University of Southern California Dissertations and Theses

Academic beliefs and behaviors in on-campus and online general education biology classes

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Academic beliefs and behaviors in on-campus and online general education biology classes

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ACADEMIC BELIEFS AND BEHAVIORS IN ON-CAMPUS AND ONLINE GENERAL
EDUCATION BIOLOGY CLASSES

by
Christopher B. Noll

A Dissertation Presented to the
FACULTY OF THE ROSSIER SCHOOL OF EDUCATION
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTORATE OF EDUCATION

December 2015

Copyright 2015 Christopher B. Noll
ii

Acknowledgements

With love and gratitude to my family for all their support and sacrifices.

Thank you to my parents, for always encouraging me. Thank you to my wife, for being
understanding and helping me make time to finish. And thank you to my daughter, who will see
more of me now that this is done!

Academic work is demanding and time consuming. Long days filled with teaching,
researching, writing, and service leave little time for anything else. I wish to express my thanks
to the members of my committee who gave their time so freely: Dr. Helena Seli, Dr. Kimberly
Hirabayashi, and Dr. Kathryn Dickson. All of them provided invaluable guidance.

Journeys, it is said, begin with single steps. Each step of the way, I have been both
challenged and supported. Now that this particular journey is successfully finished, I look
forward to taking the first few steps in a new one!

iii

Table of Contents
Acknowledgements ............................................................................................................. ii
List of Tables ...................................................................................................................... v
List of Figures .................................................................................................................... vi
Abstract ............................................................................................................................. vii
Chapter One ........................................................................................................................ 1
Introduction ..................................................................................................................... 1
Background of the Problem ............................................................................................ 2
Statement of the Problem ................................................................................................ 4
Purpose ............................................................................................................................ 4
Research Questions ......................................................................................................... 4
Significance of the Study ................................................................................................ 5
Methodology ................................................................................................................... 5
Definition of Terms......................................................................................................... 5
Organization of Dissertation ........................................................................................... 7
Chapter Two........................................................................................................................ 8
General Education Programs .......................................................................................... 8
Inclusion of Science in Higher Education and General Education Programs ........... 9
Scientific Literacy .................................................................................................... 10
California Public University Perspectives on General Education and Science ....... 11
Current Research on Biology Education.................................................................. 12
Current Research on Biology Education in Online Courses .................................... 13
Summary. ................................................................................................................. 14
Online Education .......................................................................................................... 14
Increase in Online Education ................................................................................... 15
Benefits of Online Education ................................................................................... 18
Challenges of Online Education .............................................................................. 19
Educational Outcomes of Online Education ............................................................ 20
Summary .................................................................................................................. 24
Academic Self-Efficacy ................................................................................................ 25
Impact of Academic Self-Efficacy ........................................................................... 25
Fostering Academic Self-Efficacy in Learners ........................................................ 26
Measurement of Self-Efficacy ................................................................................. 27
Summary .................................................................................................................. 27
Help-Seeking Behaviors ............................................................................................... 28
Categories of Help-Seeking Behaviors .................................................................... 30
Factors Affecting Help-Seeking Behaviors ............................................................. 33
Measurement of Help-Seeking Behavior ................................................................. 35
Barriers to Academic Help-Seeking Behaviors ....................................................... 36
Summary. ................................................................................................................. 36
Interest........................................................................................................................... 37
Development of Individual Interest ......................................................................... 37
Impact of Individual Interest on Learning ............................................................... 38
iv

Measurement of Individual Interest ......................................................................... 39
Summary. ................................................................................................................. 39
Conclusion .................................................................................................................... 40
Chapter Three.................................................................................................................... 41
Research Questions ....................................................................................................... 41
Research Design............................................................................................................ 41
Population and Sample ................................................................................................. 42
Instrumentation ............................................................................................................. 44
Demographics ........................................................................................................... 44
Self-Efficacy ............................................................................................................. 44
Help seeking.............................................................................................................. 45
Individual Interest in Biology ................................................................................... 46
Performance in Course .............................................................................................. 46
Procedure and Data Collection ..................................................................................... 47
Data Analysis ................................................................................................................ 48
Chapter Four ..................................................................................................................... 50
Demographic Information ............................................................................................. 51
Intercorrelations ............................................................................................................ 52
Analysis of Results ....................................................................................................... 54
Research Question 1 ................................................................................................ 55
Research Question 2 ................................................................................................ 56
Research Question 3 ................................................................................................ 62
Sumary .......................................................................................................................... 64
Chapter Five ...................................................................................................................... 66
Discussion ..................................................................................................................... 66
Discussion of Help-Seeking Beliefs and Behaviors ................................................ 67
Discussion of Individual Interest ............................................................................. 68
Discussion of Academic Self-Efficacy .................................................................... 69
Discussion of Course Modality ................................................................................ 71
Implications................................................................................................................... 73
Recommendation for Research .................................................................................... 74
Recommendation for Practice ....................................................................................... 76
Limitations .................................................................................................................... 79
Conclusion .................................................................................................................... 80
References ......................................................................................................................... 82
Appendices
Appendix A: Demographic Questions ........................................................................ 90
Appendix B: Self-Efficacy Measure ........................................................................... 92
Appendix C: Help-Seeking Measures ......................................................................... 93
Appendix D: Interest in Biology Measure .................................................................. 94

v

List of Tables

Table 1. Means, Standard Deviations, and Correlations of Selected Variables ............... 53
Table 2: Relation (Modality & Pretest Interest) to Post-Test Interest .............................. 55
Table 3: Statistical Significance of the Relation (Modality & Pretest Interest) to
Post-Test Interest .............................................................................................................. 55

Table 4: Relation Modality to Post-Test Interest and Pretest Interest to Post-Test
Interest............................................................................................................................... 56

Table 5: Results of t-test: Modality and Help-Seeking Beliefs ........................................ 57

Table 6: Descriptive Statistics: Modality and Help-Seeking Beliefs................................ 57
Table 7: Results of t-test: Modality and Formal/Informal Help-Seeking Beliefs ............. 58
Table 8: Descriptive Statistics: Modality and Formal/Informal Help-Seeking Beliefs .... 58
Table 9: Results of t-test: Modality and Help-Seeking Behavior ..................................... 59
Table 10: Descriptive Statistics: Modality and Help-Seeking Behavior .......................... 59
Table 11: Chi-Square Test: Modality and Frequency of Seeking Help (Professor) ......... 60
Table 12: Chi-Square Test: Modality and Frequency of Seeking Help (Someone Else) . 60
Table 13: Chi-Square Test: Modality and Frequency of Seeking Help (Classmate) ........ 60
Table 14: Results of t-test: Modality and Help-Seeking Behavior (Classmate) ............... 61
Table 15: Descriptive Statistics: Modality and Help-Seeking Behavior (Classmate) ...... 62
Table 16: Relation (Modality, Pretest Interest, & Self-Efficacy) to Final Average ......... 62
Table 17: Statistical Significance of the Relation (Modality, Pretest Interest, and
Self-Efficacy) to Final Average ........................................................................................ 63

Table 18: Regression Analysis Examining Influence on Final Average of Modality,
Pretest Interest, and Self-Efficacy..................................................................................... 63

Table 19: Results of t-test: Modality and Final Average .................................................. 64
Table 20: Descriptive Statistics: Modality and Final Average ......................................... 64
vi

List of Figures

Figure 1. Sequence of Data Collection ............................................................................. 48
Figure 2: Statistical Analyses............................................................................................ 49

vii

ABSTRACT
This study examined the effect of course delivery mode on academic help-seeking beliefs and
behaviors, academic self-efficacy, and the levels of individual interest in biology of students in
an entry-level General Education biology course. This intersection of online education, science
courses, and academic success factors merits attention because the growing impact of the
expansion of online education on undergraduate success, particularly in science courses, has not
been fully studied. The specific questions guiding the study examined: whether course delivery
mode impacted individual interest in biology; whether course delivery mode impacted help-
seeking beliefs and behaviors; and whether course delivery mode, individual interest, and
academic self-efficacy predicted academic performance in the course. Participants (N = 183)
were enrolled in either online or on-campus sections of a biology course at a large public
university in California. Quantitative data for the study were collected through two online
surveys in a pre- and post-test design and analyzed via Chi-square, t-tests, and regression
analysis using SPSS. The findings of this study indicate that course delivery mode does not
impact individual interest in biology. The data further indicate that academic help-seeking
beliefs and behaviors vary by course delivery mode. This study also finds that while neither self-
efficacy nor individual interest predict performance in the course, course delivery mode is shown
to impact performance, although the reasons for this difference are unclear. The results of the
study will be useful to course designers and administrators of online education as they seek to
maximize the experiences of online students.
1

CHAPTER ONE
Introduction
General Education (GE) programs are found at nearly every institution of higher
education in the United States (Aloi, Gardner, & Lusher, 2003). These structured sets of
interdisciplinary, introductory courses (Glynn, Aultman, & Owens, 2005) generally include
courses in the social and physical sciences (Gaff & Wasescha, 2001). The science classes are
intended to provide students with formal instruction promoting a base-level understanding of the
scientific process as well as the knowledge needed to distinguish scientific fact from opinions
and pseudoscience (Antonellis, Buxner, Impey, Johnson, & King, 2011; DeBoer, 2000). This
general understanding of science is often labeled "scientific literacy," although the precise
definition of this term is still unsettled (Antonellis et al., 2011; DeBoer, 2000). Although the
exact meaning of scientific literacy remains elusive, scholars and policy-makers agree that it is a
worthwhile goal in higher education (DeBoer, 2000; Rockefeller, 1958). With the number of
college students continuing to increase (Altbach, Gumport, & Berdahl, 2011), the number of
students enrolled in GE science courses will rise as well.
To serve the growing number of college students, not only in the science fields, but in all
areas of higher education, more and more institutions are turning to online education (Allen &
Seaman, 2013; Lenhart, Lytle, & Cross, 2001; Picciano, Seaman, & Allen, 2010). Online
education--offering courses through the Internet--has the potential to revolutionize higher
education; some believe it has done so already (Garrison & Kanuka, 2004; Hiltz, 1997). Due to
the flexibility of online courses, online education has offered greater access to students,
including nontraditional learners who are older, have families, and live far from institutions of
higher education (Brown, 2012). With online education becoming more prevalent, we must
2

examine the impact of online courses on science education. In addition, we must consider
essential factors, such as self-efficacy, help-seeking, and individual interest in biology, to assess
how these will interact with the delivery mode of the science courses.
Background of the Problem
Nearly every educational institution in the United States that offers bachelor’s degrees
offers General Education classes (Aloi et al., 2003). The specific components of GE programs
vary depending on the particular institution; however, the objectives of the programs remain
similar. These objectives include an awareness of technology (Reed, 2011), the ability to
research and gather information (Glynn et al., 2005; University of California, 2014), and the
ability to think critically and solve problems (Glynn et al., 2005). The majority of GE programs
require coursework in natural or physical sciences (Gaff & Wasescha, 2001). Although colleges
and universities have made changes to course structures, these introductory science GE courses
are often offered as large lectures (Glynn et al., 2005).
Research findings (Gasiewski, Eagan, Garcia, Hurtado, & Chang, 2011; Labov et al.,
2010; National Research Council, 2009) have suggested that the current structure of large
introductory science classes discourages student engagement with material while encouraging
simple memorization of facts. Rather than promoting critical thinking about biological concepts
and conceiving of the coursework as part of an interdisciplinary field, these entry-level classes
often serve as “gatekeepers,” designed to discourage and exclude students who are unprepared or
unmotivated to struggle through the introductory courses (Gasiewski et al., 2011; Labov et al.,
2010).
Concerns have also been raised about the rigor and effectiveness of online courses (Allen
& Seaman, 2013; Brown, 2012). Although multiple studies (Brown, 2012; Campbell, Gibson,
3

Hall, Richards, & Callery, 2008; Lou, Bernard, & Abrami, 2006) have found that academic
outcomes are similar in online and on-campus courses, the perception remains that online
courses are less challenging and less effective than traditional on-campus classes. The number
of online courses offered is expected to continue to rise (Allen & Seaman, 2013; Thurmond &
Wambach, 2004) due to a number of factors. These factors include the cost-effectiveness of
online courses for institutions (Clark, Yates, Early, & Moulton, 2010; McGuire & Castle, 2010),
as well as the ability for schools to reach students living far from the institution (Brown, 2012).
Online courses also benefit nontraditional students, who often have families or need to work
during traditional course times (Boling, Hough, Krinsky, Saleem, & Stevens, 2012; Brown,
2012).
The importance of science education, coupled with the growth of online courses, indicate
that more research is needed on the effectiveness of online education in introductory science
courses. In particular, it is important to examine three factors that promote student success--
individual interest, self-efficacy, and help-seeking behaviors--in online science courses. All
three factors, which will be discussed in greater detail in Chapter Two, correlate with academic
achievement (Hidi & Renninger, 2006; Lawson, Banks, & Logvin, 2007; Kitsantas & Chow,
2007; Multon, Brown, & Lent, 1991). Although research has examined online science courses
(Flowers, Raynor, & White, 2013) and investigated the importance of academic self-efficacy on
biology achievement (Lawson et al., 2007), there have been no studies investigating the
intersection of science education, online courses, and academic self-efficacy, help-seeking
behaviors, and individual interest, some of the most predictive factors for student success.

4

Statement of Problem
There is a lack of research about important academic beliefs and behaviors such as self-
efficacy, individual interest and help-seeking by mode of delivery among undergraduate students
in science classes. Studies (Flowers et al., 2013; Lawson et al., 2007) have examined online
science classes and self-efficacy in biology courses, but have not looked at the differences in
academic self-efficacy, academic help-seeking and individual interest in biology as a function of
course delivery method in introductory biology courses.
Purpose of the Study
The purpose of this study was to assess and compare academic beliefs and behaviors
among undergraduate students in online and on-campus learning settings. Specifically, this
study examined the impact of course delivery mode on levels of academic self-efficacy,
academic help-seeking behaviors, and individual interest in biology. The study was conducted in
an introductory-level General Education science course at a large, comprehensive, public
university in California.
Research Questions
1. Controlling for previous levels of individual interest in biology, does the course
delivery mode impact individual interest in biology at the end of the course?
2. Is there a difference in academic help-seeking beliefs and behaviors by course
delivery method among undergraduate students enrolled in an introductory-level
biology class?
3. Controlling for the course delivery mode, do academic self-efficacy and individual
interest in biology predict the performance of undergraduate students enrolled in an
introductory-level biology class?
5

Significance of the Study
Science courses are part of the General Education requirements at nearly every college
and university in the United States (Aloi et al., 2003; Gaff & Wasescha, 2001). The findings of
this study, which investigated factors known to significantly predict persistence and academic
achievement, will be useful to student affairs professionals and faculty involved with
introductory science courses. In addition, due to the increase in the number of institutions
offering online education, this study’s findings are relevant to administrators and faculty
involved with online courses.
Methodology
Since the research questions seek to compare learning and motivation in two different
settings, online and on-campus, the researcher adopted a quantitative approach. The quantitative
approach determined the existence of statistical differences and predictive relationships. Data
was gathered via surveys that included valid and reliable instruments including the Motivated
Strategies for Learning Questionnaire (MSLQ, Pintrich, Smith, Garcia, & McKeachie, 1991), the
Karabenick scale (Karabenick, 2003), and the Colorado Learning Attitudes about Science Survey
for Biology (CLASS-Bio, Semsar, Knight, Birol, & Smith, 2011) and demographic questions.
Surveys were administered online and all data was analyzed in SPSS. The specific statistical
tests used will be further described in Chapter Three.
Definition of Terms
General Education
A program of courses designed to provide undergraduate students with a broad
background in mathematics, humanities, writing, sciences, and the arts (Gaff & Wasescha,
2001).
6

Help-Seeking
“An achievement behavior involving the search for and employment of a strategy to
obtain success” (Ames & Lau, 1982, p. 414).
Formal help-seeking. Seeking assistance from instructors or teaching assistants in order
to improve performance in a course (Karabenick & Knapp, 1991).
Informal help-seeking. Seeking assistance from peers or others not associated with the
institution in order to improve performance in a course (Karabenick & Knapp, 1991).
Interest
Enjoying and choosing to pursue involvement with a particular activity (Schraw &
Lehman, 2001).
Individual interest. Interest in a subject that is generated internally, tends to be long-
lasting, and has been tied to increased processing and learning (Hidi & Renninger, 2006).
Situational interest. Interest in a subject that is generated by factors external to the
learner (Ainley, Hidi, & Berndorff, 2002)
Online Education
Courses where at least 80% of the content is delivered through the Internet (Allen &
Seaman, 2013).
Self-Efficacy
An individual’s sense of confidence that he or she will be able to complete a specific task
(Bandura, 1997).
STEM
The areas of science, technology, engineering and mathematics are often referred to as
STEM fields. The term can indicate academic courses or majors, as well as employment fields.
7

Organization of the Dissertation
Chapter One in this dissertation provides an introduction to the importance of General
Education science courses as well as online education. It also provides an overview of the study.
In addition, this chapter discusses the importance of the study and offers definitions of relevant
terms.
Chapter Two provides an overview of science courses as part of GE programs at colleges
and universities and provides more information about current research on biology education. It
offers a discussion of online education, including recent research and trends. This chapter also
examines and compares factors that have been found to influence achievement, such as academic
self-efficacy, academic help-seeking, and individual interest.
Chapter Three describes the methodology used in this study. This chapter discusses the
sample used, instrumentation, research design, and data collection process. Also described are
the data analysis methods as well as the strengths and weaknesses of this study.
Chapter Four includes a description of the results from the data analysis. Chapter Five
discusses of these results, the limitations of the study, and suggestions for future research and
application.

8

CHAPTER TWO
This study compares the impact of course delivery methods on an intersection of three
student academic success factors in a General Education science course at a comprehensive
public university. The purpose of this chapter is to provide an overview of the major topics and
a rationale for this study. The chapter opens with a review of General Education and science
courses in the United States. It also provides a comprehensive overview of the literature
concerning online education. This chapter provides a discussion of self-efficacy, help-seeking
beliefs and behaviors, and individual interest, three factors that promote student academic
success. Given the critical importance of effective education in science, and the growth of online
education, research to study the intersection of success factors in online science education is
clearly needed.
General Education Programs
General Education programs first entered U.S. higher education at Harvard in 1909
(Lucas, 1994). These wide-ranging programs and students’ flexibility to choose courses
represented a departure from previous degree programs. Widely criticized at their inception, GE
programs gained acceptance throughout the country as institutions recognized the benefits of
providing undergraduates with a background in multiple disciplines (Glynn et al., 2005; Lucas,
1994). These programs are now nearly universal; a recent study found that 95% of four-year
institutions have GE programs (Aloi et al., 2003).
The composition and objectives of General Education programs have changed over time.
Initially, these programs consisted of large introductory lecture courses in math, sciences, and the
humanities (Glynn et al., 2005). The goals of these programs were to expose students to subjects
beyond their major courses, which were often limited in scope and focused on skills needed for
9

subsequent employment (Lucas, 1994). Current GE programs have objectives far beyond simple
exposure; the set of GE courses may relate to a central theme, or the courses themselves may be
interdisciplinary (Glynn et al., 2005). At many schools, the General Education courses are
considered "fundamental to student success in college and in their careers" (Glynn et al., 2005, p.
152). Objectives for GE programs often include the development of problem-solving, critical
thinking, and writing skills (Aloi et al., 2003). Other institutions intend for GE courses to help
students understand perspectives other than their own and take them into consideration when
making decisions (Glynn et al., 2005).
The number and breadth of courses taken in General Education programs has increased.
A recent study noted that the typical program includes an average of 50 hours of coursework
(Gaff & Wasescha, 2001). While GE programs still include math, physical and natural science,
and humanities classes, students are often required to take fine arts and social science courses, as
well as writing courses (Gaff & Wasescha, 2001). Physical and natural science courses have a
distinctive place in the GE curriculum. Science courses taken as part of their GE requirements
by non-science majors often represent “the last formal exposure to the evidence-based reasoning
and ideas that have transformed our understanding of the natural world" (Antonellis et al., 2011,
p. 31).
Inclusion of Science in Higher Education and General Education Programs
Despite their importance, the inclusion of science in GE programs--and in higher
education overall--has been debated throughout the history of U.S. higher education. As vice
president in 1798, Thomas Jefferson encouraged the inclusion of courses in applied science at
colleges and universities (Hurd, 1998). For most of the 19th century, science courses were
approved based on the concept that the subjects strengthened the mind for more intellectual,
10

nobler pursuits (DeBoer, 2000). In the 1900s, the practical application of sciences became
valued in their own right. This perceived value increased in the 1950s, as administrators began
to see success in science as an element of America's national security (DeBoer, 2000).
Discussion and debate concerning the role of sciences in GE requirements continues to this day
(Hurd, 1998).
In the twenty-first century, science has been valued less as a security concern, and more
as an economic concern (National Research Council, 2012; White House, 2009). Majoring in
science or other fields in the areas of Science, Technology, Engineering, or Mathematics
(STEM) has been viewed as a path to lucrative, steady employment (National Association of
Colleges and Employers ((NACE), 2015). Although most students in introductory science
courses are not planning to major in the subject (Astin, 1993), a basic understanding of scientific
principles and reasoning is needed to participate in the nation’s “technically-trained workforce”
(DeBoer, 2000, p. 586). A survey of employers indicated that the abilities most valued by
employers included higher-order problem-solving skills, the ability to collect and process
information, and facility in processing quantitative data, all typically taught in GE science
courses (Aloi et al., 2003; NACE, 2014). In addition to financial concerns, concerns about
scientific literacy have moved to the forefront of the debate about the importance of GE science
courses (Hurd, 1998; National Research Council, 2012).
Scientific Literacy
Scientific literacy has typically been thought of as the possession of a general
understanding of science (DeBoer, 2000). It is unclear if this general knowledge should focus
primarily on traditional content, such as the ability to recite scientific laws and theories, or on
more applied content, where students connect science to their own lives (Hurd, 1998). Hurd
11

(1998) seems to favor the latter, noting that science education has shifted to include applications
of science to “human welfare, economic development, social progress, and quality of life” (p.
409). DeBoer (2000) echoes this perspective, stating that science teaching should include the
concepts that science has direct application to students’ lives, that science may be used by
students to critically examine the world around them, and that scientific knowledge allows
students to operate as informed citizens. Critics have countered that scientific literacy for all is
unattainable and that it is naïve to believe that all students can learn to think like scientists (Hurd,
1998). Nonetheless, few would suggest that key elements of scientific literacy, such as the
ability to gather information and make informed decisions, are not worthy goals for education
(Antonellis et al., 2011).
California Public University Perspectives on General Education and Science
The importance of General Education courses and the associated science classes is
acknowledged by the state of California, where this study is located. Both systems of public
universities in the state, the University of California (UC) system and the California State
University (CSU) system, have General Education requirements for their undergraduates and
both programs include physical and life sciences among the requirements (Reed, 2011;
University of California (UC), 2014). The UC system website states that GE requirements are
intended to provide "a broad background in all major academic disciplines" (UC, 2014, para. 1).
The CSU system notes that the GE courses are part of the undergraduate process of becoming a
"truly educated person" (Reed, 2011, Article 3.1, para. 1). The CSU website also indicates that
the system’s GE requirements include at least three courses in the Scientific Inquiry and
Quantitative Reasoning category. Students’ choices for this category include mathematics,
physical and life sciences, and at least one of the courses selected must include a laboratory
12

(Reed, 2011). Additional information provided about this category suggests that the CSU system
supports both the traditional and applied content of scientific literacy described by Hurd (1998).
The CSU website states that classes in Scientific Inquiry and Quantitative Reasoning GE
category will help students “achieve an understanding and appreciation of scientific principles
and the scientific method, as well as the potential limits of scientific endeavors and the value
systems and ethics associated with human inquiry” (Reed, 2011, Article 4, para. 6).
Current Research on Biology Education
Interest in improving STEM and biology education has led to numerous research
projects. Researchers (Gasiewski et al., 2011) have examined the "gatekeeping" function of
introductory STEM classes and suggest that the traditional large lecture format discourages
critical engagement with the material while promoting superficial rote memorization. Others
(Casem, 2006) have investigated the potential of inquiry-based instruction in introductory
biology courses. Statements from the National Research Council (2009) have led to calls for
greater interdisciplinary experiences in undergraduate college courses (Labov, Reid, &
Yamamoto, 2010; National Research Council, 2009).
The last decade has brought an increase in research on the effect of affective and
cognitive factors on learning biology. Recent research (Lawson et al., 2007) on biology
education has examined the impact of student self-efficacy and reasoning ability. Other scholars
(Hulleman & Harackiewicz, 2009) have examined individual interest, another factor that is
associated with student achievement. Interest in student beliefs about science led a team of
researchers (Adams, Perkins, Podolefsky, Dubson, Finkelstein, & Wieman, 2006) to develop a
set of assessment instruments measuring this factor in specific disciplines. (One of the
13

instruments, the Colorado Learning Attitudes about Science Survey for Biology (CLASS-Bio)
will be discussed in greater detail in Chapter Three.)
Current Research on Biology Education in Online Courses
A review of the recent literature found few articles specifically examining online biology
courses. These articles (Carbonaro, Dawber, & Arav, 2006; Schoenfeld-Tacher, McConnell, &
Graham, 2001; Somenarain, Akkaraju, & Gharbaran, 2010) all examined student performance
outcomes, primarily to determine whether students in online courses learned as much as students
in traditional face-to-face courses, with mixed results. Carbonaro et al. (2006) found that online
students’ performance on two midterms was similar to that of on-campus students, but
performance on the final was inferior. Somenarain et al. (2010) found that learning levels were
similar in online and on-campus courses. Schoenfeld-Tacher et al. (2001) found that online
students learned more than on-campus students; this finding was questioned by Jaggars and
Bailey (2010), who noted the lack of alignment between pretest and post-test scores, among
other issues. Somenarain et al. (2010) also examined student satisfaction, finding that students
experiencing both course modalities had similar levels of satisfaction. Schoenfeld-Tacher et al.
(2001) also studied classroom interactions; their data indicated that students in the on-campus
section of the course had more social interactions with each other, while students in the online
course section tended to ask fewer questions regarding content and understanding than the on-
campus students. None of the studies specifically examined the impact of success factors--such
as academic self-efficacy, academic help-seeking, and individual interest--in the context of
online biology courses, which will be a significant component of this study.

14

Summary
General Education programs are found at nearly every four-year institution in the United
States (Aloi et al., 2003). The requirements of these programs typically include science courses
(Gaff & Wasescha, 2001) which are valued for teaching students higher-order thinking and
problem-solving skills as well as providing them with a basic level of scientific literacy (Aloi et
al., 2003; Hurd, 1998). This general understanding of scientific principles is valued throughout
higher education, including within the CSU and UC systems (Antonellis et al., 2011; Redd,
2011; UC, 2014). Current research on biology education has explored changing the current
paradigm of large lecture courses in order to make courses more interdisciplinary and engaging
(Gasiewski et al., 2011; Labov et al., 2010; National Research Council, 2009). Additional
research (Adams et al., 2006; Hulleman & Harackiewicz, 2009; Lawson et al., 2007) has
examined the impact of cognitive and affective factors on learning, and studied academic
outcomes of online courses (Carbonaro, Dawber, & Arav, 2006; Schoenfeld-Tacher, McConnell,
& Graham, 2001; Somenarain, Akkaraju, & Gharbaran, 2010).
Online Education
Online education has the potential to revolutionize higher education. By changing the
fundamental nature of student-faculty interaction, providing access to higher education that is
unencumbered by geography, and allowing greater flexibility in student scheduling, online
education has disrupted traditional paradigms of higher education (Boling et al., 2012; Brown,
2012; Thurmond & Wambach, 2004). This section of the literature review will discuss the
expansion of online education, the benefits and challenges of this mode of delivery, and the
educational outcomes of online courses.

15

Increase in Online Education
Student enrollment in online courses has steadily increased over the past decade. Over
the ten years from 2002 to 2012, the number of students taking at least one class online more
than quadrupled from 1.6 million to over 7.1 million (Allen & Seaman, 2014). This represents
an average annual growth rate for online student enrollment of 16.1% (Allen & Seaman, 2014).
This rate is far greater than the 2.5% average annual growth rate for all higher education
enrollments in the same time period (Allen & Seaman, 2014).
Higher education in the United States was initially and solely a face-to-face experience.
In 1636, when the first college was established in the United States, there was no practical
alternative to instructors and students meeting in the same room (Lucas, 1994). Improvements in
technology--first television and then the Internet--have allowed educators to explore other
modalities for instruction (Thurmond & Wambach, 2004). Online education alters the traditional
face-to-face model by providing content and opportunities for interaction through the Internet
(Restauri, King, & Nelson, 2001).
Structure of online courses. Higher education courses may be offered in a variety of
formats, and may use varying levels of technology. Conceptions of technology-assisted
education have varied; one study noted that this could be as rudimentary as using overhead
projectors (Lenhart, Lytle, & Cross, 2001)! For the purposes of clarity, this dissertation will
adopt the classification scheme suggested by Allen and Seaman (2013) which categorizes
courses based on the percentage of content delivered online. Courses with more than 80% of the
content provided online are considered to be truly online classes, while those with smaller
proportions are classified as traditional, web-facilitated, or hybrid (Allen & Seaman, 2013).
16

Some face-to-face courses may include significant online components, and may be
considered hybrid classes. Allen and Seaman (2013) define traditional classes as those that do
not use the Internet to deliver information. In contrast, web-facilitated courses are those that
provide 1-29% of their content online (Allen & Seaman, 2013). These classes may use a
webpage or course management system (CMS) such as Blackboard, Moodle, or Desire2Learn to
distribute information and assignments. Courses with 30-79% online content are considered
hybrid classes (Allen & Seaman, 2013). Hybrid classes may meet less frequently than traditional
or web-facilitated classes, and typically use online discussion boards as a significant component
of the course (Allen & Seaman, 2013).
Allen and Seaman (2013) define online courses as those that provide over 80% of the
content online, and have no face-to-face meetings. Classes offered through entirely online
modalities may be characterized as synchronous or asynchronous. Synchronous online courses
have set times when all students are expected to participate in the course through virtual modes
(Stocks & Freddolino, 2000). These synchronous online courses are essentially similar to face-
to-face classes; however, the professor and students are in different locations (Jiang & Ting,
1999). In contrast, asynchronous online courses have no fixed meeting times, allowing students
to follow their own pace for learning (Hiltz, 1997). Researchers (Boling et al., 2012; Rabe-
Hemp, Woollen, & Humiston, 2009) have suggested that the self-pacing characteristics of
asynchronous courses provide greater time for reflection and critical thinking about the course
content.
Impact. The surge of interest in online courses has led to multiple changes in higher
education. These changes may be seen in several distinct areas, including institutional strategies,
17

expected growth, and increased opportunities for students (Allen & Seaman, 2013; McGuire &
Castle, 2010; Nora & Snyder, 2009; Picciano, Seaman, & Allen, 2010).
Research has suggested that colleges and universities are implementing online education
as a component of overall growth strategies (Picciano et al., 2010). During a period of tightening
budgets, institutions view online courses as a way to reach more students, more affordably
(Lenhart et al., 2001; McGuire & Castle, 2010; Nora & Snyder, 2009). Allen and Seaman’s
(2013) survey of chief academic officers found that nearly 70% of the institutions agreed that
online education was critical to their long-term strategy.
The number of online courses offered and the number of students enrolled in these
classes continue to grow. As stated earlier, the number of students enrolled in at least one online
course more than quadrupled over the ten years from 2002-2012 (Allen & Seaman, 2014). As
another point of comparison, in 2002, students taking at least one class online represented less
than 10% of all college students in the United States; by 2012, the percentage of students taking
at least one online class had grown to more than one-third of all college students (Allen &
Seaman, 2014). To take advantage of this increased demand, institutions responded with
increased online course offerings (Thurmond & Wambach, 2004). In 2002, 81% of colleges and
universities, and 90% of public institutions, offered at least one online course (Cavanaugh,
2005).
The accessibility and flexibility of online courses have increased opportunities for non-
traditional students (McGuire & Castle, 2010). Kennedy’s research indicated that online
students tend to be older than traditional face-to-face students (as cited in Nora & Snyder, 2009),
which would suggest that online students face different pressures than traditional college
students. This was echoed in a recent small study which found that the reasons students chose
18

online courses included lacking the time to attend regular classes and family responsibilities
(Brown, 2012).
Benefits of Online Education
Online education has benefitted both institutions and students in numerous ways.
Colleges and universities have increased access and economy, while students have enjoyed
greater flexibility (Brown, 2012; Lenhart et al., 2001; McGuire & Castle, 2010).
Benefits to institutions. Colleges and universities view online education as an
inexpensive way to accomplish their goals. Researchers (Capra, 2014; Clark et al., 2010;
McGuire & Castle, 2010) have noted the greater cost effectiveness of online courses. Campus
facilities are not impacted by students who participate in courses through the Internet and the
number of virtual "seats" available in a course is nearly unlimited, allowing institutions to
accommodate greater numbers of students (Lenhart et al., 2001). As higher education in the
United States has moved toward accountability and outcome tracking, online courses can
contribute to institutions’ course and degree completion rates (Nora & Snyder, 2009).
Benefits to students. Students believe that the structure of online courses allows them
greater flexibility to pursue their educational goals (McGuire & Castle, 2010; Picciano et al.,
2010). For asynchronous courses, this flexibility would include the students’ ability to schedule
coursework at their convenience (Boling et al., 2012). Students also have the option of taking
classes at an institution that is geographically distant from them (Brown, 2012). For older
students, or students with families, the ability to take courses without traveling to campus is a
tremendous advantage (Brown, 2012). Students who are more introverted in face-to-face classes
may also benefit from the ability to participate in courses through online modes (Rabe-Hemp et
al., 2009).
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Challenges of Online Education
Despite the advantages discussed in the previous section, there are several challenges to
online education. These include the perceptions of online courses, as well as operational issues.
Faculty perception of online courses. Faculty members remain unconvinced of the
efficacy and efficiency of online courses. Allen and Seaman’s (2013) annual survey of chief
academic officers of colleges and universities found that less than one third of these
administrators felt their faculty accepted the legitimacy of online courses. Others noted that
faculty and administrators believe that, in order to succeed in online courses, students must be
more disciplined and able to learn more autonomously than their on-campus peers (Allen &
Seaman, 2013; Dabbagh & Kitsantas, 2013; Lorenzo, 2011; Lou, Bernard, & Abrami, 2006;
Picciano et al., 2010; Rabe-Hemp et al., 2009).
In addition to concerns about the effectiveness of online courses, it has been suggested
that teaching these courses requires more time from faculty than traditional classes. Multiple
researchers found that online instruction required more time than traditional classes (Bradburn,
2002; Cavanaugh, 2005). Hiltz (1997) found that preparation for online lectures included over
ten hours of preparation, review, and rehearsal. Studies also indicated that the greatest increase
in time was due to communications with students through email and chat room participation
(Bradburn, 2002; Hiltz, 1997; Stocks & Freddolino, 2000). It has been suggested that the time
needed to maintain appropriate communication is directly proportional to the number of students
in the course (Cavanaugh, 2005), which raises concerns about the feasibility of nearly-unlimited
lecture sizes made possible by asynchronous online coursework. In addition to the additional
hours required, professors for online courses must shift their roles to act as facilitators and
mediators for online discussions, rather than knowledge dispensers (Boling et al., 2012; Rabe-
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Hemp et al., 2009; Thurmond & Wambach, 2004). This change of function does not happen
automatically, or easily, for many faculty members (Boling et al, 2012).
Student perception of online courses. Students view online courses in ways that are
distinctly different from face-to-face courses. Students expect that online classes will be more
flexible, allowing them to access coursework at their convenience and work at their own pace
(Boling et al., 2012; Thurmond & Wambach, 2004). In addition to the benefits of flexibility,
students generally anticipate that online courses will be easier than face-to-face courses (Brown,
2012). Other researchers (Clark & Feldon, 2005) raised concerns that multimedia presentations
found in some online courses may overload students’ working memories, which may actually
make the classes more difficult.
Educational Outcomes of Online Education
It is worth examining the specific educational outcomes of online education. These
outcomes may be considered from multiple perspectives, including academic achievement,
course participation, motivation, and engagement.
Academic achievement. Learning outcomes for online courses appear to be comparable
to outcomes in face-to-face courses, although findings have been inconsistent. Several studies
(Hiltz, 1997; Schoenfeld-Tacher, McConnell, & Graham, 2001) found that levels of content
acquisition were actually higher for online students than students in face-to-face courses. A
meta-analysis commissioned by the U.S. Department of Education (Means, Toyama, Murphy,
Bakia, & Jones, 2009) agreed, stating that “on average, students in online learning conditions
performed better than those receiving face-to-face instruction” [emphasis in the original] (p. ix).
However, an analysis (Jaggars & Bailey, 2010) of the meta-analysis raised a number of concerns
about the conclusions the original authors reached. The Jaggars and Bailey (2010) review noted
21

that the studies included in the meta-analysis included short workshops and online courses with
significant face-to-face components. When the authors looked strictly at the studies of semester-
long college courses, they found that learning outcomes tended to be equal between on-campus
and online students (Jaggars & Bailey, 2010). Similar findings were noted in multiple studies
(Brown, 2012; Campbell et al., 2008; Lou et al., 2006; Robinson & Hullinger, 2008; Somenarain
et al., 2010; Zhan & Mei, 2013) which found no significant differences in academic achievement
based on course modality. In addition, one reviewed study (Carbonaro et al., 2006) found
performance to be lower in the online section than in the on-campus section.
Researchers (Chen, Lambert, & Guidry, 2010) also found correlations between increased
use of technology and learning, noting that online students in courses which promoted
collaborative discussions through the Internet earned significantly better grades than students in
face-to-face sections of the courses (Lou et al., 2006). Recent research (Hachey, Wladis &
Conway, 2015) found that students who have successfully completed online courses tend to
succeed in subsequent online classes. Other researchers found that online students with high
levels of participation in online discussions had higher grades than other online students
(Campbell et al., 2008), again suggesting the correlation between increased use of technology
and improved learning. In contrast to this finding, Nora and Snyder (2009) noted that the
connection between technology and learning was not “overwhelmingly strong” (p 8). Along
with other researchers, they would suggest that other factors, such as course content and the skill
of the instructor, have greater effects on student learning than the course delivery mode (Nora &
Snyder, 2009; McGuire & Castle, 2010).
Course participation. Students in online courses often display lower levels of
participation than students in face-to-face courses. Studies (Fleming and Bonwell, 2002 (as cited
22

in Nora & Snyder, 2009); Mentzer, Cryan & Teclehaimanot, 2007) have noted that online
students had greater numbers of incomplete assignments than students in traditional courses.
Other researchers found higher levels of student withdrawals in online sections of courses
(Brown, 2012; Hiltz, 1997; Jaggars & Bailey, 2010; Picciano et al., 2010). In contrast, one
study, which examined an asynchronous online course for undergraduates, found that the
students they surveyed “reported significantly higher levels of in class participation” (Rabe-
Hemp et al., 2009, p. 212).
Motivation. Researchers have suggested that motivation is not affected by course
modalities (Clark & Feldon, 2005, Clark et al., 2010). They have noted the importance of other
course elements, such as instructional design, while stating, “online courses have not and will not
influence learning, motivation, or work performance” (Clark et al., 2010, p. 263). Other scholars
have questioned the potential monotony in online coursework (Capra, 2014).
Engagement. Student engagement with faculty appears to be impacted, but not
decreased, in online courses. Designing online courses to promote this sense of connection to
the faculty and to other students is one of the greatest challenges for online instructors (Boling et
al., 2012; Lorenzo, 2011).
Student-faculty contact. It has been suggested that student-to-faculty contact differs in
online and on-campus courses, but the findings have been inconsistent. The virtual nature of the
student-faculty relationship in online courses has changed the basic structure of this important
relationship. Some online students indicated that they felt disconnected from their professors
(Boling et al., 2012). Other studies noted that communication between faculty and students have
different characteristics for online students. Robinson and Hullinger (2008) found that
communication with instructors of online courses focused on grades or assignments while
23

seldom discussing careers or future goals. Nora and Snyder (2009) noted that professors
provided less explanation to online students than to face-to-face students.
Several researchers found that prompt feedback and frequent communication, whether
face-to-face or virtual, was critical to establishing an effective student-faculty relationship
(Chickering & Ehrmann, 1996; Rabe-Hemp et al., 2009; Robinson & Hullinger, 2008).
Consistent communication appears to be more important than face-to-face conversations in
establishing this rapport (Thurmond & Wambach, 2004). Professors who intentionally created
opportunities for virtual interactions with students often found their relationships with online
students stronger than their relationships with face-to-face students (Chen et al., 2010; Rabe-
Hemp et al., 2009; Robinson & Hullinger, 2008). The lack of social cues and the absence of
nonverbal communication resulted in the need for more thoughtful written communication
(Boling et al., 2012; Coppola, Hiltz, & Rotter, 2002; Rabe-Hemp et al., 2009). Some faculty felt
that their electronic communication with students was actually "more intimate, more connected"
(Coppola et al., 2002, p. 179) than face-to-face conversations. Two studies indicated that online
students actually felt more connected with instructors than face-to-face students (Hiltz, 1997;
Rabe-Hemp et al., 2009); in research by Hiltz (1997), a remarkable 71% of participants felt that
their online course provided better access to professors than face-to-face classes.
Student-student contact. In general, student-to-student interaction is decreased in online
courses. Researchers noted that online students felt disconnected from their classmates (Boling
et al., 2012). Overall, students in online courses reported less collaborative work and fewer
student-to-student interactions (Rabe-Hemp et al., 2009). This decreased contact may result in
less support and greater feelings of isolation for online students (Lou et al., 2006).

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Summary
Online education continues to grow as a component of higher education in the United
States (Allen & Seaman, 2013; Thurmond & Wambach, 2004). Utilization of the Internet in
courses may range from the simple use of webpages to distribute syllabi, to sharing links and
information throughout the semester, to asynchronous courses offered as completely online, self-
paced learning modules (Allen & Seaman, 2013; Hiltz, 1997). The growth of online education
has provided institutions less expensive ways to increase enrollment (Clark et al., 2010; McGuire
& Castle, 2010), while providing greater ease of access for nontraditional students (McGuire &
Castle, 2010). Drawbacks of online education include student and faculty perceptions of this
mode of courses, particularly educators’ concerns about the time and effectiveness of online
courses (Allen & Seaman, 2013; Cavanaugh, 2005). Despite these concerns, research has
generally shown that learning outcomes are similar in online and face-to-face courses (Campbell
et al., 2008; Lou et al., 2006; Robinson & Hullinger, 2008). Research results for course
participation and engagement of online students have not been as conclusive. Most, but not all,
studies reviewed indicated that online students participate less and withdraw more frequently
than face-to-face students (Brown, 2012; Hiltz, 1997; Nora & Snyder, 2009). Studies on student-
to-student engagement suggest that online students are not as integrated with their classmates
(Boling et al., 2012; Rabe-Hemp et al., 2009). However, research on student-faculty interaction
indicates that this important relationship may be just as strong for online students as for students
in traditional face-to-face classes (Boling et al., 2012; Chen et al., 2010; Hiltz, 1997; Rabe-Hemp
et al., 2009; Robinson & Hullinger, 2008).
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After providing this overview of the current research on STEM education and online
education, the next sections of this dissertation will review factors related to student success:
academic self-efficacy, academic help-seeking, and individual interest.
Academic Self-Efficacy
The third research question of this study explored self-efficacy, which has been described
as an individual’s confidence in his or her ability to complete a specific task (Bandura, 1986;
Bandura, 1997). For learners, this may include how confident they are that they will be able to
solve a homework problem or do well on a test in a specific subject. Self-efficacy is associated
with a specific outcome and is different from confidence, which usually signifies an overall
optimistic view of one’s abilities (Clark, 2004). Academic self-efficacy has significant
implications for education (Bandura, 1986).
Impact of Academic Self-Efficacy
Students’ level of academic self-efficacy influences their self-regulatory behaviors and
impacts their levels of academic achievement (Bandura, 1986; Lawson et al., 2007). The
relationship of self-efficacy to persistence and academic performance has been found to be
statistically significant in multiple studies (Lawson et al., 2007; Multon et al., 1991).
Behaviors. Academic self-efficacy has been found to influence learners’ behaviors in
multiple critical areas (Bandura, 2006). These include the choice of objectives to pursue, as well
as the mental effort expended in attaining these objectives, and the persistence in the face of
obstacles (Bandura, 2006; Clark, 2004; Pajares & Schunk, 2001). Self-efficacious learners
choose more challenging goals and then put forth the effort needed to accomplish them (Clark,
2004; Mann & Golubski, 2013). In addition, students with higher levels of academic self-
efficacy are more likely to persevere in the face of adversity (Multon et al., 1991). Overall, the
26

level of a learner’s academic self-efficacy is a better predictor of behavior than other predictive
characteristics, such as the learner’s general motivation for learning or locus of control (Graham
& Weiner, 1996). In online courses in particular, high levels of academic self-efficacy and other
regulatory behaviors are believed to be critical to student success (Allen & Seaman, 2013;
Dabbagh & Kitsantas, 2013; Lou et al., 2006; Picciano et al., 2010; Rabe-Hemp et al., 2009).
Academic achievement. Higher levels of self-efficacy are correlated with higher levels
of academic achievement (Kitsantas & Chow, 2007; Lawson et al., 2007; Mann & Golubski,
2013). Perhaps this finding is unsurprising, considering the effect of academic self-efficacy on
behavior, and the importance of behavior on academic achievement. In many college majors,
including the STEM fields, academic success is a product of dedication, practice, and repetition
(Mann & Golubski, 2013). The relationship of academic self-efficacy to academic outcomes has
been demonstrated in numerous studies and meta-analyses (Lawson et al., 2007; Mann &
Golubski, 2013; Pajares & Schunk, 2001). Students who succeed in challenging majors must be
able to focus on their work and apply the requisite amount of mental effort. When they face
obstacles on assignments or exams, they must continue to strive to reach solutions. There is a
direct connection from academic self-efficacy to productive behaviors, and from productive
behaviors to academic success (Kitsantas & Chow, 2007).
Fostering Academic Self-Efficacy in Learners
Given the connection of academic self-efficacy to learning and academic achievement, it
is reasonable that instructors and institutions would wish to promote students’ sense of academic
self-efficacy, particularly in early experiences (Mann & Golubski, 2013). Development of
academic self-efficacy may be encouraged in numerous ways, including previous mastery
experiences, awareness of peers’ mastery experiences, social persuasion, and physiological state
27

(Bandura, 1986: Clark, 2004; Margolis & McCabe, 2006). However, researchers (Lawson et al.,
2007; Margolis & McCabe, 2006; Pajares & Schunk, 2001) disagree on the best methods to build
academic self-efficacy in their students. Providing students with early experiences of attaining
success on intermediate goals can promote academic self-efficacy and increase persistence in
working towards more complicated goals (Margolis & McCabe, 2006; Pajares & Schunk, 2001).
Other researchers (Lawson et al., 2007) suggest challenging students earlier with more
complicated experiences in order to shock the students into increasing their efforts, leading to
later, greater academic outcomes.
Measurement of Self-Efficacy
Researchers have created multiple survey instruments to measure levels of self-efficacy.
Because self-efficacy is related to expectations of success for a specific task, instruments must be
focused rather than general (Bandura, 2006). In addition, a learner’s level of academic self-
efficacy can be assessed for its generality, its strength, and its level (Bandura, 2006). One of the
most common instruments to measure self-efficacy is the Motivated Strategies for Learning
Questionnaire (MSLQ, Pintrich et al., 1991), which has been found to have a high measure of
reliability (Artino & McCoach, 2008; Pintrich, Smith, Garcia, & McKeachie, 1993). This
instrument will be discussed in greater detail in the third chapter of this dissertation.
Summary
Self-efficacious students behave differently from students with low levels of self-efficacy
(Bandura, 1986; Lawson et al., 2007), as they set high expectations for themselves and persist
until these goals are accomplished (Mann & Golubski, 2013; Multon et al., 1991). As a
consequence of these behaviors, self-efficacious students demonstrate high levels of academic
achievement (Kitsantas & Chow, 2007). Students with high academic self-efficacy view a
28

difficult course as “a challenge to transcend rather than a threat to escape” (Mann & Golubski,
2013, p. 2). Instructors agree on the benefits of fostering academic self-efficacy in learners, but
disagree on the best methods to accomplish this (Lawson et al., 2007; Mann & Golubski, 2013;
Margolis & McCabe, 2006; Pajares & Schunk, 2001). The third research question of this study
investigated if differences in academic self-efficacy exist between students in online and face-to-
face sections of an introductory biology course.
The literature review will now shift to an examination of help-seeking beliefs and
behaviors. This will be followed by a discussion of individual interest.
Help-Seeking Behaviors
Another factor that influences students’ success in academic settings, both online and
face-to-face, is help-seeking behaviors. This concept was explored as part of the second research
question of this study. Students’ academic help-seeking behaviors can take multiple forms and
are considered an important component of self-regulation (Kitsantas & Chow, 2007). The term
help-seeking covers a range of behaviors intended to improve performance (Karabenick, 2003;
Kitsantas & Chow, 2007; Nelson-Le Gall, 1985), and developing effective academic help-
seeking behaviors is correlated with increased academic achievement (Kitsantas & Chow, 2007).
Although some researchers (Zimmerman & Pons, 1986) distinguish between help-seeking (i.e.,
asking for assistance from another person) and information-seeking (i.e., looking to books or
other media for assistance), others note that the distinction between the two behaviors “has
become blurred” (Puustinen & Rouet, 2009, p. 2).
Academic help-seeking behaviors may be considered either instrumental or executive
(Karabenick, 2003). Examples of instrumental help-seeking include asking for hints to a
solution, or seeking assistance to understand the problem-solving process being used. In
29

contrast, executive help-seeking behaviors are used to avoid work and simply attain the answer.
Instrumental help-seeking results in greater learner autonomy, while executive help-seeking
leads to learners who remain dependent on assistance (Karabenick, 2003).
Early exploration of help-seeking behavior led to the proposal of a five-step help-seeking
process for learners (Nelson-Le Gall, 1985). The five steps were described as: recognizing the
need for help, deciding to get help, identifying helpers, using strategies to obtain help, and
reacting to the results (Nelson-Le Gall, 1985). This model served as the foundation for later
researchers to investigate each step in greater detail.
The third and fourth steps of Nelson-Le Gall’s (1985) model are worth a more detailed
exploration, particularly in the context of online education. Initially, the process of identifying
potential helpers may have been rather straightforward. When the five-step model was proposed,
the most common sources of help were faculty or teaching assistants, mentors, peers, or parents,
along with printed materials and other media (Puustinen & Rouet, 2009). With the new
capabilities presented by Internet, e-mail, and online education, the choices of available helpers
expanded dramatically; seeking help from another person remotely became much more common.
In addition, the methods of contacting these helpers increased significantly, as learners moved
beyond direct contact--including face-to-face conversations and phone calls--to emails and less
personal communication, such as posting on discussion boards or websites. Researchers (Cheng
& Tsai, 2011; Karabenick & Knapp, 1991; Puustinen & Rouet, 2009) who examined these
differences in sources of help and contact methods created multiple models to categorize help-
seeking behaviors.

30

Categories of help-seeking behaviors
Scholars (Cheng & Tsai, 2011; Karabenick & Knapp, 1991; Puustinen & Rouet, 2009)
have proposed multiple categories of help-seeking behaviors. These models represent different
perspectives and are relevant in the context of different modalities of courses.
Karabenick and Knapp’s categories. Karabenick and Knapp (1991) proposed five
categories of academic help-seeking behaviors. These categories cover a range of activities, from
actively seeking help from others to altering activities and expectations. The first two categories
included formal help-seeking, seeking assistance from instructors or teaching assistants, and
informal help-seeking, asking friends and colleagues for assistance (Karabenick & Knapp, 1991).
Instrumental activities were activities that were intended to improve the learner's performance,
such as taking notes, creating study guides, or studying additional hours (Karabenick & Knapp,
1991). The final two categories involved doing less: lowering performance aspirations, such as
taking a lighter courseload; and altering goals, such as changing majors or institution
(Karabenick & Knapp, 1991).
Karabenick and Knapp’s (1991) model does not consider the method of contacting
helpers, but only differentiates between the individual’s relationship with the helpers. It also
includes a category, instrumental activities, which seems to include a remarkably large range of
behaviors. Essentially, any action which does not include another person could be considered
instrumental. Since this model was proposed in the early stages of the Internet, it does not
account for applications such as online discussion boards or other websites where users can post
questions. It is unclear how Karabenick and Knapp (1991) would classify these activities. The
last two categories, which involve changing aspirations and reducing goals, do not seem to be
31

true help-seeking behaviors, as they will not benefit a learner who is struggling with a specific
problem or in a particular course.
Puustinen and Rouet’s categories. Puustinen and Rouet (2009) examined help-seeking
behaviors by categorizing the source and connection to the helper. These authors discarded the
distinction between help-seeking and information-seeking, noting that the information available
in books, websites, and other media is initially created by a person. From this perspective,
looking up the facts in a book is essentially the same as turning to the author for assistance
(Puustinen & Rouet, 2009). Their article (Puustinen & Rouet, 2009) discussed help-seeking
from a human expert, such as a teacher or librarian, while noting that this helper gathered the
knowledge either from another human expert or through the use of media, such as books or the
Internet. The authors classified this differently from a learner seeking information directly from
a book or other form of media (Puustinen & Rouet, 2009). Although the information the learner
acquires was created by a human expert, the authors regarded it as one step further removed from
its source (Puustinen & Rouet, 2009). The authors also discussed the difference between sources
of information that are responsive to questions, and those that remain relatively static (Puustinen
& Rouet, 2009).
Puustinen and Rouet’s (2009) model seems to be more of a philosophical statement than
a true model of help-seeking behavior. The authors wish to recognize the prevalence of the
Internet and other media in help-seeking, while also noting that the media being used was
ultimately a human creation. This model, with the introduction of online content, provides a
bridge from Karabenick and Knapp (1991) to the next model of help-seeking behaviors.
Cheng and Tsai’s categories. Cheng and Tsai (2011) focused specifically on the
academic help-seeking process of online students and proposed three categories of help-seeking
32

behaviors. These categories are: information searching, formal queries, and informal queries
(Cheng & Tsai, 2011). The formal queries are similar to Karabenick and Knapp’s (1991)
category of the same name, although help is sought through an online mode (Cheng & Tsai,
2011). Informal queries also use an online method of communication, but help is sought from
peers, or by posting messages online to receive assistance from "unknown experts" (Cheng &
Tsai, 2011, p. 152). The third category of online help-seeking is information searching, which in
this case refers to relying on search engines or other nonresponsive websites for information
(Cheng & Tsai, 2011).
Cheng and Tsai (2011) were the first to specifically examine academic help-seeking
behaviors taking place in an online environment. Their model drew on both Karabenick and
Knapp’s (1991) model as well as Puustinen and Rouet’s (2009) work. Cheng and Tsai (2011)
essentially duplicated three of the five categories proposed by Karabenick and Knapp (1991),
which leads to similar concerns. They define information-seeking as any activity involving
nonresponsive websites, which is a rather broad range. Because this includes so many different
types of online resources, it may make analysis of student behavior challenging. Formal queries
involve instructors and teaching assistants, while informal queries involve direct e-mails to peers,
as well as posting questions to discussion boards or other sites where a learner may receive a
response. Cheng and Tsai’s (2011) model does not differentiate between questions directed to
specific individuals and those that are more general in nature, which may be considered a
shortcoming of the model.
This study will classify academic help-seeking behaviors as formal or informal
depending on the person from whom assistance is sought. This draws on Karabenick and
Knapp’s (1991) and Cheng and Tsai’s (2011) models of help-seeking. This study will not
33

consider instrumental activities (Karabenick & Knapp, 1991) or information searching (Cheng &
Tsai, 2011), which do not involve other individuals.
This dissertation will now examine specific aspects of academic help-seeking behavior.
This includes a discussion of factors, such as self-efficacy and course modality, that may affect
help-seeking behaviors. This section will also provide information on barriers to academic help-
seeking behaviors and measurement of help-seeking beliefs.
Factors Affecting Help-Seeking Behaviors
There are multiple factors that affect students’ help-seeking behaviors (Cheng, Liang, &
Tsai, 2013; Clegg, 2006; Dabbagh & Kitsantas, 2013). These factors may be characteristics of
the student, the instructor, the course, or the institution. Factors related to the student include
employment and finances (Clegg, 2006), levels of introversion (Rabe-Hemp et al., 2009), and
feelings of self-efficacy related to a particular task (Kitsantas & Chow, 2007). The intersection
of academic self-efficacy and academic help-seeking behaviors will be discussed in greater detail
in the next section. Instructors who are seen as caring and provide praise to students who ask
questions encourage additional help-seeking behavior from students in the process (Kitsantas &
Chow, 2007). The modality of the course has been shown to impact the methods students used
to seek help (Cheng et al., 2013; Kitsantas & Chow, 2007); the effect of course modality on help-
seeking behaviors will be discussed in greater detail in a later section. Researchers (Dabbagh &
Kitsantas, 2013) have explored the use of course-management systems to influence academic
help-seeking behaviors, and found that these systems could be used to assist students in creating
and maintaining productive behaviors. In addition, research by Clegg (2006) has suggested that
students at schools that are perceived as welcoming institutions tend to be more comfortable in
seeking assistance from school faculty and staff.
34

Interaction of academic self-efficacy and academic help-seeking behaviors.
Academic self-efficacy and academic help-seeking behaviors are closely related (Kitsantas &
Chow, 2007). Learners with high academic self-efficacy have been found to seek help more
readily (Clegg, 2006; Kitsantas & Chow, 2007). There are several interrelated reasons for this.
As stated earlier, self-efficacy is a belief about one's abilities to complete a task (Bandura, 1997;
Kitsantas & Chow, 2007). It predicts persistence in the face of obstacles as well as the
individual effort put forth (Bandura 1997; Kitsantas & Chow, 2007). When a learner with high
academic self-efficacy encounters difficulty with tasks, such as completing assignments or
understanding concepts, they persist in their efforts to complete the task. In addition, because
they believe that they should be able to complete the task, they are “less likely to interpret their
need for help due to lack of ability, and therefore tend to seek help more frequently” (Kitsantas
& Chow, 2007, p. 384). Learners with high academic self-efficacy do not interpret seeking help
as indicative of a lack of ability (Kitsantas & Chow, 2007). This is in contrast to learners with
low academic self-efficacy; these individuals are concerned that asking for help is an admission
of inability (Clegg, 2006; Kitsantas & Chow, 2007). The more public the help-seeking process
for low self-efficacious students, the higher the threats they perceive to their self-esteem, and the
less likely they are to seek assistance (Kitsantas & Chow, 2007).
Effect of course modality on academic help-seeking behaviors. Patterns of help-
seeking behaviors have been found to differ depending on the modality of the course (Cheng et
al., 2013; Dabbagh & Kitsantas, 2013; Kitsantas & Chow, 2007). As one might expect, learners
in face-to-face classes seek help from fellow students more often than students in online courses
do (Kitsantas & Chow, 2007). These face-to-face students also resist contacting instructors or
teaching assistants (Clegg, 2006; Kitsantas & Chow, 2007). Online students were found to
35

contact these formal sources more readily, possibly because the contact is electronic in nature
(Kitsantas & Chow, 2007). As discussed earlier (in the online section), e-mail communication
between students and instructors is often considered more thoughtful than face-to-face
conversations (Boling et al., 2012: Coppola et al., 2002), as it allows the learner more time to
consider questions and craft careful messages (Kitsantas & Chow, 2007; Rabe-Hemp et al.,
2009). In addition, because access to e-mail often implies access to the Internet, learners are able
to use additional resources when seeking assistance (Cheng et al., 2013).
Online communication options are beneficial to help-seekers in multiple ways,
particularly those learners with low self-efficacy or higher levels of introversion (Kitsantas &
Chow, 2007; Rabe-Hemp et al., 2009). Students are able to benefit from discussion boards, even
if their own contribution is limited. Observing the conversations on discussion boards gives
students an opportunity to gather information (Kitsantas & Chow, 2007), even if they are hesitant
to contribute their own ideas to the discussion (Kitsantas & Chow, 2007; Rabe-Hemp et al.,
2009). Students in online courses often ask for help from formal sources more readily than
students in traditional face-to-face courses, as e-mails to professors are considered less
threatening to students’ levels of self-esteem (Kitsantas & Chow, 2007).
Measurement of Help-Seeking Behavior
Researchers have created multiple survey instruments to measure attitudes and actions
regarding help-seeking behavior. Similar to self-efficacy, one of the more common scales used
to measure help-seeking is the Motivated Strategies for Learning Questionnaire (MSLQ, Pintrich
et al., 1991). The MSLQ allows measurement of formal and informal help-seeking; these scales
may be combined to a single measurement with a relatively strong measure of reliability
36

(Karabenick & Knapp, 1991; Pintrich et al., 1993). This instrument will be discussed in greater
detail in the third chapter of this dissertation.
Barriers to Academic Help-Seeking Behaviors
Research (Clegg, 2006; Kitsantas & Chow, 2007) has suggested that the majority of
students do not use support services and often hesitate to seek help from instructors. As
discussed in the previous sections, students who typically most need help are the ones who seek
it the least (Ames & Lau, 1982; Kitsantas & Chow, 2007). Students often view academic help-
seeking as an admission of failure (Kitsantas & Chow, 2007), or believe they should be able to
solve their problems on their own (Clegg, 2006).
Summary
Learners may employ various help-seeking strategies in order to improve academic
performance (Karabenick, 2003; Nelson-Le Gall, 1985). Instrumental help-seeking behaviors
lead learners to greater autonomy and increased academic achievement (Karabenick 2003;
Kitsantas & Chow, 2007). Researchers (Cheng & Tsai, 2011; Karabenick & Knapp, 1991;
Nelson-Le Gall, 1982; Puustinen & Rouet, 2009) have created multiple models to explore and to
define the academic help-seeking process. Studies (Clegg, 2006; Kitsantas & Chow, 2007) have
indicated that a variety of elements affect help-seeking behaviors, particularly the learner’s self-
efficacy and the modality of the courses (Cheng et al., 2013; Kitsantas & Chow, 2007). The
second research question of this study examined academic help-seeking beliefs and behaviors as
a function of course-delivery method. The literature review will now offer a discussion of the
concept of interest.

37

Interest
Interest, particularly as it relates to education, is an area of concern for multiple
researchers (Hidi, 1990; Hidi & Renninger, 2006; Mitchell, 1993), and is explored in the first
research question in this study. Engaging an audience and sustaining students’ attention is an
important component of teaching, while encouraging life-long learning is often the goal of
education (Durik & Harackiewicz, 2007). Scholars (Hidi, 1990) have categorized interest as
being either situational or individual. Situational interest is transitory and is related to focusing a
student’s attention, often through novelty or incongruity, on a particular topic (Ainley et al.,
2002). Interest in a subject tends to produce spontaneous attention to a topic (Hidi, 1990).
Individual interest, also known as personal interest, is longer-lasting and unrelated to a specific
situation; it is more closely connected with sustained engagement with content and long-term
learning than is situational learning (Hidi & Renninger, 2006).
Development of Individual Interest
Researchers (Hidi & Renninger, 2006; Hulleman & Harackiewicz, 2009; Mitchell, 1993)
examined the development of individual interest and suggested that situational interest may lead
to individual interest. In a sense, situational interest may be considered a “gateway” to
individual interest. Situational interest, which depends on factors external to the learner, may be
encouraged through a number of strategies (Durik & Harackiewicz, 2007; Schraw & Lehman,
2001). These strategies include the selective use of novel or surprising information (Hidi, 1990;
Hidi & Renninger, 2006). Emotional connection with the material often increases learners’
situational interest (Schraw & Lehman, 2001). Connecting assignments and discussions to
students’ experiences has also been found to increase interest (Durik & Harackiewicz, 2007; Hidi
& Renninger, 2006).
38

Repeated instances of situational interest may inspire a learner’s individual interest
(Durik & Harackiewicz, 2007; Hidi & Renninger, 2006). Hidi and Renninger (2006) proposed a
four-step model to explain the process. The steps include: triggered situational interest,
maintained situational interest, emerging individual interest, and well-developed individual
interest (Hidi & Renninger, 2006). As an individual progresses through these steps, his or her
motivation changes from extrinsic to intrinsic, and the individual will become more engaged
with the material. In addition to increased engagement, learners with individual interest in a
topic develop the ability to regulate their learning (Hidi, 1990).
Impact of Individual Interest on Learning
Research (Durik & Harackiewicz, 2007; Schraw & Lehman, 2001) has found that interest
impacts learning, although situational interest and individual interest appear to operate in
different ways. As discussed earlier, situational interest may be generated by a well-written
textbook, an engaging professor, or an exciting demonstration (Ainley et al., 2002; Durik &
Harackiewicz, 2007). Individual interest develops more gradually and persists, even in the
absence of external stimuli (Durik & Harackiewicz, 2007; Hidi 1990).
Interest has been considered one of the “most important factors in learning and
development” (Krapp, 1999, p. 23). Individual interest, and the concomitant continued
fascination with a topic, leads to multiple behaviors that are associated with positive academic
outcomes. Research (summarized in Hidi, 1990) noted that individual interest has “a profound
effect on cognitive functioning and performance” (p. 554). Students interested in a topic are
more engaged with the content and create connections between their existing knowledge and
new material (Sansone, Fraughton, Zachary, Butner, & Heiner, 2011). Individual interest also
produces effects similar to self-efficacy. It increases motivation and self-regulation (Hidi, 1990).
39

Studies have also found that students with individual interest in a topic devote more time to their
studies (Sansone et al., 2011) and persist in the face of obstacles (Durik & Harackiewicz, 2007;
Hidi, 1990).
Measurement of Individual Interest
Instruments to measure individual interest include a number of questionnaires, such as
the Strong-Campbell Interest Inventory (Campbell, 1987) and the Study Interest Questionnaire
(Schiefele, 2009). These surveys measure interest in particular vocations and university subjects,
respectively. A more recent survey, the Colorado Learning Attitudes about Science Survey for
Biology (CLASS-Bio), includes a set of questions that are related to an individual’s enjoyment
and interest in biology (Semsar et al., 2011). The scoring of the CLASS-Bio assessment is based
on comparing the answers provided by a respondent to the answers provided by an expert in
biology. This is intended to determine if the respondent is more expert-like or more novice-like
in their thinking about the material (Semsar et al., 2011). Interestingly, when examining the
results of the complete CLASS assessment, multiple researchers have found that students in
entry-level science classes often become more novice-like in their thinking at the end of the
course than they were at the beginning of the course (Adams, Wieman, & Perkins, 2008; Redish,
Saul, & Steinberg, 1998).
Summary
Interest is an important topic in education. It may be situational--generated by a specific
circumstance--or it may be individual--existing in a learner regardless of external stimuli (Hidi,
1990). Maintained, or repeated, instances of situational interest often generate individual interest
(Durik & Harackiewicz, 2007; Hidi & Renninger, 2006). This type of long-lasting engagement
with a topic results in beliefs and behaviors that benefit learning (Hidi, 1990). These include
40

greater motivation, self-regulated learning, and increased persistence (Durik & Harackiewicz,
2007; Hidi, 1990). The first research question of this study examined if interest in biology
developed differently between students in online and face-to-face sections of an introductory
biology course.
Conclusion
The importance of science education to promote a basic level of scientific literacy and the
increased number of online courses has led to a need for expanded research on the intersection of
these topics. Research has explored academic outcomes in online classes, and found few
significant differences with outcomes in traditional face-to-face classes. Studies have also
looked at the importance of student success factors, such as self-efficacy, help-seeking, and
interest. Among other positive outcomes, academic self-efficacy and interest have been found to
increase motivation and persistence among learners, while effective academic help-seeking
behaviors increase learning. However, there remains a need to examine the impact of course
modality on these student success factors. This current study will examine academic self-
efficacy, academic help-seeking beliefs and behaviors, as well as interest, to determine whether
or not there are differences between the online and face-to-face sections of an introductory
biology course.

41

CHAPTER THREE
This chapter provides details on the research methodology employed in this study.
Specifically, it describes the population, the sampling procedure, and the instrumentation used.
It also provides a discussion of the procedures used to collect and analyze the data.
Research Questions
The following research questions guided this study:
1. Controlling for previous levels of individual interest in biology, does the course
delivery mode impact individual interest in biology at the end of the course?
2. Is there a difference in academic help-seeking beliefs and behaviors by course
delivery method among undergraduate students enrolled in an introductory-level
biology class?
3. Controlling for the course delivery mode, do academic self-efficacy and individual
interest in biology predict the performance of undergraduate students enrolled in an
introductory-level biology class?
Research Design
This study investigated a number of independent and dependent variables. The
independent variables were: the course delivery method, the students’ individual interest in
biology at the beginning of the course, and the students’ academic self-efficacy. The dependent
variables were: students’ help-seeking beliefs and behaviors, the students’ individual interest in
biology after completing the class, and the students’ final averages in the course. A quantitative
design was used to examine differences between course-related beliefs and behaviors measured
in online and on-campus settings. The study used correlational data gathered through self-
42

reported surveys. The data were analyzed for statistical significance. The study was non-
experimental.
Population and Sample
The population for the data collection was composed of students in an introductory
biology course at a large public university in California. The university, which will be referred
to as Eureka State University (ESU), is part of the twenty-three-campus California State
University system. There are more than 1500 full- and part-time faculty members at ESU;
current student enrollment is more than 30,000 students.
The introductory biology course, which will be referred to as Biology 100 (BIO 100), is a
General Education class at ESU. It is intended to teach basic biological principles, as well as the
skills needed to research and analyze biological problems and make decisions based on this
analysis. BIO 100 does not count for major credit for biology majors, so nearly all of the
students in the course major in other subjects. The BIO 100 course can be used to satisfy part of
students’ Scientific Inquiry and Quantitative Reasoning GE requirements. The course is one of
students’ few choices to complete the Life Science General Education requirement; many
students take it strictly for this purpose. The majority of the students in the course are in their
first or second year at ESU.
The university offers approximately twenty sections of BIO 100 each semester, and the
classes are often filled to capacity. The majority of these sections are taught on-campus in
traditional face-to-face format, but several sections are offered online in an asynchronous format.
The online courses are fairly well-structured, and have been developed in consultation with
learning specialists. Each online course is composed of three modules, with each module
43

covering several interrelated topics. There are deadlines for assignments, but students are
otherwise given autonomy in setting their own learning schedule.
Seven BIO 100 instructors, two of online sections and five of on-campus sections, agreed
to allow their students to participate in the study. Although all of the sections of BIO 100
involved in the study had similar syllabi and identical learning goals, each section was taught by
a different instructor. Enrollment in each section ranged from 84 to 100. The two online
sections had a total student enrollment of 174, while the total enrollment in the five on-campus
sections was 472.
Students participating in the study were asked to complete two surveys, one at the
beginning of the semester, and one at the end of the semester. The pretest survey had a total of
461 respondents, while the post-test survey had a total of 434 respondents. The pretest/post-test
design of the study required that responses on both surveys be matched, which reduced the
number of valid participants. In addition, the CLASS-Bio instrument included a question
designed to determine which students were actively reading and responding to the questions
(Semsar et al., 2011), which further reduced the number of useable responses. Lastly, one
student in the remaining group of participants was assigned a grade due to an academic integrity
issue, rather than his or her work in the course. When this student’s information was removed,
the number of study participants remaining was 183, which served as the sample for this study.
Of these participants, 135 were enrolled in on-campus sections of the course, representing 28.6%
of the students in the five on-campus sections. Forty-eight participating students were enrolled
in online sections of BIO 100, representing 27.5% of the students in the two online sections.

44

Instrumentation
The next section of the dissertation describes the survey questions that were used in this
study. Appendix A includes a copy of the demographic questions and the other survey
instruments.
Demographics
The survey asked a series of demographic questions. The data collected included
personal characteristics, such as age, gender, ethnicity, relationship status and highest level of
parents’ education. Additional questions probed educational factors. These factors included
current major, intended major, year in school, and number of online courses taken previously.
The students also indicated whether they are enrolled in the online or face-to-face section of the
class, why they chose that particular course modality, and information regarding their access to
computers.
Self-Efficacy
Academic self-efficacy was measured using the Motivated Strategies for Learning
Questionnaire (MSLQ, Pintrich et al., 1991). The section of the MSLQ that measures self-
efficacy has eight items and has a Cronbach’s alpha of 0.93 (Pintrich et al., 1991). In the current
study, the Cronbach’s alpha for these questions was found to be 0.92. Two sample items from
this section of the MSLQ (Pintrich et al., 1991) are the following:
 I’m certain I can understand the most difficult course material presented in the
readings for this course.
 I expect to do well in this class.

45

Help-Seeking
Help-seeking beliefs and behaviors were measured through a number of assessments.
Students’ help-seeking beliefs were assessed at the beginning of the class through a set of
questions from the MSLQ (Pintrich et al., 1991) and the Karabenick scale (Karabenick, 2003)
while the frequency and target of academic help-seeking behaviors were measured through
survey questions at the end of the class.
Beliefs. Help-seeking beliefs were measured using the MSLQ (Pintrich et al., 1991).
The section of the MSLQ that measures help-seeking has four items and has a Cronbach’s alpha
of 0.52 (Pintrich et al., 1991). In the current study, the Cronbach’s alpha for this set of questions
was found to be 0.48; when the reverse-coded question was removed from the calculation due to
possible rating confusion, the Cronbach’s alpha for these questions was found to be 0.59. Two
sample items from this section of the MSLQ (Pintrich et al., 1991) are the following:
 I ask the instructor to clarify concepts I don’t understand well.
 When I can’t understand the material in this course, I ask another student in this class
for help.
Further data on help-seeking beliefs were measured using the Karabenick scale
(Karabenick, 2003), which examined the concepts of formal and informal help-seeking. This
scale has three questions and a Cronbach’s alpha of 0.66 (Karabenick, 2003). In the current
study, the Cronbach’s alpha for these questions was found to be 0.68. One sample item from this
scale (Karabenick, 2003) is the following:
 If I were to seek help in this class I would ask the teacher rather than another
student.
46

Behaviors. The frequency of academic help-seeking behaviors was measured through an
online survey that was developed by the researcher in collaboration with other doctoral students.
This survey also explored formal and informal help-seeking by questioning whom the students
contacted for assistance. The survey was administered at the end of the course.
Individual Interest in Biology
Individual interest in biology was measured using the Colorado Learning Attitudes about
Science Scale for Biology (CLASS-Bio, Semsar et al., 2011). The CLASS-Bio assessment
includes 31 questions; there is a subset of these questions that relate to individual interest. This
Enjoyment (Personal Interest) category includes six questions and has a robustness index (RI) of
10.0 (Semsar et al., 2011). According to Adams, et al (2006), the RI considers “all the relevant
statistical quantities” (p. 8) to provide a measure of reliability similar to a Cronbach’s alpha
measure. Cronbach’s alphas for the assessment were not provided by the developers, however,
the RI for the Personal Interest category has the highest possible value, which indicates
extremely strong correlations between the questions (Adams et al., 2006; Semsar et al., 2011). In
the current study, the Cronbach’s alpha for these questions was found to be 0.86 on the pretest
and 0.87 on the post-test. Two sample items from this section of the CLASS-Bio (Semsar et al.,
2011) are the following:
 I think about the biology I experience in everyday life.
 If I had plenty of time, I would take a biology class outside of my major requirements
just for fun.
Performance in Course
Students’ final averages at the end of the course and assigned grades were provided by
the academic department.
47

Procedure and Data Collection
The data collection process was a multi-step procedure. Approval was granted by the
institutional review boards for both the University of Southern California (the researcher’s
institution) and ESU. Regardless of the modality of the course, all the surveys were
administered online. Participating instructors emailed the students to invite them to participate in
the survey and provide a link to access the survey. The surveys were created using Qualtrics and
students accessed them through ESU’s online course management system. Students who
completed both surveys received extra-credit in their BIO 100 course.
Participants were asked to take two surveys. The first survey was administered during
the third and fourth weeks of the semester, while the second survey was administered at the end
of the course. This post-test survey was initially scheduled for the twelfth and thirteenth weeks
of the semester, ending prior to ESU’s Thanksgiving holiday. At the request of an instructor, it
was extended for an additional week and was accessible to students during the holiday. The
second survey administration period ended two weeks before the end of the semester. The two
surveys included authentication panels based on student identification numbers to connect
students’ pretest and post-test results. In addition to the self-reported survey data, the Biology
Department provided a list of BIO 100 grades and the student identification numbers with which
they are associated. A chart showing the sequence of data collection is presented in Figure 1 on
the next page.

48

Figure 1. Sequence of Data Collection
Method Timeline Variables Measured
Survey 1 (pretest)
Third and fourth
weeks of semester
1. Demographic Data
2. Individual Interest in Biology (at
beginning of course)
3. Academic Self-Efficacy
4. Academic Help-Seeking Beliefs
5. Formal-Informal Help-Seeking Beliefs
Survey 2 (post-test)
Twelfth through
fourteenth weeks of
semester
1. Individual Interest in Biology (at end of
course)
2. Help-Seeking Behaviors
a. Frequency of Help-Seeking
b. Target of Help-Seeking
Provided by Academic
Department
After end of course
1. Student’s Average in BIO 100 Class
2. Student’s Assigned Grade in BIO 100
Class
Figure 1. Data collection sequencing chart indicating the methods of data collection and the
variables measured at each stage of the study.

Data Analysis
The data that were collected were analyzed through a number of statistical analyses using
SPSS. These include ANOVA, t-tests, chi-square, and regression analyses. Figure 2, shown on
the next page, presents a chart summarizing the relevant elements of the data analysis. The chart
restates the research questions listed earlier and identifies the independent and dependent
variable associated with each question. It also provides the level of measurement for each
independent and dependent variable, and the statistical tests that were used to analyze the data
associated with each research question.

49

Figure 2. Statistical Analyses
Research
Question
IV(s)
Level of
Measure
ment
DV(s)
Level of
Measure
ment
Statistical
Test
1. Controlling
for previous
levels of
individual
interest in
biology, does
the course
delivery mode
impact
individual
interest in
biology at the
end of the
course?
Program
delivery
method
Nominal
Individual interest in
biology (post-test)
Interval Regression Individual
interest in
biology
(pretest)
Interval
2. Is there a
difference in
student
academic
help-seeking
beliefs and
behaviors by
program
delivery
method?
Program
delivery
method

Nominal
Academic Help-
seeking beliefs
(pretest)
Interval
Independent
samples t-
test
Frequency of
academic help-
seeking behaviors
(post-test)
Interval
Independent
samples t-
test
Person contacted for
help (post-test)
Nominal Chi-square
3. Controlling
for the course
delivery
mode, do
academic self-
efficacy and
individual
interest in
biology
predict final
grade in the
course?
Academic
self-
efficacy
Interval
Grade Interval Regression
Program
delivery
method
Nominal
Individual
interest in
biology
(pretest)
Interval
Figure 2. Research questions, variables, and statistical tests chart restating the research
questions, as well as the independent and dependent variables, their levels of measurement, and
the statistical analyses used in the study.

50

CHAPTER FOUR
This chapter describes the results of the data analysis. This study examined on-campus
and online sections of an introductory biology course to determine if there were differences in
several key factors related to student success. The factors studied were students’ individual
interest in biology, levels of academic self-efficacy, help-seeking beliefs, and academic help-
seeking behaviors. The following research questions guided this study:
1. Controlling for previous levels of individual interest in biology, does the course
delivery mode impact individual interest in biology at the end of the course?
2. Is there a difference in academic help-seeking beliefs and behaviors by course
delivery method among undergraduate students enrolled in an introductory-level
biology class?
3. Controlling for the course delivery mode, do academic self-efficacy and individual
interest in biology predict the performance of undergraduate students enrolled in an
introductory-level biology class?
Participants in the study completed two online surveys. The pretest survey, administered
during the third and fourth weeks of the course, intended to collect demographic information as
well as measure students’ interest in biology, levels of academic self-efficacy, and beliefs about
help-seeking, consisted of 60 questions. In addition to the demographic questions created by the
researcher, other survey questions were drawn from the CLASS-Bio (Semsar et al., 2011), the
MSLQ (Pintrich et al., 1991), and the Karabenick scale (Karabenick, 2003). The post-test
survey, administered during the twelfth through fourteen weeks of the semester, consisted of 36
questions and was intended to examine students’ academic help-seeking behavior, as well as
retest students’ interest in biology through the CLASS-Bio (Semsar et al., 2011) instrument.
51

Both surveys were distributed online by BIO 100 instructors who agreed to have their students
participate in the study. As stated previously, a total of seven instructors distributed the surveys
to their students; two of the instructors taught online sections of BIO 100 while the other five
taught on-campus sections of the course.
Demographic Information
The demographic measurements of students enrolled in the online sections of BIO 100
and the students enrolled in the on-campus sections of BIO 100 were relatively similar across a
number of variables. The average age of the online students was 19 years, 9 months, while the
on-campus students’ average age was 19 years, 4 months. The ratio of female to male students
enrolled in each type of course delivery mode was similar, with female students comprising 70%
of the on-campus students and 75% of the online students. The relationship status reported for
students in both online and on-campus courses were almost identical; more than 96% in each
course delivery mode indicated they were single. Employment status was similar between
students in the online and on-campus sections. Both course delivery modes had a large
percentage of students who were not employed; the percentage of students who were employed,
either full-time or just part-time, was found to be 40% in the online sections and 56% in the on-
campus sections. A greater percentage of online students, 10%, were employed full-time, while
only 4% of on-campus students were employed full-time. However, a chi-square test indicated
that these differences were not statistically significant, 
2
(2, N = 183) = 4.69, p = 0.096. All
sections were composed primarily of full-time students, with 98% of the on-campus students and
100% of the online students indicating that they were enrolled in at least 12 units for the
semester. Nearly all study participants own a laptop, with more than 91% of the students
indicating this to be the case.
52

Despite these similarities, there were areas of differentiation between the online and on-
campus students. Although students in both course modalities indicated high levels of laptop
ownership, 8% of the students in the on-campus sections of BIO 100 indicated that their primary
access to computers was in the ESU library or computer labs. There were no students in the on-
line sections who indicated that this was the case, and a chi-square test found this to be
significant, 
2
(1, N = 183) = 4.16, p = 0.04. The number of previous online courses was another
area of difference. Not surprisingly, the students in the online sections indicated greater previous
experience in online courses than the on-campus students. On-campus students had previously
taken an average of 0.8 online courses while the online students had taken an average of 2.7
previous online courses. A chi-square test showed this to be significant, 
2
(12, N = 182) = 51.7,
p < .001.
Intercorrelations
Analyses were conducted to determine the means, standard deviations, and correlations
of selected variables. These are presented in Table 1 on the next page; a discussion of some of
the more meaningful correlations is offered below.

53

Table 1
Means, Standard Deviations, and Pearson Product Correlations of Selected Variables
Variable Mean
Std
Dev
2 3 4 5 6 7 8 9 10
1. Course
modality
-.132 -.080 -.204** .208** -.301** .003 .008 .227** .195**
2. Self-
efficacy
3.94 0.58 - .037 .192** .059 .268** .407** .248** -.157* .007
3. Age 19.4 2.5 - .010 -.140 .161* .034 -.074 -.076 .018
4. Number
prev
online
courses
1.32 4.13 - -.094 .199** .041 .101 -.105 -.002
5. Help-
seeking
beliefs
3.96 0.58 - -.253** .066 .021 .319** -.081
6. Formal-
informal
help-
seeking
beliefs
3.77 0.7 - .041 -.012 -.140 -.123
7. Interest
(pretest)
2.9 0.8 - .779** .063 -.024
8. Interest
(post-test)
2.91 0.84 - .108 .019
9. Help-
seeking
frequency
(post-test)
2.2 0.73 - -.107
10. Final
class
average
79.9 9.95 -
*p < .05, **p < .01

Course modality was found to have statistically significant correlations with several other
variables in the study. These variables included: the number of previous online courses (r =-
.204, p = .006), help-seeking beliefs (r =.208, p = .005), formal-informal help-seeking beliefs (r
= -.301, p < .001), frequency of academic help-seeking behavior (r = .227, p = .002), and final
average in the class (r = .195, p = .009). These results suggest that students in the online section
of BIO 100 were likely to have taken more previous online courses than the on-campus students.
The online students also indicated that they believed they were less-likely to seek help in the
course, and their frequency of academic help-seeking behavior supported this pretest belief.
54

However, the online students indicated that, if they were to seek help, they believed the
instructor would be a better resource than other students. Course modality was also found to
correlate with students’ final averages in the course, with students in the online section
performing more poorly than on-campus students.
Academic self-efficacy was found to correlate strongly with both pretest interest (r
=.407, p < .001) and post-test interest (r =.248, p =.001), suggesting connections between
students’ interest in the material and students’ beliefs in their ability to master the material.
Aacademic self-efficacy was also found to have a negative correlation with frequency of
academic help-seeking behavior (r =-.157, p =.034). This suggests that students with high
academic self-efficacy were less likely to have sought assistance during the course, which
contradicts some of the research reviewed earlier (see Kitsantas & Chow, 2007). Academic self-
efficacy also correlated with the number of previous online courses (r =.192, p =.01), suggesting
a connection between students’ experience with the online modality and students’ confidence in
their ability to do well in the introductory biology course.
Academic help-seeking beliefs at the beginning of the course were found to strongly
correlate with frequency of academic help-seeking behavior reported (r =.319, p < .001), and
interest in the material at the beginning of the course showed strong correlations with interest at
the end of the course (r =.779, p < .001).
Analysis of Results
The survey data collected by the method described in the previous chapter was analyzed
using SPSS to address the three research questions. The findings will be presented in this section
of the chapter.

55

Research Question 1: Controlling for previous levels of individual interest in biology, does
the course delivery mode impact individual interest in biology at the end of the course?
This question examined individual interest in biology to determine if course modality
impacted the level of individual interest measured at the end of the course. The data indicated
that the combination of the course delivery mode and individual interest in biology at the start of
the course were strongly correlated to individual interest in biology at the end of the course. The
two independent variables, pretest interest and course delivery mode, explain nearly 61% of the
variance in the post-test interest dependent variable, as presented in Table 2 below. In addition,
this level of correlation was demonstrated to be statistically significant as seen in Table 3 below.
There was a significant effect of course delivery mode and individual interest in biology at the
beginning of the course on individual interest in biology at the end of the course at the p < .05
level for the conditions [F(2,180) = 139.34, p < .001].
Table 2
Model Summary: Relation (Modality & Pretest Interest) to Post-Test Interest
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .779
a
.608 .603 .527
a. Predictors: (Constant), Pretest Interest, Course Delivery Mode

Table 3
ANOVA Results: Statistical Significance of the Relation (Modality & Pretest Interest) to
Post-Test Interest
Model
Sum of
Squares
df
Mean
Square
F Sig.
Regression 77.428
a
2 38.714 139.344 .000
b
Residual 50.009 180 .278
Total 127.437 182
a. Dependent Variable: Post-test Interest
b. Predictors: (Constant), Pretest Interest, Delivery Mode

Further statistical analysis was performed to examine the separate contributions of course
delivery mode and the level of individual interest at the beginning of the course to students’ level
56

of interest at the end of the course. The analysis demonstrated that the correlation coefficient
between interest in biology at the beginning of the course and interest in biology at the end of the
course was statistically significant and rather large (  = .779, p < .001). In contrast, the
correlation coefficient between course delivery mode and interest at the end of the course was
demonstrated to be extremely small and not statistically significant (  = .005, p =.910). Based
on the results presented in Table 4 below, it is clear that course delivery mode does not impact
individual interest in biology at the end of the course.
Table 4
Regression Analysis: Relation Modality to Post-Test Interest and Pretest Interest to Post-
Test Interest
Unstandardized
Coefficients
Standardized
Coefficients

B Std Error Beta t Sig.
(Constant) .546 .212 2.576 .011
Course Delivery
Mode
.010 .089 .005 .114 .910
Pretest Interest .812 .049 .779 16.693 .000
a. Dependent Variable: Post-test Interest

Research Question 2: Is there a difference in academic help-seeking beliefs and behaviors
by course delivery method among undergraduate students enrolled in an introductory-level
biology class?
This question examined course delivery method to determine if it correlated with
differences in help-seeking beliefs as measured on the first survey and differences in academic
help-seeking behaviors as measured on the second survey. The help-seeking behaviors measured
were the frequency of academic help-seeking actions, as well as the person from whom help was
sought.
Academic help-seeking beliefs. Analyses were conducted to determine if there were
significant differences in beliefs about help-seeking by course delivery mode. An independent
57

samples t-test indicated a statistically significant difference in academic help-seeking beliefs
based on course delivery mode, as seen in Table 5 below. As reported in Table 6, students in the
online sections of BIO 100 indicated that they believed it was less important to seek help in the
course than did students in the on-campus sections of BIO 100, t(181) = -2.855, p =.005.
Table 5
Independent Samples Test. Results of t-test: Modality and Help-Seeking Beliefs
Levene’s Test
for Equality of
Variances
t-test for Equality of Means
F Sig t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
.007 .932 -2.855 181 .005 -.271 .095 -.458 -.084

Table 6
Group Statistics: Descriptive Statistics: Modality and Help-Seeking Beliefs
Are you
taking this
course
online or on-
campus?
N Mean
Std
Deviation
Std. Error
Mean
Mean_
HelpSeek
Online 48 3.764 .546 .079
On-campus 135 4.035 .570 .049

Additional analyses examined potential differences in formal and informal academic
help-seeking beliefs based on delivery mode. This explored whether students believed they were
more likely to contact the instructor (formal) or contact another student (informal) if they sought
assistance in the course. An independent samples t-test indicated a statistically significant
difference in formal and informal help-seeking beliefs based on course delivery mode, as shown
in Table 7 below. Compared to students in the on-campus sections of BIO 100, students in the
online sections of BIO 100 indicated that they believed they were more likely to seek help from
the instructor instead of from other students, as reported in Table 8.
58

Table 7
Independent Samples Test: Results of t-test: Modality and Formal/Informal Help-Seeking Beliefs
Levene’s Test
for Equality of
Variances
t-test for Equality of Means
F Sig t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
.550 .459 4.240 181 .000 -.476 .112 .254 .697

Table 8
Group Statistics: Descriptive Statistics: Modality and Formal/Informal Help-Seeking
Beliefs
Are you
taking this
course
online or on-
campus?
N Mean
Std
Deviation
Std. Error
Mean
Mean_
HelpSeek
Online 48 4.125 .640 .092
On-campus 135 3.649 .677 .058

Academic help-seeking behaviors. Analyses were conducted to determine if there were
significant differences in academic help-seeking behaviors by course delivery mode.
Specifically, this study examined differences in the number of times students asked for help in
the BIO 100 course, as well as from whom they sought help. The sources of help were
categorized as: Professor or Teaching Assistant, Classmate, or Someone Else. Data regarding
academic help-seeking behaviors were collected through the post-test survey conducted at the
end of the semester.
Analyses were conducted to determine if there were significant differences in the
frequency of academic help-seeking behaviors based on course delivery mode. An independent
samples t-test indicated there was a statistically significant difference in help-seeking behavior
based on course delivery mode, as presented in Table 9 below. As seen in Table 10, students in
59

the online sections of BIO 100 sought help less frequently than students in the on-campus
sections of BIO 100, t(181) = -3.137, p =.002.
Table 9
Independent Samples Test: Results of t-test: Modality and Help-Seeking Behavior
Levene’s Test
for Equality of
Variances
t-test for Equality of Means
F Sig t df
Sig.
(2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
2.713 .101 -3.137 181 .002 -.374 .119 -.609 -.139

Table 10
Group Statistics: Descriptive Statistics: Modality and Help-Seeking Behavior
Are you
taking this
course
online or on-
campus?
N Mean
Std
Deviation
Std. Error
Mean
Mean
HelpSeek
Online 48 1.944 .605 .087
On-campus 135 2.319 .743 .064

Additional analyses were conducted to determine if there were differences in the sources
from whom the students sought help. These analyses examined whether students in the different
course delivery modes were more likely to seek help from a professor, from a classmate, or from
someone else. A set of three chi-square tests were performed to examine student help-seeking
behaviors regarding each source of assistance.
No relationship was found between course delivery mode and frequency of seeking help
from the professor, 
2
(4, N = 181) = 8.74, p =.07. See Table 11 below.

60

Table 11
Chi-Square Tests: Modality and Frequency of Seeking Help (Professor)
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 8.741
a
4 .068
Likelihood Ratio 9.760 4 .045
Linear-by-Linear
Association
1.075 1 .300
N of Valid Cases 181
a. 3 cells (30.0%) have expected count less than 5. The minimum expected count is .53.

Similarly, no relationship was found between course delivery mode and frequency of
seeking help from someone else, 
2
(4, N = 178) = 2.38, p =.67.
Table 12
Chi-Square Tests: Modality and Frequency of Seeking Help (Someone Else)
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 2.381
a
4 .666
Likelihood Ratio 2.332 4 .675
Linear-by-Linear
Association
.220 1 .639
N of Valid Cases 178
a. 2 cells (20.0%) have expected count less than 5. The minimum expected count is 1.29.

However, a significant relationship was found between course delivery mode and
frequency of seeking help from a classmate, 
2
(4, N = 180) = 26.89, p < .001, as presented in
Table 13. This indicates that students in the online sections of BIO 100 were significantly less
likely to seek help from a classmate than were the students in the on-campus sections of BIO
100.
Table 13
Chi-Square Tests: Modality and Frequency of Seeking Help (Classmate)
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 26.893
a
4 .000
Likelihood Ratio 28.185 4 .000
Linear-by-Linear
Association
24.004 1 .000
N of Valid Cases 180
a. 1 cell (10.0%) has expected count less than 5. The minimum expected count is 2.30.
61

Because the findings indicated a significant difference in seeking help from a classmate
based on course delivery mode, the researcher conducted an independent samples t-test to further
examine this data. The results of this analysis demonstrated that students in on-campus sections
of BIO 100 sought help from classmates much more frequently than students in online sections
of the course. This difference was shown to be significant at the level p < .001, and is reported
in Tables 14 and 15 t(178) = -5.250, p < .001. Due to statistical concerns related to this post-hoc
test, an additional ANOVA analysis utilizing the Bonferroni correction was conducted on this
data. The Bonferroni correction reduces the chances of receiving a “false positive” result
indicating significance when none exists. The corrected ANOVA analysis still found the
difference to be significant, F(1,178) = 27.56, p < .001.
Table 14
Independent Samples Test: Results of t-test: Modality and Help-Seeking Behavior (Classmate)
Levene’s Test
for Equality of
Variances
t-test for Equality of Means
F Sig t df
Sig.
(2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
1.273 .261 -5.250 178 .000 -1.004 .191 -1.382 -.627

62

Table 15
Group Statistics: Descriptive Statistics: Modality and Help-Seeking Behavior
(Classmate)
Are you
taking this
course
online or on-
campus?
N Mean
Std
Deviation
Std. Error
Mean
During the
class, how
often did
you seek
help from:-
Classmate or
peer
Online 46 1.89 1.016 .150
On-campus 134 2.90 1.152 .100

Research Question 3: Controlling for the course delivery mode, do academic self-efficacy
and individual interest in biology predict the performance of undergraduate students
enrolled in an introductory-level biology class?
This analysis examined whether or not students’ levels of academic self-efficacy and
students’ levels of individual interest correlated with their final averages in BIO 100. The data
indicated that the independent variables, which were course delivery mode, individual interest at
the beginning of the course, and academic self-efficacy, did not demonstrate a statistically
significant relationship to the dependent variable, which was final average in the course.
Table 16
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .202
a
.041 .025 9.830
a. Predictors: (Constant), Course Delivery Mode, Mean Interest, Mean Self-Efficacy

63

Table 17
ANOVA Results
a

Model
Sum of
Squares
df
Mean
Square
F Sig.
Regression 725.125

3 241.708 2.501 .061
b
Residual 17009.582 176 96.645
Total 17734.706 179
a. Dependent Variable: Final Grade
b. Predictors: (Constant), Course Delivery Mode, Mean Interest, Mean Self-Efficacy

Regression analysis was performed to more closely examine the individual contribution
of each independent variable. The results of the regression analysis, shown in Table 18 below,
indicated that neither academic self-efficacy nor individual interest had a significant influence on
student performance in the course. However, the results do indicate that there is a statistically
significant relationship between course modality and final average in the course.
Table 18
Regression Analysis
Unstandardized
Coefficients
Standardized
Coefficients

B Std Error Beta t Sig.
(Constant) 70.252 6.154 11.416 .000
Pretest Interest -.605 1.011 -.048 -.598 .551
Self-Efficacy .882 1.390 .052 .635 .526
Course Delivery
Mode
4.566 1.686 .202 2.708 .007
a. Dependent Variable: Final Grade

Because the findings indicated a significant difference in final average in the class based
on course delivery mode, the researcher conducted a t-test to further examine this data. The
results of this analysis demonstrated that, on average, students in on-campus sections of BIO 100
had higher final averages than students in online sections of the course. This difference was
shown to be significant at the level p =.009, and is reported in Tables 19 and 20, t(178) = -2.649,
p = .009. Due to statistical concerns related to this post-hoc test, an additional ANOVA analysis
64

utilizing the Bonferroni correction was conducted on this data. The corrected ANOVA analysis
still found the difference to be significant, F(1,178) = 7.019, p = .009.
Table 19
Independent Samples Test
Levene’s Test
for Equality of
Variances
t-test for Equality of Means
F Sig t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
.609 .436 -2.649 178 .009 -4.401 1.661 -7.68 -1.12

Table 20
Group Statistics
Are you
taking this
course
online or on-
campus?
N Mean
Std
Deviation
Std. Error
Mean
Grade value
Online 47 76.67 10.336 1.508
On-campus 133 81.07 9.593 .832

Summary
This chapter reported the data collected through two online surveys and provided the
results of statistical analyses used to explore the three research questions posed in the first
chapter of this study. Each research question was addressed individually.
The first research question examined if, after controlling for individual interest in biology
at the beginning of the course, the course delivery mode would impact individual interest in
biology at the end of the course. The results of the statistical analyses demonstrated that the
impact of course delivery mode on interest at the end of the course was not statistically
significant.
65

The second question examined beliefs and behaviors related to academic help-seeking to
determine if there were differences between the students in the online sections and the on-
campus sections of the BIO 100 course. The analyses demonstrated that there were statistically
significant differences in all academic help-seeking beliefs and behaviors explored in the study.
Specifically, it was found that, compared to students in the on-campus sections of BIO 100,
students in the online sections of the course believed that seeking help in the class was less
important. The online students also indicated a preference for formal help-seeking; they believed
that they were more likely to ask for help from the instructor than from another student when
they did seek help. Data collected at the end of the BIO 100 course on academic help-seeking
behaviors indicated that students’ behaviors corresponded with their expectations. Statistical
analyses demonstrated that the students in the online sections sought help less frequently than
students in the on-campus sections. It was also shown that students in the online sections were
less likely to seek help from classmates than the students in the on-campus sections of the
course.
The third research question examined whether or not there were relationships between
course delivery, academic self-efficacy or individual interest and the students’ final averages in
BIO 100. Analysis of the data indicated that neither individual interest nor academic self-
efficacy showed a statistically significant relationship with the final grade in the BIO 100 class.
Interestingly, the data did indicate that there was a significant relationship between course
modality and students’ final grades in the course. Implications of these findings will be
discussed in the next chapter of this study.

66

CHAPTER FIVE
Discussion
This study explored the relationships between academic success factors and course
delivery modes in online and on-campus sections of an introductory science course at a large
comprehensive public university in California. A review of relevant literature found that General
Education programs are prevalent throughout U.S. higher education (Aloi et al., 2003).
Introductory laboratory science courses are a requirement in many of these programs, including
the GE programs in the California State University system (Reed, 2011). The extent of science
courses throughout higher education (Antonellis et al., 2011) and the important contributions
such courses make to students’ research, critical-thinking, and problem-solving skills (Aloi et al.,
2003), make them a worthy subject for investigation.
Online courses rapidly became an integral component of the U.S. higher education
landscape (Allen & Seaman, 2014), increasing access for nontraditional students, as well as those
living in remote areas (Boling et al., 2012; Brown, 2012). Administrators at numerous colleges
and universities indicated that online courses are a critical component of their institutions’
strategic plans (Allen & Seaman, 2014). The expected continued growth in online classes makes
these Internet-based courses a highly-relevant setting for research.
The importance of student success prompted an examination of factors connected to
academic achievement. This study specifically investigated individual interest, academic self-
efficacy, and academic help-seeking beliefs and behaviors. Numerous studies found that these
factors promote student success in college courses (Hidi & Renninger, 2006; Lawson et al.,
2007; Kitsantas & Chow, 2007; Multon et al., 1991). However, there had been very little
67

research examining success factors in online and on-campus introductory science courses, which
has led to this current study.
The previous chapter of this dissertation provided the results of the study, as well as a
review of the statistical analyses conducted. This chapter discusses the findings and their
implications. The next section of this chapter will discuss the findings based on significant
components of the study, including the success factors and course modality.
Help-Seeking Beliefs and Behaviors
The pretest survey measured academic help-seeking beliefs, while the post-test survey
measured academic help-seeking behaviors. In several ways, the findings of this study regarding
the help-seeking construct aligned with the cited research (Clegg, 2006; Kitsantas & Chow,
2007). Students in the online sections of BIO 100 believed they would be less likely to seek help
and they sought help less often throughout the course. In addition, the online students were far
less likely to seek assistance from classmates; when they did ask for help, it was from an
instructor rather than another student.
This reduced frequency of academic help-seeking behaviors among online students as
well as their tendency to favor formal help-seeking (from the instructor) as opposed to informal
help-seeking (from peers) were observed in the cited research (Boling et al., 2012; Kitsantas &
Chow, 2007). Since online students may be more isolated from their classmates (Boling et al.,
2012, Rabe-Hemp et al., 2009), their tendency to seek assistance from instructors rather than
other students is understandable. Despite participation in discussion boards, it is feasible that
online BIO 100 students were not familiar with other students in the course, which would have
made it nearly impossible for them to seek help from their classmates. Without fellow students
as sources of assistance, online students must seek help from the instructor.
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The reduced frequency of help-seeking during the semester may also have been due to
online students’ relative isolation. While on-campus BIO 100 students could speak informally
with other students before and after class--and may have counted these unplanned conversations
as help-seeking behaviors on the post-test survey--online students must be more deliberate in
their help-seeking activity. Pondering an issue, phrasing a question, and writing and sending an
email typically take more effort than simply asking for feedback from a student sitting nearby.
The multiple steps that online BIO 100 students must take in order to seek assistance could have
made the academic help-seeking process more onerous and may explain why the help-seeking
frequency was reduced for online students.
Individual Interest
For this study, students’ individual interest in biology was measured twice through a
subset of questions on the CLASS-Bio survey instrument. As one might expect, the level of
individual interest measured on the post-test closely matched the level of individual interest
measured on the pretest. The level of interest in biology showed a negligible increase from the
pretest measurement to the post-test measurement. Other introductory science courses that used
a version of the CLASS assessment tool (Adams et al., 2008; Redish et al., 1998; Semsar et al.,
2011) found that students’ attitudes towards science, including individual interest, tended to
become less expert-like.
The results of this study also indicated that the modality of the course did not influence
the level of individual interest of the students. This finding supports several researchers’
contention that other factors, such as instructional design and instructor, have a greater impact
than the mode of instruction on student interest (Clark et al., 2010; McGuire & Castle, 2010).
69

This study's results also indicated that individual interest levels in biology were not
correlated with academic achievement in the BIO 100 course. This finding seems
counterintuitive--one would expect students interested in biology to do well in a biology course--
and appears to contradict several cited sources (see Durik & Harackiewicz, 2007; Krapp, 1999;
Schraw & Lehman, 2001). One possible explanation why interest in biology did not correlate
with higher grades in BIO 100 may be that students’ interest in the subject did not align with the
specific topics covered in the course. As an example, although students’ survey responses
indicated that they were interested in biology in a general sense, it is possible that they were not
interested in the topics covered in BIO 100, such as the structure of cells or the scientific method.
Further research to examine this lack of alignment between this study’s findings and earlier
publications would be useful.
Academic Self-Efficacy
Students’ levels of academic self-efficacy--their sense of confidence that they would be
successful in the ESU BIO 100 class--were measured as part of the pretest survey at the
beginning of the course. The results of this survey indicated that self-efficacy in BIO 100 was
correlated with the number of previous online courses taken. Notably, this correlation was found
in both the online version of the course and the on-campus version of the course.
The results of the survey also indicated that students’ academic self-efficacy correlated
with their levels of individual interest. As stated previously, individual interest and academic
self-efficacy can lead to academically advantageous behaviors, such as persistence and self-
regulation (Durik & Harackiewicz, 2007; Hidi, 1990). Due to the limitations of the study, it is
not clear if one factor precedes the other. That is, does interest in a topic promote confidence for
success, or does confidence in one’s ability to succeed promote interest in a topic?
70

The study's findings also indicated that academic self-efficacy levels were not correlated
with academic achievement in the course. This finding contradicts several cited studies (see
Kitsantas & Chow, 2007; Lawson et al., 2007; Multon et al., 1991) that found connections
between academic self-efficacy and learning. Academic self-efficacy has been found to predict
mental effort and persistence even in the face of obstacles (Bandura, 2006; Pajares & Schunk,
2001). It is surprising that students’ predictions of their abilities to succeed in the BIO 100
course did not appear to relate to their actual success in the class. Because the pretest survey
took place before the first exam in the course, it is possible that students had not yet received any
feedback on their abilities to succeed in the class. It is also possible that students at the
beginning of their university career may overestimate the likelihood for success in a college-
level science course.
The finding that academic self-efficacy levels were not correlated with academic
achievement, taken together with the finding regarding individual interest and achievement, is
rather startling. It suggests that students who were interested in biology, and those students who
assumed they would do well in the course, did not perform statistically better than students who
were not interested in the material and were doubtful of their abilities to succeed in the class.
Educators expect that students with high levels of interest and self-efficacy in a course display
more motivation, increased self-regulation, and greater mental effort than others, which leads to
higher grades in the course (Clark, 2009; Hidi, 1990; Lawson et al., 2007). That this connection
was not observed in this study is an unexpected result and is worth further consideration.
One possibility is that students with high levels of self-efficacy and individual interest did
not display the expected behaviors of increased self-regulation, greater mental effort, and
improved motivation. It is feasible that students were overly optimistic when answering the
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questions on the survey related to self-efficacy and individual interest. Students’ survey
responses may have reflected what they thought their answers should be, or may not have
aligned with their later actions. These discrepancies, known as social desirability and
consistency, will be discussed in the Limitations section. Another plausible explanation for this
finding is that, despite the students’ interest and academic self-efficacy, there were other factors
that prevented the students from displaying the expected behaviors. Personal responsibilities,
such as work or family obligations, may have limited the time students put towards their BIO
100 class and prevented students from exhibiting these academically advantageous behaviors.
A second possible explanation is that students with higher levels of interest and academic
self-efficacy did exhibit these academically advantageous behaviors, but that the behaviors did
not result in increased achievement in the BIO 100 course. In this second situation, one feasible
explanation is that these students studied the material but did so in an unproductive manner. For
example, they may have studied biology-related material that interested them but did not appear
on exams, resulting in lower test scores. Further research would allow for a more focused
investigation of this result.
Course Modality
There were a number of structural differences between the online and on-campus sections
of BIO 100. While the on-campus classes had regular, structured meeting times, the online
sections were asynchronous courses. Although there were deadlines for the completion of course
modules, the online students could choose the times to work on the lessons and assignments.
The grade components were also different for the two modalities of the course. For several of
the on-campus sections, the students’ final averages were based on four exams and two research
papers. In contrast, the online sections had three exams and one research assignment, but also
72

quizzes, additional assignments, and required discussion board participation. In addition, since
online students could access course resources when they took their exams, these exams included
more conceptual questions in addition to knowledge-based questions. These structural
variations, as well as differences in instructors, are limitations that may impact the study’s
findings.
This study demonstrated that levels of individual interest were not impacted by course
modality. Students in the online and on-campus sections of BIO 100 displayed similar levels of
interest in biology as measured on the pretest and post-test. This finding reflects well on the
work of the course designers and instructors of the online sections of BIO 100. Despite the
differences in settings and modes of interaction, all students experienced the same changes in
individual interest.
There were some differences observed between students in the online sections and
students in the on-campus sections of the course. These include the unsurprising finding that
students in the online section of BIO 100 had taken a greater number of previous online courses.
It is reasonable to assume that students with previous online course experience were more likely
to choose another online course. In addition, it is likely that the reasons a student chose to take a
previous online course would prompt them to do so again. Differences in academic help-seeking
beliefs and behaviors between the online and on-campus students, discussed at length in the
previous section, were relatively unremarkable and consistent with previous research by Clegg
(2006) and Kitsantas and Chow (2007).
One notable difference was the variation in final averages between the online and on-
campus students. Studies of achievement in online courses reviewed for this study had
inconsistent results, with some suggesting that learning is increased online (Hiltz, 1997;
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Schoenfeld-Tacher et al., 2001) and others finding the opposite (Carbonaro et al., 2006). This
study showed lower levels of achievement in BIO 100 among online students when compared to
the on-campus students. In this study, the average final score for students in the online sections
of BIO 100 was lower than the average final score for students in the on-campus sections of the
course.
There are a number of possible explanations for this discrepancy that should be
considered. One possible explanation for this difference is that the online students simply did
not learn as much as the on-campus students in the course and scored lower on exams, resulting
in lower course averages. An alternate possibility supported by previous research (Mentzer et
al., 2007) is that the online students completed fewer assignments, or performed more poorly on
the assignments they did complete, and earned lower class averages because of this difference.
Additionally, differences in instructors may have led to differences in grading, resulting in this
finding. Further exploration to examine the lower averages for online students is clearly
warranted and will be discussed in the next section.
Implications
This section provides recommendations for future research and for future practice.
Recommendations for research include several areas that are beyond the scope of this study, but
would provide valuable clarification to some of the issues raised by the findings. Other research
recommendations offer opportunities to expand on this initial investigation. Recommendations
for practice address three areas of interest: differences in student experience based on course
modality; promoting academic achievement in BIO 100, particularly among online students; and
promoting success factors. Some of these recommendations for practice can be easily
implemented by BIO 100 instructors with minimal impact on their workload. Several
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recommendations can be implemented by the instructor or the department, while others are more
properly situated at the university level.
Recommendations for Research
The observed variation in grades between the online and on-campus students is a point of
concern. With more and more institutions offering online courses, it is critical to further explore
any discrepancies that are found between students on campus and students who are online. The
findings of the study suggest several new areas of research.
The ESU Biology Department provided this researcher with final course averages for all
students in the BIO 100 course who participated in both surveys. Although the final averages
indicated that the online students did not perform as well as on-campus students, this may not
necessarily indicate that online students learned less than on-campus students. As noted
previously, other researchers found that online students had similar test scores but lower levels of
homework completion, and this led to lower class averages among online students (Mentzer et
al., 2007). To account for this, it would be useful for future researchers to obtain additional data
on student performance, including information on assignment and exam grades. Alternately, if
the class includes a cumulative final exam, grades on the final may be more informative as a
measure of learning. Either of these two metrics in combination with final course average would
provide a more robust set of data on student performance. Qualitative data on students’
experiences in the course--such as those collected through the university’s faculty and course
evaluations--would also be useful to explore this area further.
Previous studies (Jaggars & Bailey, 2010; Picciano et al., 2010) noted higher withdrawal
rates and lower assignment completion rates among online students than among on-campus
students. It would be instructive to examine if this trend would be observed in the ESU BIO 100
75

course. For example, if assignment completion rates were lower for online students, it would be
worth exploring the reasons for this to determine if they were student-centered or university-
centered. University-centered issues (such as confusing assignment directions or technical
malfunctions) could be easily resolved while student-centered issues (such as family
responsibilities or working overtime) are more difficult to address. Additional data, such as the
reasons behind students’ decisions to withdraw, would also be worthy of consideration.
An additional area for further study would be to explore the academic characteristics and
preparation of the students in the online sections of BIO 100 at ESU. Because the study did not
allow for random assignment of students, it is possible that the students in the on-campus
sections of the course were stronger students than those in the online sections, which could
explain observed differences in academic achievement. Collecting data on the students, such as
overall grade point average at ESU, or high school grades and standardized test scores, may
provide a sense of any differences in academic preparation.
The surprising lack of correlations between final averages and the factors of academic
help-seeking and academic self-efficacy are also worth exploring. Qualitative data may allow
for a more detailed examination of these factors. Additionally, it may be useful to measure
academic self-efficacy at another point during the semester, such as after the first exam, when
students may have a more accurate understanding of their abilities to succeed in the course.
As discussed previously, the CLASS survey instrument was designed to study students’
attitudes towards science and compare them to the attitudes held by experts in the field (Semsar
et al., 2011). This current study only examined responses to the subset of questions on the
instrument that aligned with the construct of individual interest in biology. Future researchers
76

may wish to use students’ responses to the entire survey and examine any observed differences
in attitudes observed between online and on-campus students.
Other researchers may also wish to replicate the study with other science departments at
ESU. In addition to biology, there are GE courses at ESU that provide introductions to
chemistry and physics, and there are CLASS surveys for these areas as well (Adams et al., 2006;
Adams et al., 2008). It would also be of value to conduct similar studies at other types of
institutions such as community colleges, private colleges, and more highly-selective universities,
to see if the results are consistent.
Recommendations for Practice
The recommendations for practice are intended to impact three targeted goals: address
differences between online and on-campus students; increase academic achievement; and
increase levels of academic help-seeking, individual interest, and academic self-efficacy. With
more courses offered online, it will be important to address discrepancies in experiences and
outcomes between online and on-campus students. Improved academic achievement and
learning is clearly beneficial to students. Increasing the levels of help-seeking, interest, and self-
efficacy are also beneficial to students. Although this particular study did not find the expected
correlations between these three factors and academic achievement in BIO 100, these factors still
possess value in their own right.
Several recommendations focus on help-seeking and the observed differences between
online and on-campus students. As noted previously, online students indicated that they were
less likely to seek help and sought help less frequently. Frequent contact with instructors has
been seen to be beneficial (Kitsantas & Chow, 2007; Rabe-Hemp et al., 2009) and should be
77

encouraged, particularly among online students. Actions at the faculty and department level that
could encourage student contact include
 ensuring that syllabi include statements that encourage students to connect with their
faculty;
 providing students with evidence that seeking help has been shown to be beneficial;
 providing multiple methods of contacting faculty;
 promoting virtual office hours for online students, including options for real-time
conversations through online chat and video conferencing; and
 offering nontraditional times for student-faculty interactions when possible.
Since student-to-student interaction can also be beneficial, the department may also wish
to explore methods to connect online students to their classmates. One possibility would be to
provide student chat rooms or virtual commons, although the nature of non-moderated online
interaction provides reasons for caution when considering these option.
Greater preparation for online students has been seen to promote academic success
(Jaggars & Bailey, 2010; Lorenzo, 2011). Although ESU provides information on technical
requirements for online students as well as optional tutorials, it is not clear if there is specific
training required for students enrolled in online courses. Videos by the BIO 100 instructors
explaining their expectations for students, as well as details on how to participate in an online
course, would enhance student preparation for the class. Tracking the viewing of the video, or
making the students responsible for the content through an assignment or exam, would ensure
that more students participate in this process and acquire the needed information. Viewing these
preparatory videos would also provide students with an introduction to their instructors; this
personal connection may encourage students to contact instructors more readily.
78

Additional technical assistance could be offered to online students by the university.
ESU does offer technical support by phone every day of the week and makes it available until
the early evening. However, an expansion of the times of operation until late in the evening
would allow more online students to access this service. It would also be beneficial if the
institution was able to provide assistance through an online chat mode or video conferencing,
which may be more helpful than a phone call. This could be useful in addressing the discrepancy
in grades between online and on-campus students, particularly if technical issues were found to
be an obstacle facing the online students.
Another recommendation would address levels of interest and academic self-efficacy.
This study found that individual interest in the topic correlated with self-efficacy in the course.
Although the results of this study do not suggest that increasing interest and academic self-
efficacy will lead to improved grades, increasing these elements can be beneficial in other ways.
Promoting a general understanding of biology and its applications in everyday life are included
in the goals of the BIO 100 course; this objective becomes more easily attained through
developing students’ interest in the material. In addition, it seems likely that instructors would
prefer to teach students who are interested in biology and confident in their abilities to succeed in
the BIO 100 course, rather than students who are apathetic and believe themselves to be facing
failure. Instructors may wish to consider opportunities to build academic self-efficacy and
individual interest in concert.
One recommendation to build academic self-efficacy would be the inclusion of attainable
goals in the beginning of the BIO 100 class. It has been found that early mastery experiences
can encourage students’ confidence in their abilities to successfully complete a course (Clark,
2004; Margolis & McCabe, 2006). Academic self-efficacy can also be developed by providing
79

videos of other students working through challenging problems (Clark, 2004). This type of
modeling has been found to be effective in promoting self-efficacy (Bandura, 1986: Margolis &
McCabe, 2006). Courses that teach study skills have also been found to increase students’
academic self-efficacy (Wernersbach, Crowley, Bates, & Rosenthal, 2014) and could be
considered at ESU.
In order to promote students’ individual interest in biology, instructors may wish to
include elements that foster situational interest. Elements with situational interest, such as the
use of surprising information or connecting new information to students’ experiences, often lead
to the development of individual interest in a topic (Hidi & Renninger, 2006).
Limitations
The design of the study presented a number of limiting factors. The study was
correlational, not causational. Although relationships were found between the independent and
dependent variables, it was not possible to conclude that the changes in the dependent variables
were caused by the independent variables. Other limitations may have included social
desirability, consistency, and self-selection biases. Social desirability bias occurs when
participants report answers they believe are acceptable to others, rather than answering honestly.
Consistency bias occurs when participants’ responses do not accurately reflect their actions. It is
possible that participants misinterpreted questions or answered them inaccurately. Self-selection
bias occurs when individuals voluntarily assign themselves into a group, which could cause a
biased sample. Because the researcher was not able to randomly assign students to online or on-
campus sections, the self-selection bias may be present in this study.
In addition, each of the seven sections of BIO 100 included in the study was taught by a
different instructor, which may have led to changes in the students’ experiences in the course.
80

These differences may have included the content or sequencing of the course, as well as the
availability and responsiveness of the instructor. Individual instructors may also have graded
more or less rigorously, causing differences in grading and making it difficult to generalize
across the entire course. There were also differences in the components of the final averages,
which makes it problematic to compare the final averages of students based on class modality.
In addition, it is possible that students’ final grades reflected self-regulatory skills, such as time
management, rather than knowledge of the topic. Finally, the students who chose to complete
the survey may not have accurately represented the student population. Limitations of this study
could compromise the results and lead to incorrect conclusions.
Although the study has numerous limitations, the researcher controlled several factors,
including the number of students who received the survey, how the survey was administered,
what topics the survey addressed and how the questions were asked. To control how the
questions were asked, the researcher developed the questions based on existing, validated
surveys and distributed the same surveys through the same method to all participants. In
addition, information was provided that explained the confidentiality of the study. The factors
the researcher controlled provided internal and external validity as well as reliability by reducing
the possible limitations of the study.
Conclusion
This study examined the relationship of academic success factors to course delivery mode
in online and on-campus introductory biology courses at a comprehensive public university in
California. The importance of scientific literacy makes introductory science courses a
productive site for investigation. The success factors that this study explored--individual
interest, academic help-seeking, and academic self-efficacy--are factors that can have
81

tremendous impact on students’ success in higher education and as life-long learners and
informed citizens. In addition, as more institutions offer more courses online, exploring
differences by course modality is critical to ensure that students in virtual classrooms achieve
results similar to students in brick-and-mortar classrooms. This study revealed several
differences based on the modality of the course, primarily related to help-seeking beliefs and
behaviors, as well as academic achievement. The study found no differences in academic self-
efficacy or individual interest based on course modality.
The findings of this study are relevant to course designers and instructors of online
classes. The findings may also be useful to colleges and universities offering online courses as
they seek to provide meaningful learning experiences to online students. The recommendations
offered can assist in minimizing discrepancies between online and on-campus students,
particularly as related to academic outcomes. In addition, the recommendations can encourage
help-seeking behavior and increase levels of individual interest and academic self-efficacy
among students.
82

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Appendix A

Demographic questions

1. What is your current employment status?
a. Not currently working
b. Working part-time
c. Working full-time

2. Please indicate your ethnicity.
a. Hispanic/Latino
b. American Indian or Alaska Native
c. Asian
d. Black or African American
e. Native Hawaiian or other Pacific Islander
f. White
g. Two or more races
h. Other – text

3. What is your gender?
a. Male
b. Female
c. Transgender

4. What is your age in years?

5. Please indicate your relationship status
a. Single
b. Married/Domestic Partner
c. Separated/Divorced
d. Widowed

6. What is the highest level of education either of your parents has completed?
a. Primary or less
b. Middle school
c. Some high school
d. High school diploma or GED
e. Some college
f. Associate Degree/Certificate
g. Bachelor’s Degree
h. Master’s Degree
i. Doctoral Degree

7. I am currently enrolled as a
Part-time student (less than 12 units)
Full-time student (12 units or more)
91

8. What is your current major?

9. What is your intended major?

10. Please check all that apply:
a. I own my own laptop
b. I own an iPad or other tablet device
c. I have a personal computer at home or work
d. I share a computer with my family or roommates
e. My primary access to computers is at the CSUF library or computer labs
f. None of the above

11. How many online courses have you taken previously?

12. Are you taking this course online or on-campus?

13. Why did you choose to take this format? (check all that apply)
a. Scheduling
b. Instructional considerations (e.g., preferred method of instruction, quality of
instruction, access to instructor)
c. Geographic reasons
d. Family responsibilities
e. Professional responsibilities
f. Other: Text answer

92

Appendix B

Self-Efficacy (MSLQ)

1. I believe I will receive an excellent grade in class.

2. I’m certain I can understand the most difficult material presented in the readings for the
course.

3. I’m confident I can learn the basic concepts taught in this course.

4. I’m confident I can understand the most complex material presented by the instructor in
this course.

5. I’m confident I can do an excellent job on the assignments and tests in this course.

6. I expect to do well in this class.

7. I’m certain I can master the skills being taught in this class.

8. Considering the difficulty of this course, the teacher, and my skills, I think I will do well
in this class.

93

Appendix C

Help-Seeking Beliefs and Behaviors Measures

Help-Seeking Beliefs (MSLQ)

1. Even if I have trouble learning the material in this class, it is important that I try to do the
work on my own, without help from anyone. (REV)

2. It is important to ask the instructor to clarify concepts I don’t understand well.

3. If I don’t understand the material in this course, it is important that I ask another student
in this class for help.

4. It is important to identify students in this class whom I can ask for help if necessary.

Formal Versus Informal Help-Seeking Beliefs (Karabenick)

1. If I were to seek help in this class I would ask the teacher rather than another student.

2. I would prefer asking another student for help in this class rather than the instructor.
(REV)

3. In this class, the teacher would be better to get help from than would a student.

Help-Seeking Behaviors

During this class, how often did you seek help from:

Never/Not
at all
1-2 times
per
semester
1-2 times
per month
Once a
week
More than
once a
week
Instructor or
Teaching Assistant

Classmate or peer
Other

94

Appendix D

Interest in Biology (CLASS-Bio)

The entire CLASS-Bio instrument will be included in the online survey, so all questions
are reproduced below. The questions to be used in the analysis are those relating to personal
interest, which are numbers 1, 2, 9, 12, 18, and 27 (Semsar et al., 2011).

1. My curiosity about the living world led me to study biology.

2. I think about the biology I experience in everyday life.

3. After I study a topic in biology and feel that I understand it, I have difficulty applying
that information to answer questions on the same topic.

4. Knowledge in biology consists of many disconnected topics.

5. When I am answering a biology question, I find it difficult to put what I know into my
own words.

6. I do not expect the rules of biological principles to help my understanding of the ideas.

7. To understand biology, I sometimes think about my personal experiences and relate them
to the topic being analyzed.

8. If I get stuck on answering a biology question on my first try, I usually try to figure out a
different way that works.

9. I want to study biology because I want to make a contribution to society.

10. If I don’t remember a particular approach needed for a question on an exam, there’s
nothing much I can do (legally!) to come up with it.

11. If I want to apply a method or idea used for understanding one biological problem to
another problem, the problems must involve very similar situations.

12. I enjoy figuring out answers to biology questions.

13. It is important for the government to approve new scientific ideas before they can be
widely accepted.

14. Learning biology changes my ideas about how the natural world works.

95

15. To learn biology, I only need to memorize facts and definitions.

16. Reasoning skills used to understand biology can be helpful to my everyday life.

17. It is a valuable use of my time to study the fundamental experiments behind biological
ideas.

18. If I had plenty of time, I would take a biology class outside of my major requirements just
for fun.

19. The subject of biology has little relation to what I experience in the real world.

20. There are times I think about or solve a biology question in more than one way to help
my understanding.

21. If I get stuck on a biology question, there is no chance I'll figure it out on my own.

22. When studying biology, I relate the important information to what I already know rather
than just memorizing it the way it is presented.

23. There is usually only one correct approach to solving a biology problem.

24. When I am not pressed for time, I will continue to work on a biology problem until I
understand why something works the way it does.

25. Learning biology that is not directly relevant to or applicable to human health is not
worth my time.

26. Mathematical skills are important for understanding biology.

27. I enjoy explaining biological ideas that I learn about to my friends.

28. We use this statement to discard the survey of people who are not reading the questions.
Please select agree (not strongly agree) for this question to preserve your answers.

29. The general public misunderstands many biological ideas.

30. I do not spend more than a few minutes stuck on a biology question before giving up or
seeking help from someone else.

31. Biological principles are just to be memorized.

32. For me, biology is primarily about learning known facts as opposed to investigating the
unknown.

Abstract (if available)

Abstract This study examined the effect of course delivery mode on academic help-seeking beliefs and behaviors, academic self-efficacy, and the levels of individual interest in biology of students in an entry-level General Education biology course. This intersection of online education, science courses, and academic success factors merits attention because the growing impact of the expansion of online education on undergraduate success, particularly in science courses, has not been fully studied. The specific questions guiding the study examined: whether course delivery mode impacted individual interest in biology

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Asset Metadata

Creator Noll, Christopher B. (author)

Core Title Academic beliefs and behaviors in on-campus and online general education biology classes

School Rossier School of Education

Degree Doctor of Education

Degree Program Education (Leadership)

Publication Date 08/27/2015

Defense Date 05/11/2015

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

Tag academic success factors,College students,comprehensive public university,course modality,general education,help-seeking,Higher education,individual interest,Internet courses,OAI-PMH Harvest,online courses,online education,quantitative study,Science education,self-efficacy

Format application/pdf (imt)

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Creator Email chrisnoll@yahoo.com,cnoll@usc.edu

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