocs and means reported. 65 65 Figure 5: MEANS of TPTI Note: Never (1), Rarely (2), Sometimes (3), Frequently (4), Usually (5), Always (6) and N/A (0) Table 14: Descriptive statistics of TPTI Subscale Mean SD N Learner 3.58 1.08 380 Leader 3.63 1.12 357 Citizen 3.58 1.11 337 Collaborator 3.74 1.12 319 Designer 3.76 1.11 314 Facilitator 4.15 1.04 305 Analyst 3.67 1.13 286 Note: Never (1), Rarely (2), Sometimes (3), Frequently (4), Usually (5), Always (6) and N/A (0) 66 66 Gender Differences The MANOVA revealed an overall significant effect for gender (male and female) in the seven TPTI subscales [F(7, 269) = 2.37, p<.023, Wilks Lambda = 0.942]. However, subsequent univariate analysis of variance did not show significance between genders in any of the TPTI subscales. Race Differences The overall MANOVA model was significant for the main effect of race (Asian/Pacific Islander, Hispanic, White/Caucasian and Other) in the seven TPTI subscales, [F(21, 770) = 2.03, p<.004, Wilks Lambda = 0.857]. Subsequent ANOVAS revealed significant racial group differences in all seven subscales (see table 15). For the subscale of Learner, White/Caucasian scored significantly lower than Asian/Pacific Islander and Other Races. For the subscale of Leader, White/Caucasian scored significantly lower than all other categories (Asian/Pacific Islander, Hispanic and Other Races). For the subscale of Citizen, White/Caucasian scored significantly lower than Asian/Pacific Islander and Other Races. For the subscale of Collaborator, White/Caucasian scored significantly lower than Hispanic and Other Races. For the subscale of Designer, White/Caucasian scored significantly lower than all other categories (Asian/Pacific Islander, Hispanic and Other Races). Finally, for the subscales of Facilitator and Analyst, White/Caucasian scored significantly lower than Other Races. 67 67 Table 15: Univariate Analysis of Variance for Race in TPTI (seven subscales) Subscale df F p Racial Group Differences Learner 3 6.28 .001 White/Caucasian < Asian/Pacific Islander White/Caucasian < Other Races Leader 3 7.73 .001 White/Caucasian < Asian/Pacific Islander White/Caucasian < Hispanic White/Caucasian < Other Races Citizen 3 7.24 .001 White/Caucasian < Asian White/Caucasian < Other Races Collaborator 3 7.83 .001 White/Caucasian < Hispanic White/Caucasian < Other Races Designer 3 5.77 .001 White/Caucasian < Asian/Pacific Islander White/Caucasian < Hispanic White/Caucasian < Other Races Facilitator 3 4.53 .004 White/Caucasian < Other Races Analyst 3 5.98 .001 White/Caucasian < Other Races Table 16: Means and Standard Deviations of TPTI by Race Subscale Race Mean SD N Learner Asian/Pacific Islander 3.92 1.15 29 Hispanic 3.63 1.13 78 White/Caucasian 3.31 1.04 225 Other Race 3.88 1.03 48 Leader Asian/Pacific Islander 3.99 1.13 28 Hispanic 3.76 1.12 73 68 68 White/Caucasian 3.33 1.06 209 Other Race 4.00 1.15 47 Citizen Asian/Pacific Islander 4.02 1.10 27 Hispanic 3.68 1.10 66 White/Caucasian 3.30 1.05 199 Other Race 3.92 1.13 45 Collaborator Asian/Pacific Islander 4.06 1.08 25 Hispanic 3.90 1.08 62 White/Caucasian 3.46 1.07 191 Other Race 4.22 1.15 41 Designer Asian/Pacific Islander 4.13 1.07 25 Hispanic 3.95 1.03 63 White/Caucasian 3.52 1.07 184 Other Race 4.08 1.22 42 Facilitator Asian/Pacific Islander 4.43 0.91 25 69 69 Hispanic 4.23 1.00 63 White/Caucasian 3.98 1.05 177 Other Race 4.56 1.03 40 Analyst Asian/Pacific Islander 4.08 1.06 24 Hispanic 3.79 1.10 59 White/Caucasian 3.45 1.11 164 Other Race 4.14 1.07 39 Note. All scores were scaled scores. Teacher Education Differences The overall MANOVA was not significant between teacher education (bachelor’s degree and master’s degree) in the seven TPTI subscales, [F(7, 270) = 1.17, p<.322, Wilks Lambda = 0.971]. Grade Level Differences The overall MANOVA model was significant for the main effect of grade level (lower elementary (K-3), upper elementary (4-6), junior high and high school) in the seven TPTI subscales, [F(21, 770) = 2.00, p<.005, Wilks Lambda = 0.859]. Subsequent ANOVAS revealed significant grade level group differences in all of the seven subscales (see table 17). For the subscale of Learner, lower elementary scored significantly lower than upper elementary. For the subscale of Leader, lower elementary scored significantly lower than upper elementary and high school. Additionally, for Leader, junior high scored significantly lower than upper elementary. 70 70 For the subscales of Citizen, Collaborator, and Designer, lower elementary scored significantly lower than both upper elementary and high school. For the subscale of Facilitator, lower elementary scored significantly lower than upper elementary. Finally, for the subscale of Analyst, lower scored significantly lower than all other categories (upper elementary, junior high, and high school). The means for the subscales by grade level are in Table 18. Table 17: Univariate Analysis of Variance for Grade Level in TPTI (seven subscales) Subscale df F p Grade Level Group Differences Learner 3 6.11 .001 Lower Elementary < Upper Elementary Leader 3 7.81 .001 Lower Elementary < Upper Elementary Lower Elementary < High School Junior High < Upper Elementary Citizen 3 6.73 .001 Lower Elementary < Upper Elementary Lower Elementary < High School Collaborator 3 8.87 .001 Lower Elementary < Upper Elementary Lower Elementary < High School Designer 3 5.76 .001 Lower Elementary < Upper Elementary Lower Elementary < High School Facilitator 3 5.90 .001 Lower Elementary < Upper Elementary Analyst 3 8.15 .001 Lower Elementary < Upper Elementary Lower Elementary < High School Lower Elementary < Junior High Table 18: Means and Standard Deviations of TPTI by Grade Level Subscale Grade Level Mean SD N Learner Lower Elementary (K- 3) 3.17 1.15 98 Upper Elementary (4-6) 3.84 1.12 82 Junior High 3.41 1.05 77 71 71 High School 3.57 0.95 123 Leader Lower Elementary (K- 3) 3.17 1.14 94 Upper Elementary (4-6) 3.94 1.10 77 Junior High 3.45 1.56 74 High School 3.56 1.01 112 Citizen Lower Elementary (K- 3) 3.14 1.03 88 Upper Elementary (4-6) 3.90 1.16 73 Junior High 3.50 1.11 67 High School 3.57 1.04 109 Collaborator Lower Elementary (K- 3) 3.25 1.06 86 Upper Elementary (4-6) 4.13 1.14 70 Junior High 3.67 1.14 63 High School 3.78 1.00 100 Designer Lower Elementary (K- 3) 3.35 1.00 84 Upper Elementary (4-6) 4.06 1.23 72 72 72 Junior High 3.75 1.14 62 High School 3.80 1.00 96 Facilitator Lower Elementary (K- 3) 3.81 1.00 84 Upper Elementary (4-6) 4.51 1.13 68 Junior High 4.15 1.04 60 High School 4.17 0.93 93 Analyst Lower Elementary (K- 3) 3.18 1.01 80 Upper Elementary (4-6) 4.01 1.25 65 Junior High 3.80 1.08 54 High School 3.77 1.04 87 Note. All scores were scaled scores. Rigor Differences The overall MANOVA model was not significant for the main effect of rigor (general education, advanced/honors and special education) in the seven TPTI subscales, [F(14, 530) = 1.50, p<.105, Wilks Lambda = 0.925]. Subject Differences The overall MANOVA model was significant for the main effect of subjects (English/Language Arts, Foreign Language, History/Social Science, Mathematics, Physical 73 73 Education, Science and other electives) in the seven TPTI subscales, [F(42, 585) = 1.76, p<.003, Wilks Lambda = 0.572]. Subsequent ANOVAS revealed significant subject differences in four of the seven subscales (see table 19). For the subscale of Learner, physical education teachers scored significantly lower than English/Language Arts teachers, science teachers and other elective teachers. For the subscale of Leader, physical education teachers scored significantly lower than other elective teachers. Additionally, for the Leader subscale, other elective teachers scored significantly lower than History/Social Science teachers. For the subscales of Citizen, Collaborator and Designer, although the ANOVAs were significant, subsequent post hocs did not yield specific group differences. Finally, for the subscales of Facilitator and Analyst, physical education teachers scored significantly lower than science teachers and other elective teachers. The means for the subscales by subject are in Table 20. Table 19: Univariate Analysis of Variance for Subjects in TPTI (seven subscales) Subscale df F p Subject Group Differences Learner 6 3.82 .001 Physical Education < English/Language Arts Physical Education < Science Physical Education < Other Electives Leader 6 4.08 .001 Physical Education < Other Electives Other Electives < History/Social Science Citizen 6 2.38 .031 Collaborator 6 2.46 .027 Designer 6 2.78 .039 Facilitator 6 4.16 .001 Physical Education < Science Physical Education < Other Electives Analyst 6 3.08 .007 Physical Education < Science Physical Education < Other Electives 74 74 Table 20: Means and Standard Deviations of TPTI by Subject Subscale Subject Mean SD N Learner English/Language Arts 3.69 0.91 52 Foreign Language 3.84 1.25 7 History/Social Science 3.11 1.08 28 Mathematics 3.46 1.04 34 Physical Education 2.49 0.79 11 Science 3.66 0.93 40 Other Electives 3.73 1.04 28 Leader English/Language Arts 3.61 0.91 49 Foreign Language 4.32 0.72 5 History/Social Science 3.20 1.24 27 Mathematics 3.44 1.08 34 Physical Education 2.60 0.65 9 Science 3.78 0.99 39 Other Electives 3.60 1.07 23 Facilitator English/Language Arts 4.11 0.87 43 75 75 Foreign Language 4.27 1.15 6 History/Social Science 3.84 1.21 20 Mathematics 4.03 1.02 24 Physical Education 3.05 0.35 8 Science 4.63 0.76 33 Other Electives 4.40 0.89 19 Analyst English/Language Arts 3.63 0.91 40 Foreign Language 3.75 1.53 4 History/Social Science 3.46 1.35 18 Mathematics 3.87 1.13 23 Physical Education 2.52 0.34 6 Science 4.15 0.87 34 Other Electives 4.11 0.92 16 Note. All scores were scaled scores. Years of Experience Differences The overall MANOVA model was significant for the main effect of years of experience (1-5, 6-15, 16-20 and 21+) in the seven TPTI subscales, [F(21, 770) = 2.21, p<.001, Wilks Lambda = 0.845]. Subsequent ANOVAS revealed significant grade level group differences in six of the seven subscales (see table 21). For the subscales of Learner, Leader, Citizen, Collaborator 76 76 and Designer teachers who have taught 16 or more years scored significantly lower than teachers who have taught 15 or less years. For the subscale of Facilitator, although the ANOVAs were significant, subsequent post hocs did not yield specific group differences. Finally, for the subscale of Analyst, teachers who have taught more than 21 years scores significantly lower than teachers who have taught 15 years or less. The means for the subscales by years of experience are in Table 22. Table 21: Univariate Analysis of Variance for Years of Experience in TPTI (seven subscales) Subscale df F p Experience Group Differences Learner 3 8.36 .001 16-20 < 1-5 16-20 < 6-15 (approaching p=.069) 21+ < 1-5 21+ < 6-15 Leader 3 9.15 .001 16-20 < 1-5 16-20 < 6-15 21+ < 1-5 21+ < 6-15 Citizen 3 5.19 .002 16-20 < 1-5 16-20 < 6-15 (approaching p=.072) 21+ < 1-5 21+ < 6-15 Collaborator 3 5.76 .001 21+ < 1-5 21+ < 6-15 16-20 < 6-15 (approaching p=.079) Designer 3 5.30 .001 21+ < 6-15 16-20 < 6-15 16-20 < 1-5 (approaching p=.081) Facilitator 3 3.25 .022 Analyst 3 4.35 .005 21+ < 1-5 21+ < 6-15 77 77 Table 22: Means and Standard Deviations of TPTI by Years of Experience Subscale Years of Experience Mean SD N Learner 1-5 Years 3.85 1.06 73 6-15 Years 3.68 1.03 115 16-20 Years 3.08 0.99 61 21+ 3.32 1.10 131 Leader 1-5 Years 3.87 1.02 69 6-15 Years 3.85 1.04 107 16-20 Years 3.20 1.11 57 21+ 3.29 1.15 124 Citizen 1-5 Years 3.79 1.12 68 6-15 Years 3.72 1.05 100 16-20 Years 3.23 1.06 54 21+ 3.31 1.10 115 Collaborator 1-5 Years 3.94 1.06 62 6-15 Years 3.95 1.01 97 78 78 16-20 Years 3.46 1.17 53 21+ 3.43 1.14 107 Designer 1-5 Years 3.95 1.04 61 6-15 Years 3.99 1.04 95 16-20 Years 3.42 1.15 52 21+ 3.53 1.12 106 Analyst 1-5 Years 3.95 1.06 56 6-15 Years 3.86 1.05 89 16-20 Years 3.40 1.09 43 21+ 3.44 1.18 98 Note. All scores were scaled scores. Socioeconomic Status of School Differences The overall MANOVA model was not significant for the main effect of socioeconomic status of the school (Title I or not) in the seven TPTI subscales, [F(7, 270) = 0.426, p<.886, Wilks Lambda = 0.989]. 79 79 Research Question Four: Teachers Knowledge (TPACK) and Technology Implementation (TPTI) Research question four explored the relationship between teacher knowledge (TPACK) and technology implementation (TPTI). Seven separate multiple regressions were conducted for each of the seven subscales of TPTI with TPACK and years teaching. Learner A simultaneous multiple linear regression with the Learner subscale of TPTI as the criteria variable and the seven subscales of TPACK and years as the predictor variables were conducted. Results indicated a significant overall model [F(8,369) = 31.36, p<.001], with the eight domains explaining 41% of the variances in the way teachers engage with technology as a learner. Furthermore, Technology Knowledge (TK), Content Knowledge (CK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK) were each significant predictors (see Table 23). Leader A simultaneous multiple linear regression with the Leader subscale of TPTI as the criteria variable and the seven subscales of TPACK and years as the predictor variables were conducted. Results indicated a significant [F(8, 346) = 31.36, p<.001], with the eight domains explaining 42% of the variances in the way teachers engage with technology as a leader. Furthermore, Technology Knowledge (TK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK) were each significant predictors (see Table 23). Citizen A simultaneous multiple linear regression with the Citizen subscale of TPTI as the criteria variable and the seven subscales of TPACK and years as the predictor variables were 80 80 conducted. Results indicated a significant [F(8, 326) = 22.47, p<.001], with the eight domains explaining 34% of the variances in the way teachers engage with technology as a citizen. Furthermore, Technology Knowledge (TK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK) were each significant predictors (see Table 23). Collaborator A simultaneous multiple linear regression with the Collaborator subscale of TPTI as the criteria variable and the seven subscales of TPACK and years as the predictor variables were conducted. Results indicated a significant [F(8, 308) = 24.34, p<.001], with the eight domains explaining 37% of the variances in the way teachers engage with technology as a collaborator. Furthermore, Technology Knowledge (TK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK) were each significant predictor (see Table 23). Designer A simultaneous multiple linear regression with the Designer subscale of TPTI as the criteria variable and the seven subscales of TPACK and years as the predictor variables were conducted. Results indicated a significant [F(8, 303) = 20.78, p<.001], with the eight domains explaining 35% of the variances in the way teachers engage with technology as a designer. Furthermore, Technology Knowledge (TK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK) were each significant predictors (see Table 23). 81 81 Facilitator A simultaneous multiple linear regression with the Facilitator subscale of TPTI as the criteria variable and the seven subscales of TPACK and years as the predictor variables were conducted. Results indicated a significant [F(8, 294) = 19.05, p<.001], with the eight domains explaining 34% of the variances in the way teachers engage with technology as a facilitator. Furthermore, Technology Knowledge (TK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK) were each significant predictors (see Table 23). Analyst A simultaneous multiple linear regression with the Analyst subscale of TPTI as the criteria variable and the seven subscales of TPACK and years as the predictor variables were conducted. Results indicated a significant [F(8, 275) = 25.45, p<.001], with the eight domains explaining 43% of the variances in the way teachers engage with technology as an analyst. Furthermore, Technology Knowledge (TK), Pedagogy Knowledge (PK), Technological Content Knowledge (CK), Technological Pedagogical Knowledge (TPK) and Technological Pedagogical Content Knowledge (TPACK) were each significant predictors (see Table 23). Table 23: Summary of Simultaneous Regression Analysis for TPACK and TPTI R^2 F Beta p Learner .405 31.356 .001 TK .238 .001 CK -.110 .050 PK .022 .739 82 82 PCK .006 .915 TCK -.061 .326 TPK .178 .031 TPACK .363 .001 Years .019 .694 Leader .420 31.364 .001 TK .184 .002 CK -.074 .201 PK .012 .858 PCK -.005 .931 TCK -.036 .574 TPK .215 .011 TPACK .352 .001 Years -.024 .620 Citizen .340 22.465 .001 TK .167 .009 CK -.072 .260 PK -.010 .889 PCK .058 .373 TCK -.132 .064 83 83 TPK .276 .003 TPACK .325 .001 Years .009 .867 Collaborator .371 .24.343 .001 TK .178 .005 CK -.049 .451 PK -.026 .733 PCK .060 .359 TCK -.122 .091 TPK .302 .001 TPACK .307 .001 Years .003 .960 Designer .354 20.782 .001 TK .189 .004 CK .050 .454 PK -.063 .424 PCK .077 .259 TCK -.128 .081 TPK .235 .014 TPACK .303 .001 84 84 Years -.019 .726 Facilitator .341 19.054 .001 TK .233 .001 CK .000 .996 PK .005 .953 PCK .087 .210 TCK -.127 .094 TPK .249 .010 TPACK .242 .006 Years .017 .768 Analyst .425 25.452 .001 TK .216 .001 CK .079 .242 PK -.164 .037 PCK .103 .124 TCK -.220 .003 TPK .286 .003 TPACK .364 .001 Years .000 .994 85 85 Research Question Five: Role of COVID-19 on Technology Implementation Research question five served to demonstrate how teachers reported COVID-19 affected technology implementation in the classroom. As seen in Figure 6, a majority of teachers reported that their implementation of technology was totally different than prior to COVID-19. Figure 6: Role of COVID-19 on Technology Implementation 86 86 Chapter Five: Discussion The goal of this study was to empirically example the factors that affect technology implementation in K-12 classrooms. More specifically, this study sought to explore if technology knowledge (TPACK) and years of experience could be used to predict technology implementation as measured by TPTI. Results of this study show that across the board there is a lot of room for improvement on all categories of the 2017 ISTE Standards for Educators. The following chapter provides a summary and discussion of the results as well as implications for both theory and practice. Limitations of this study are also discussed as well as suggestions for future research. Discussion of Findings The results of this study emphasize the vast need for improvement in appropriate implementation of technology. Teachers lack technology knowledge, especially knowledge for implementing technology in an academic setting. Furthermore, overall technology is not being implemented in a way that best prepares students to be competitive in the 21st century workplace. Teacher Knowledge (TPACK) Results from this study confirmed the hypothesis that there are demographic group differences in knowledge of TPACK. On the TPACK four of the eight tested demographic groups showed differences in TPACK: race, grade taught, rigor and years of experience. Years of experience showed statistical significance in all seven of the TPACK subscales. Grade taught showed statistical significance in four of the seven TPACK subscales (Technology Knowledge, Content Knowledge, Technological Pedagogical Knowledge and TPACK). Race showed statistical significance in four of the seven TPACK subscales (Technology Knowledge, Consent 87 87 Knowledge, Pedagogical Knowledge, and Technological Pedagogical Knowledge). Finally, rigor only showed statistical significance in two of the seven TPACK subscales, neither having to do with technology (Content knowledge and Pedagogical Knowledge). There were four major trends from the results of TPACK that are important to discuss. First, there was an inverse relationship between teacher experience and knowledge—teachers with more experience had statistically significantly more content and pedagogical knowledge, and significantly less technological knowledge than teachers who have taught less years. Teachers who have taught five years or less have the least amount of Pedagogy Knowledge and Content knowledge compared to teachers who have taught six years or more. This is consistent with the research as beginning teachers are just starting out they will develop and gain more pedagogical and content knowledge from experiences in the classroom (Liu, Zang, & Wang, 2015; Koh, Chai, & Tsai, 2014; Cheng & Xie, 2018). The opposite is true for teachers who have been teaching for sixteen years or more, they have significantly less technology knowledge and knowledge to apply technology in an academic setting than teachers who have taught for five years or less. This makes sense because technology was added as a third paradigm as TPACK in 2006 (Mishra & Koehler, 2006) and since then, there has been a large focus on teaching preservice teachers how to appropriately and effectively teach with technology. (Abbitt, 2011; Anderson, et al., 2017; Brush, et al., 2008; Koh et. al, 2010). Therefore, unless teachers who graduated their credential program before 2006 have received professional development from the district or have sought out training for themselves then teachers who have taught for sixteen or more years lack formal technology training. 88 88 Although these findings were consistent with the mentioned previous studies, this study was unique because it is the first study (to the researcher’s knowledge) to examine in-service teacher’s TPACK. Previous research focused mostly on preservice teachers’ knowledge. As such, this finding makes a valuable contribution to the literature because it provides empirical evidence of the gap that needs to be addressed as 50.8% of teachers have taught in the district for sixteen or more years. Furthermore, because the average teacher retires after 27 years, this means that unless addressed these teachers will remain under qualified and able to provide the best opportunities for students to develop skills to be successful in the 21st century workforce for years to come. Second, lower elementary (K-3) teachers had the least knowledge across Technology Knowledge (TK), Content Knowledge (CK), Technological Pedagogical Knowledge (TPK), and Technological Pedagogical Content Knowledge (TPACK) as compared to all other teachers. This is interesting because the preservice training is the exact same courses for teachers who teach upper elementary (4-6) and very similar to secondary teachers except for the courses that are content specific (Commission on Teacher Credentialing, 2017). This study found different results than Farrell and Hamed (2017) who found no relationship between grade level and amount of TPACK. While not part of the original research question, in order to dive into the reasoning behind this finding, it was revealed that K-3 teachers have statistically significant more years of experience than other grade levels. This may be a confounding variable that accounts for the difference in knowledge as this follows the trend of teachers with more experience having less technology knowledge. Additionally, both lower elementary and upper elementary scored lower than both junior high and high school on the Content Knowledge subscale. This is supported by the subject matter 89 89 competency requirements to become a secondary teacher versus a primary teacher. Primary teachers specialize in multiple subjects and must pass three subtests—the first one tests English/Language Arts and history, the second one tests mathematics and science and the last one tests physical education. On the other hand, secondary teachers must pass two or three, junior high and high school respectively, on all aspects of one subject (Commission for Teacher Credentialing, 2017). This requires knowledge of a content area in much more depth. Race has not been previously studied with TPACK. In this study, there was not an overarching comprehensive theme that was found as there was with years of experience and grade level. However, significance was found in TK, CK, PK, and TPK. In both subscales that included technology, TK and TPK, Caucasian teachers were found to have significantly less TPACK than their Asian/Pacific Islander and Other Race counterparts, respectively. Hispanic teachers had significantly less CK and PK than White/Caucasian teachers and Other Races of teachers. These findings significantly contribute to the literature as it serves as a baseline as it is the first study to empirically demonstrate teachers’ TPACK by race. Lastly, It is important to draw attention to the lack of significant difference in knowledge of Technology Pedagogical Content Knowledge (TPACK) across socioeconomic status. This finding refutes previous research on the digital divide and the second digital divide that argues that students in lower income areas are provided less rigorous application of technology there more affluent counterparts (DiMaggio, et al., 2004). This is a positive finding as it suggests that students across socioeconomic classes are being taught by teachers with similar knowledge— thus, there is equity in access to teacher human capital. 90 90 2017 ISTE Standards for Education Overall, teachers are largely unfamiliar with the 2017 ISTE Standards for Education. 88.5% of teachers in ABC Valley were only familiar with the standards to some extent. There are many factors that influence this finding. While the ISTE standards are the most recognized and widely accepted technology standards across the country and internationally their adoption varies from state to state and district to district. While only 22 miles away, the largest district in California, Los Angeles County Unified School District has formally adopted the 2017 ISTE Standards for Educators, ABC Valley Unified has not formally adopted any technology standards. In further research of the district website the only mention of ISTE is the 2011 ISTE Standards for Coaches. There were no demographic group differences found in this study when familiarity of the 2017 ISTE Standards were explored. This finding contributes to literature in two important ways: 1) it focuses the attention on the enormous gap of exposure, 88.5% of teachers need to learn about and how to use the technology standards and 2) technology standards need to be adopted nationally like Common Core and NGSS to ensure that all students are being taught equally and the most advanced technology standards. Technology Implementation (TPTI) Teachers scored between 3.58 and 4.15 on a scale of six across all subscales of the TPTI. A score of a six would be considered a “gold standard” —implementing technology in the most appropriate way. While there were demographic differences in how technology was used in the classroom, across the board there is much room for improvement. This finding is concerning because if technology is not effectively being utilized in the classroom, then students are missing opportunities to develop skills to be competitive in the 21st century marketplace. Most research that has been conducted in studying technology implementation has been measured qualitatively. 91 91 To the researcher’s knowledge there has not been any studies that have quantitatively measured in-service teachers' use of technology, especially not with the 2017 ISTE Standards for Educators. This baseline is an extremely valuable insight to contribute to literature because it is the first and serves to highlight the gap between what teachers are currently doing in the classroom versus what they could do. Results from this study confirmed the hypothesis that there are demographic group differences in implementation of technology in the classroom as measured using TPTI. On the TPTI only four of the eight tested demographic groups showed differences across the TPTI subscales: race, grade taught, subject and years of experience. Years of experience, race, and grade taught showed statistical significance in all seven of the TPTI subscales. Subject showed statistical significance in four of the seven TPTI subscales: Learner, Leader, Facilitator and Analyst. First, across all seven subscales, teachers with sixteen years of experience or more implemented technology less appropriately than teachers with fifteen years of experience or less. Again, as previously stated, unless teachers were provided training from the district or personally sought out professional development then teachers who have taught longer than fifteen years lack formal training in implementing technology in the classroom. Additionally, fifteen years ago neither the 2017 ISTE Standards for Educators nor the 2007 ISTE standards existed. As a result, for the same reasons the teachers may not be aware of the existence of technology standards. This is supported by the second research question where 47% of teachers were completely unfamiliar with 2017 ISTE Standards for Educators. Definitions of technology have changed over time and varies dramatically. Technology can refer to the hardware, to the software, to the way it is used or a combination thereof. 92 92 Hardware is classified as the physical device(s)—a computer, a cell phone, a wearable, etc. Software is the system, it is what the device can do—Google Suite, Microsoft Suite, applications, social media platforms, etc.. Then there are branches and intertwined concepts such as, Digital Learning Materials (Kreijns, et al., 2013), Technology Literacy, Computer Literacy, Information Literacy, Technology Literacy, Media Literacy, Digital Literacy (Covello, 2010; Kazu & Erten, 2013; Spengler, 2015; US Department of Education National Center for Education Statistics, 2007). Each of these concepts address the vastness of technology. Yet, we do not have a formal definition of technology and therefore, there are a lot of misconceptions about what is meant by technology implementation. Teachers in older years may believe that creating a PowerPoint presentation is using technology, rather than the more updated concept of technology that includes the intentional use of technology to gain deeper understandings of the content. If teachers do not know and understand how to implement the standards, this suggests they are incapable of doing their job to help students master the desired skills and content. Therefore, there would be a lack of consistency from one teacher to the next and inconsistencies in students’ baseline skills. This study serves to highlight that 50.8% of teachers (teachers who have taught more than 15 years) are unprepared to best support our students, and that without intervention some of these teachers may continue to teach another 30 years before retiring. While no research has yet to measure teacher technology usage according the 2017 ISTE Standards for educators, these findings are consistent with research regarding technology knowledge, specifically the inverse relationship between teacher experience and technology knowledge (Cheng & Xie, 2018; Koh et. al; 2014; Liu, et al., 2015; ). These findings were novel because they are the first in the literature to examine technology implementation practices of in-service teachers according to the 2017 ISTE 93 93 Standards for Educators. These findings further highlight the important gap, not just in differences in technology knowledge, but in technology practices in classrooms that unless addressed will have long standing consequences on the development of students to be college and career ready and in time may potentially impact the ability to be competitive globally. Secondly, the two groups of teachers who implement technology the most and the least appropriately reside on the same campuses—lower elementary (K-3) and upper elementary (4-6) teachers. Lower elementary teachers implement technology statistically significantly less appropriately than all other grade levels. There was no research regarding implementation by grade level and the 2017 ISTE Standards for Educators. However, these findings somewhat contradict Koh et. al (2014), who theoretically discussed technology implementation and suggested that secondary teachers had more time and energy to implement technology. Teachers who taught upper elementary (grades 4-6) implemented technology most appropriately, then followed by high school teachers and junior high (grades 7-12) teachers. This significantly adds to the literature because it is the first study to comprehensively examine technology implementation by grade level. Thirdly, White teachers implement technology least appropriately as compared to the rest of their colleagues. This is important to note because according to this study 58.6% of the population of teachers are Caucasian. Therefore, a majority of teachers are significantly influencing students’ access to better opportunities to develop their technological skills to be competitive in college and their careers. There is no research regarding teachers’ technology implementation by race. This study will fill the gap and contribute to the literature by providing information regarding the status of teachers’ technology implementation by race. 94 94 Fourthly, physical education teachers overwhelmingly scored significantly lower in all areas of technology implementation. This is a concerning finding because physical education teachers’ practices have not mirrored the evolution of available technology and gains that leveraging technology could mean. For example, in the standard archery unit of only participating using the bow and arrow to strike the target—technology could be leveraged to learn about trajectory, speed, angles, etc. Or, Fitbits could be worn, and data could be analyzed to learn about fitness, Body Mass Index (BMI), and macronutrients, etc. There is no research on technology implementation by subject in relationship to the 2017 ISTE Standards for Educators. This study contributes to the literature to raise awareness to the gap in physical education and the need to update practices, and also recognize that appropriate technology implementation is part of all teachers’ duty because technology transcends subject matter. Finally, it is important to draw attention to the lack of significant difference in both knowledge and implementation of technology across socioeconomic status. This finding refutes previous research on the digital divide and the second digital divide that argues that students in lower income areas are provided less rigorous application of technology there more affluent counterparts (DiMaggio, et al., 2004; Warschauer, et al., 2002; Wayne, et al., 2002). While there is no literature that specifically discusses technology implementation according to SES with the 2017 ISTE Standards for Educators, this is a positive finding as it suggests that students across socioeconomic classes are being taught by teachers with similar knowledge—thus, there is equity in access to teacher human capital. Relationship between TPACK and Years of Experience with TPTI This study sought to explore whether TPACK and years of experience were predictive of technology implementation according to TPTI. Specifically, it was hypothesized that both 95 95 knowledge (TPACK) and years of experience would predict how appropriately teachers implemented technology. While together TPACK and years of experience do predict the appropriateness of technology implementation on all seven subscales: Learner, Leader, Citizen, Collaborator, Designer, Facilitator, and Analyst. TPACK was far more predictive of implementation than years of experience. This is a finding that builds on the works of Constructivist Theory practitioners such as Dewey, Piaget and Vygotsky (Anderson, et al., 1997; Pardjono, 2016) who each contributed to the relationship between knowledge and skill via the study of cognitive skill acquisition. This study adds to this in terms of knowledge of technology and application of that knowledge in the K-12 classroom. Further, this adds an important component that is important to be aware of for both preservice and in-service teachers—if they have the knowledge, they will be more likely to implement the knowledge. This study adds to the literature by demonstrating that knowledge plays a key role in appropriate technology implementation. The Role of COVID-19 Most teachers reported that because of COVID-19 they use technology completely differently than normal. The entire school district was online for an entire year. There was the immediate transition from March 13, 2020 until the end of the 2019-2020 school year where students were not required to attend synchronous online classes, all work could be completed asynchronously. Then, for the 2020-2021 school year the school district released Distance Learning 2.0 which required all students to attend synchronous classes every day of the week. This required all teachers to use Zoom, Google Meet, or Microsoft Teams to meet with their students every day and use online platforms like Google Classroom and the Google Suite to disseminate assignments. All students had access to a laptop and Internet (whether personal or 96 96 district provided). Namely, this was the most technology that has been used to date. Even so, there were still demographic group differences in knowledge and application of technology. Implications for Practice While further research is needed, because the demographics of the participants in this mirror the state demographics (EdSource, 2020), larger generalizations may be made from the findings in this study to other teachers in California. This is the first study to quantify teacher TPACK knowledge as well as quantify appropriateness of technology implementation with respect to the 2017 ISTE Standards for Educators. The findings in this study revealed several important implications for practice including: the need to nationally adopt the 2017 ISTE Standards for Educators, provide training to in-service teachers, and realize that technology is rapidly evolving, and it will constantly require investments of time and money to effectively support students. Improved Technology Does Not Mean More Technology Technology has the potential to improve student learning and develop higher order thinking skills (DiGregorio & Sobel-Lojeski, 2010), but to do this, teachers need more than just access to hardware and software in order to implement technology effectively. Teachers need knowledge of how and when to integrate specific technologies to better position students’ opportunities to engage with the content in a more meaningful way. Because of the lack of understanding of the educational paradigm of technology many teachers confuse what technology is, the purpose of technology and when to appropriately implement it in the classroom to leverage it for higher levels of learning. The intention of this study is not to advocate for the use of more technology, rather it is to advocate for intentional and appropriate 97 97 use of technology. While implementing technology in the curriculum, technology should be intentionally chosen using Tyler’s (1949) four questions: 1) What educational purposes should the school seek to attain? 2) What educational experience can be provided that are to likely attain these purposes? 3) How can these educational experiences be effectively organized? 4) How can we determine whether these purposes are being attained? These questions help intentionally select the technology to implement by thinking about the purpose for choosing the specific technology in order to assist students in gaining deeper understandings of the content. National Adoption of Technology Standards Many teachers rely on technology as a skill to be learned rather than a tool to leverage knowledge. It is important that this misconception is made clear through further education and the national adoption of the 2017 ISTE Standards for Educators. The standards provide a baseline language and benchmark for understanding what technology is and the purpose for using technological tools. Currently, the standards and iterations of the standards are randomly adopted across the country. It is time to give technology the same consideration as core subjects that English, mathematics and science receive and push for the consistent and effective implementation of the standards in classrooms across the country. In order to complete globally, students need to be best prepared to do so. The 2017 ISTE Standards for Educators are the best available standards— these standards were developed with the intention that they would need to be updated within five to ten years in order to keep with the technological advances and capabilities. In-Service Teachers Need Help 98 98 Much of the research on technology, TPACK and TPTI are centered on pre-service teachers. However, as noted in this study about half of teachers received their credentials before technology was added as a third paradigm (Mishra & Koehler, 2006). The question remains for in-service teachers, who is responsible for keeping teachers up to date with technology and best practices? Is it the responsibility of the employer or is the responsibility of the individual? This could start a debate about who is responsible, but the more pertinent question is how to ensure that teachers are “highly qualified”? One recommendation to create a common starting place would be something like an ISTE Certification. The ISTE Certification entails rigorous training of appropriate use of technology. This is not out of the norm for credentialed teachers—in order to be credentialed in the state of California teachers must have passed the CSET (Content) and a preservice program (Pedagogy), but there is no official certification yet to determine teacher competency in implementing technology. An ISTE Certification fills that gap and is aligned with the current standards. The researcher recognizes this is just a temporary solution as technology is constantly and rapidly changing and that what is current now could be outdated in two years, five years or ten years from now, but nonetheless something must be done to ensure that teachers are highly qualified to implement technology in their curricula. Furthermore, professional learning opportunities need to focus on the mindset behind technology in helping teachers understand current technology capabilities and how they align with higher order thinking. Because much of the current professional learning opportunities and training are directed on how to use specific hardware and/or software, and not on the conceptual understanding of its purpose (Mishra & Koehler, 2006, p. 1018). Shifting the focus of professional learning to understanding the conceptual capabilities of various technologies such as when to use repetition versus research versus movies, analytics, etc. will last longer than the fad 99 99 of the current device or software, and thus be more effective in long-term technological decision making. Never Ending Cycle Society is amidst the beginnings of the evolution of technology. As advances are made, hardware, software and strategies will need to be replaced and updated. Students, parents, teachers, administrators, politicians need to accept that technology is not a one-time investment, they are continuous and costly, but necessary investments. Devices, programs and training will need to occur every three to five years to remain current with capability in order to best position our students to be successful in college and their careers. Money will need to be earmarked specifically for technology improvement and maintenance. Limitations of the Study One of the largest limitations of this study is the effect of COVID-19. At the time of this study all teachers in this study are required to teach remotely using Zoom, Google Meet or Microsoft Teams in conjunction with Google Classroom. Therefore, this is the most technology that teachers in the district have used technology in their instruction. Furthermore, this is the most support and training teachers have received with regards to technology usage. This may have skewed the results in showing higher knowledge or implementation than would be expected in a normal classroom setting. Another limitation that may influence the results of this study is who chooses to respond to the survey. Teachers are already experiencing cognitive overload with respect to the pandemic and transition to online teaching, as a result some teachers may choose not to participate due to fatigue—this may skew the results towards teachers who already implement technology in more sophisticated ways. 100 100 Furthermore, the survey was quite lengthy and took approximately fifteen minutes to complete. The quality of answers or lack of complete responses may have been a result of survey fatigue. Finally, the survey did not define technology. Therefore, each time technology was used in the survey each respondent may have been interpreting its meaning differently. Additionally, when asking about the familiarity of 2017 ISTE Standards for Educators, some respondents may have been familiar with earlier iterations of the ISTE standards and, may have answered that they were not familiar with the latest or mistook the 2017 for the 2007 ISTE Standards. Recommendations for future studies Technology is just at the genesis of its capabilities. Technological skills will continue to be required to be successful in the 21st century and as such accountability to quality technological practices in the classrooms will continue to gain more importance. While there is some research, at this point there is a lot of segmented research and much of it is outdated as access and capability evolves and advances. There is still so much to learn about increasing effective technology practices in K-12 classrooms. First, while knowledge was proven to be a good predictor of implementation, it may be prudent to include motivation with knowledge to explore how motivation plays a role in technology implementation. Teachers may have knowledge, but that does equate to action. It would be important to explore how much motivation and TPACK knowledge together can be used to predict technology implementation. Secondly, studies should repeat this study protocol to determine generalizability. While there are many indicators such as matching demographics to the states’ demographics of teachers, there are still other factors such as potentially the role administration and policies 101 101 played in ensuring equity in technology implementation across socioeconomic status. Therefore, further research duplicating this study would add further credibility to its findings. Finally, in order to validate the need for the wide scale adoption of 2017 ISTE Standards for Educators, which currently are selectively implemented in areas across the country, it would be important to compare student performance in areas where the standards have officially been adopted versus areas where the standards have not been adopted. This will show the impact awareness and support of standards have on technology implementation and students’ performance. Conclusion The goal of this study was to explore teacher technology knowledge and implementation of technology. This study found that overall, most teachers are unfamiliar with the 2017 ISTE Standards for Educators that provide a benchmark for technology implementation in K-12 classrooms. This study also found that there is a lot of room for improvement when it comes to implementing technology appropriately. Specifically, teachers with sixteen years or more experience, White/Caucasian teachers, lower elementary (K-3) and physical education teachers have the most room for improvement in terms of appropriate technology implementation. There was an inverse relationship between teachers’ years of experience with technology knowledge and teaching and learning knowledge. This relationship also applied to technology implementation. In fact, in general teachers’ knowledge predicted technology implementation. The main source of work after college will be in STEM related fields. 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Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational psychology review, 29(1), 119-140. Warschauer, M., Knobel, M., & Stone, L. (2004). Technology and Equity in Schooling: Deconstructing the Digital Divide. Educational Policy, 18(4), 562–588. https://doi.org/10.1177/0895904804266469 Wayne, A., Zucker, A., & Powell, T. (2002). So what about the ‘digital divide’ in K12 schools? Educational technology and equity in U.S. K-12 schools. Paper presented at the Telecommunications Policy Research Conference, Arlington, VA. 116 116 Zilversmit, A. (1993). Changing schools: progressive education theory and practice, 1930-1960. University of Chicago Press. https://doi-org.libproxy2.usc.edu/info:doi/ 117 117 Appendix A: Consent Form 118 118 Appendix B: Demographics (Self-Created) 1. What school do you currently teach at? (Dropdown of 36 schools) 2. What grade(s) do you teach? ● K-2 (stepwise skip next question) ● 3-6 (stepwise skip next question) ● 7-8 ● 9-12 3. What subject do you teach? (Check all that apply) ● Mathematics ● English ● Science ● History ● Physical Education ● Foreign Language ● AVID ● Band/Performing Arts ● Other Elective 4. What type of class do you primarily teach? ● General Education ● Honors/Advanced Placement/GATE ● Special Day Class 5. How many years of teaching have you completed? 6. What gender do you identify with? ● Male ● Female ● Other: Fill in 7. What is your race? ● Asian ● Pacfic Islander ● Black ● Hispanic ● White/Caucasian ● Two or More Races / Other 8. What is your education level? 119 119 Appendix C: Prior Knowledge (Self-Created) 9. To what extent are you familiar with the 2017 ISTE? 10. To what extent do you use the 2017 ISTE when planning for instruction? 120 120 Appendix D: Knowledge - TPACK Survey (7 subscales .75-.92) or (4 subscales .84-.92) Strongly Disagree, Disagree, Neither Agree/Disagree, Agree, Strongly Agree (TK) 11. I know how to solve my own technical problems. 12. I can learn technology easily. 13. I keep up with important new technologies. 14. I frequently play around with the technology. 15. I know about a lot of different technologies. 16. I have the technical skills I need to use technology. 17. I have had sufficient opportunities to work with different technologies. (CK) 18. I have sufficient knowledge about (SUBJECT) 19. I can use a (SUBJECT) way of thinking 20. I have various ways and strategies of developing my understanding of (SUBJECT) (PK) 21. I know how to assess student performance in a classroom. 22. I can adapt my teaching based upon what students currently understand or do not understand. 23. I can adapt my teaching style to different learners. 24. I can assess student learning in multiple ways. 25. I can use a wide range of teaching approaches in a classroom setting. 26. I am familiar with common student understandings and misconceptions. 27. I know how to organize and maintain classroom management. (PCK) 28. I can select effective teaching approaches to guide student thinking and learning in (SUBJECT). (TCK) 29. I know about technologies that I can use for understanding (SUBJECT). (TPK) 30. I can choose technologies that enhance the teaching approaches for a lesson. 31. I can choose technologies that enhance students’ learning for a lesson. 32. My teacher education program has caused me to think more deeply about how technology could influence the teaching approaches I use in my classroom. 33. I am thinking critically about how to use technology in my classroom. 34. I can adapt the use of the technologies that I am learning about to different teaching activities. (TPACK) 35. I can teach lessons that appropriately combine (SUBJECT), technologies, and teaching approaches. 36. I can select technologies to use in my classroom that enhance what I teach, how I teach and what students learn. 37. I can use strategies that combine content, technologies, and teaching approaches that I learned about in my coursework in my classroom. 38. I can provide leadership in helping others to coordinate the use of content, technologies, and teaching approaches at my school and/or district. 121 121 39. I can choose technologies that enhance the content for a lesson. 122 122 Appendix E: Skills - TPTI (7 subscales .88-.96) Scale: Never, Rarely, Sometimes, Frequently, Usually Always, N/A Thinking across the courses you took in your teacher preparation program, how often did you… Learner (9 Questions .88) 40. Collaborate and co-learn with students to diagnose technology issues? 41. Model for peers the identification of new digital resources and tools for learning? 42. Establish a learning culture that promotes critical examination of online resources? 43. Set professional learning goals to explore pedagogical approaches made possible by technology? 44. Use technology to implement a variety of summative assessments that provide timely feedback to students? 45. Pursue professional interests by creating global learning networks? 46. Mentor students in safe practices with digital tools? 47. Use technology to design a variety of formative assessments that accommodate learner needs? 48. Use collaborative tools to expand students’ authentic, real-world learning experiences by engaging virtually with local experts? Leader (10 Questions) 49. Create experiences for learners to make positive, socially responsible contributions online? 50. Use assessment data to communicate with students to build student self-direction? 51. Use technology to create learning experiences that foster independent learning? 52. Promote secure management of personal data? 53. Model for peers the adoption of new digital resources and tools for learning? 54. Stay current with research that supports improved student learning outcomes? 55. Use technology to design a variety of formative assessments that provide timely feedback to students? 56. Use collaborative tools to expand students’ authentic, real-world learning experiences by engaging virtually with global students? 57. Collaborate and co-learn with students to discover new digital resources? 58. Use technology to create learning experiences that accommodate learner differences and needs? Citizen (14 Questions) 59. Create experiences for learners to exhibit empathetic behavior online? 60. Interact with peers as co-collaborators in student learning? 61. Advocate for equitable access to educational technology to meet the diverse needs of all students? 62. Collaborate and co-learn with students to troubleshoot technology issues? 63. Model for peers the exploration of new digital resources and tools for learning? 64. Foster a culture where students take ownership of their learning goals and outcomes in independent settings? 65. Promot management of personal digital identity? 66. Use technology to implement a variety of formative assessments that inform instruction? 67. Interact with parents as co-collaborators in student learning? 68. Pursue professional interest by actively participating in local learning networks? 123 123 69. Use technology to design a variety of summative assessments that accommodate learner needs? 70. Mentor students in legal practices with digital tools? 71. USe collaborative tools to expand students’ authentic, real-world learning experiences by engaging virtually with local teams? 72. Model for peers the evaluation of new digital resources and tools for learning? Collaborator (15 Questions) 73. Use technology to personalize learning experiences that accommodate learner differences and needs? 74. Use assessment data to communicate with education stakeholders to build student self- direction? 75. Protect student data privacy? 76. Accelerate a shared vision for empowered learning with technology by engaging with education stakeholders? 77. Use technology to implement a variety of summative assessments that accommodate learner needs? 78. Create experiences for learners to build relationships and community online? 79. Mentor students in ethical practices with digital tools? 80. Use technology to adapt learning experiences that foster independent learning? 81. Stay current with interdisciplinary research focused on how to create new, improved and equitable learning environments for 21st century learners? 82. Explore instructional design principles to create innovative digital learning environments that engage and support learning? 83. Use collaborative tools to expand students’ authentic, real-world learning experiences by engaging virtually with global experts? 84. Use technology to design a variety of formative assessments that inform instruction? 85. Use technology to design a variety of summative assessments that provided timely feedback to students? 86. Reflect on the effectiveness of professional learning goals related to technology? 87. Advocate for equitable access to digital content to meet the diverse needs of all students? Designer (10 Questions) 88. Use technology to personalize learning experiences that foster independent learning? 89. Model creativity and creative expression to communicate ideas, knowledge, or connections? 90. Collaborate and co-learn with students to use new digital resources? 91. Model for peers the curation of new digital resources and tools for learning? 92. Use technology to implement a variety of summative assessments that inform instruction? 93. Apply instructional design principles to create innovative digital learning environments that engage and support learning? 94. Demonstrate cultural competency when communicating with education stakeholders? 95. Use assessment data to communicate with parents to build student self-direction? 96. Use collaborative tools to expand students’ authentic, real-world learning practices by engaging virtually with local students? 97. Establish a learning culture that fosters digital literacy? Facilitator (8 Questions) 124 124 98. Use technology to implement a variety of formative assessments that accommodate learner needs? 99. Foster a culture where students take ownership of their learning goals and outcomes in group settings? 100. Model secure management of personal data? 101. Design authentic learning activities that align with content area standards? 102. Nurture creativity and creative expression to communicate ideas, knowledge or connections? 103. Use technology to adapt learning experiences that accommodate learner differences and needs? 104. Use technology to implement a variety of formative assessments that provide timely feedback to students? 105. Manage the use of student learning strategies in digital platforms, virtual environments, hands-on makerspaces or in the field? Analyst (16 Questions) 106. Set professional learning goals to apply pedagogical approaches made possible by technology? 107. Use technology to design a variety of summative assessments that inform instruction? 108. Dedicate planning time to collaborate with peers to create authentic learning experiences that leverage technology? 109. Model management of personal digital identity? 110. Create learning opportunities that challenge students to use computational thinking to solve problems? 111. Advocate for equitable access to learning opportunities to meet the diverse needs of all students? 112. Manage the use of technology in digital platforms, virtual environments, hands-on makerspaces or in the field? 113. Create learning opportunities that challenge students to use a design process to solve problems? 114. Use collaborative tools to expand students’ authentic, real-world learning experiences by engaging virtually with global teams? 115. Pursue professional interests by actively participating in global learning networks? 116. Establish a learning culture that fosters media fluency? 117. Interact with students as co-collaborators in student learning? 118. Use assessment data to guide progress? 119. Design authentic learning activities that use digital tools and resources to maximize active, deep learning? 120. Shape a shared vision for empowered learning with technology by engaging with education stakeholders? 125 125 Appendix F: COVID-19 (Self-Created) 121. How has your use of technology been affected by COVID-19? ● Same ● A Little Different ● Somewhat Different ● Totally Different 122. Please explain 123. Please share any ideas or thoughts you had related to technology implementation in the classroom that may not have been captured by this survey.

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Asset Metadata

Creator Weinstein, Danielle Erin (author)

Core Title Appropriate technology implementation in K-12 classrooms

School Rossier School of Education

Degree Doctor of Education

Degree Program Educational Leadership

Degree Conferral Date 2022-05

Publication Date 01/25/2022

Defense Date 05/16/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag ISTE,ISTE 2017,K-12,OAI-PMH Harvest,Technology,technology implementation,TPACK,TPCK

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Chung, Ruth (committee chair), Cabrera, Kimberly (committee member), Kaplan, Sandra (committee member)

Creator Email dweinstein@ucla.edu,weinsted@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC110575739

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