/

University of Southern California Dissertations and Theses

/

Cyber-harassment in higher education: online learning environments

(USC Thesis Other)

Cyber-harassment in higher education: online learning environments

PDF

Transcript (if available)

Content
CYBER-HARASSMENT IN HIGHER EDUCATION:
ONLINE LEARNING ENVIRONMENTS

by

Justin W. Vance

________________________________________________

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

May 2010

Copyright 2010 Justin W. Vance

ii

Soli Deo Gloria

iii
TABLE OF CONTENTS
DEDICATION…………………………………………….......................... ii
LIST OF TABLES……………………………………………………......... v
ABSTRACT…………...………………………………………………….... vi
CHAPTER 1: Introduction………………………………………………… 1
Background of the Problem………………………………………... 2
Statement of the Problem………………………………………....... 13
Purpose of the Study……………………………………………..… 15
Research Questions……………………………………………....… 16
Importance of the Study…………………………………………… 17
Assumptions………………………………………………………... 17
Limitations…………………………………………………………. 17
Delimitations……………………………………………………….. 18
Definition of Terms…………………………………………………18
Organization of the Study………………………………………..… 19

CHAPTER 2: Literature Review………………………………………....... 20
Bullying and Cyber-bullying……...………………………………. 21
Cyber-harassment in Online Learning in College…………………. 24
Gender…………………………………………………………....... 26
Race……………………………………………………………....... 30
Age/SES……………………………………………………………. 31
Status: Student or Instructor……………………………………….. 31
Reporting Rates……………………………………………………. 32
Chapter Two Conclusion..……………………………………..…. 34

CHAPTER 3: Methodology……………………………………………….. 35
Research Questions…………………………………………….. … 35
Design………………………………………………………….. …. 38
Population and Sample…………………………………………….. 38
Instrumentation……………………………………………………. 39
Data Collection…………………………………………………….. 40
Data Analysis………………………………………………………. 41

iv

CHAPTER 4: Findings…………………………………………………….. 42
Description of the Sample…………………………………………. 43
RQ 1A: Cyber-harassment Incident Rate vs. Characteristics…....... 45
RQ 1B: Rate of Cyber-harassment by Type………………………. 52
RQ 1C: Type of Cyber-harassment vs. Characteristics…………… 53
RQ 2A: Reporting of Cyber-harassment vs. Characteristics……… 56
RQ 2B: Rate of Reporting Cyber-harassment by Type………….… 58
RQ 2C: Barriers to Reporting Cyber-harassment………………….. 59
Chapter Four Conclusion…………………………………………... 60

CHAPTER 5: Conclusions………………………………………………… 62
Discussion………………………………………………………..… 62
Implications for Practice………………………………………....… 67
Implications for Research………………………………………..… 70
Chapter Five Conclusion……………………………………….…...72

References……..………………………………………………………........ 73

Appendix: Survey Questions………………………………………………. 80

v

LIST OF TABLES

Table 1: Summary of Analysis……………………………………….......... 37
Table 2: Characteristics of Sample………………………………………… 44
Table 3: Cyber-harassment Rate by Number of Courses………………….. 46
Table 4: Cyber-harassment Rate by Characteristics……………………….. 47
Table 5: Rate of Cyber-harassment by Type………………………………. 52
Table 6: Type of Cyber-harassment Rates by Characteristics…………....... 54
Table 7: Cyber-harassment Reporting Rate by Characteristic…………....... 57
Table 8: Cyber-harassment Reporting Rate by Type………………………. 58
Table 9: Reasons Cyber-harassment not Reported by Percentage………… 59

vi
ABSTRACT
There is burgeoning scholarship relating to cyber-bullying in middle and high
school settings. When cyber-bullying occurs among adults it is known as cyber-
harassment. Online higher education has exploded in terms of growth in the last several
years. Related Literature including university policies and online teaching guides
suggested there may be a cyber-harassment problem in online learning in higher
education as well although no quantitative evidence of cyber-harassment in online
learning in higher education currently exists. The purpose of this study was to use a
quantitative approach to explore the nature of and extent to which students and faculty
experience and report cyber-harassment in and as a result of the online learning settings
of colleges and universities. The study was conducted at a large, private, non-profit,
liberal arts university in Hawaii. The study is based on 225 online student participants
and 56 online faculty participants. Of the participants it was found that 12% of students
and 39% of faculty were the victims of cyber-harassment in or as a result of an online
course. This much higher rate of cyber-harassment for faculty is statistically significant.
Those students and faculty over the age of 35 also suffered cyber-harassment at a higher
and statistically significant rate in or as a result of online courses. Less than half of those
cyber-harassed in this context reported it. Preventions and solutions for practice and
prescriptions for future research are recommended.

1

CHAPTER 1:
Introduction

The stories are now commonplace in the news. Stories of teenagers bullied by
dozens of classmates via the internet until they commit suicide (Long, 2008), women
stalked via the internet which eventually end in real life murder (Tavani & Grodzinsky,
2002), people impersonating people others care about to cause severe trauma or even
suicide of those individuals (Long, 2008), people impersonating someone to post ads that
they want to be raped as a fantasy (Hopkins, January 19, 2005), and intellectuals‘ and
others‘ reputations destroyed by false postings or websites about them on the internet or
even others impersonating them online (Kolowich, 2009). Also alarming is that in most
states, many incidents such as these are not considered serious crimes or crimes at all
(Katyal, 2001, p. 1035).
From Flaming –―derogatory, abusive, or aggressive attacks online‖
("WiredSafety.org — the world's largest Internet safety and help group," 2008) to Cyber-
rape –―forced online sexual activity‖ - (Dibbell, December 23, 1993 ; Lynn, May 4,
2007) and from murder to suicide, Cyber-bullying, Cyber-harassment, and Cyber-stalking
are a reality in and through the internet. According to unscientific surveys, as many as
60% of people using the internet have experienced some form of cyber-
bullying/harassment/stalking ("By the Numbers: Cyberbullying," 2005). Considering
such great potential for harm affecting so many, this phenomenon warrants more
investigation in all venues. There has been little research on cyber-harassment among
adults to systematically determine the extent to which it occurs and which groups are

2

most at risk. In fact, a bill introduced in the U.S. House of Representatives in April 2009
to prevent cyber-bullying was attacked by opponents because, despite the apparent strong
anecdotal evidence, there is no ―data that cyberbullying among adults is an issue" (Kotler,
May 14, 2009). The topic of cyber-harassment among adults in general, however, is too
broad for this study. This study focuses on finding more about what is going on with
cyber-harassment among adults in the growing world of online learning. The purpose of
this dissertation is to explore the nature of and extent to which students and faculty
experience and report cyber-harassment in and as a result of the online learning settings
of colleges and universities.
Background of the Problem
Student enrollment in online learning has increased dramatically and become
increasingly more important to the accessibility to higher education. The 12.9 percent
growth rate from 2007 to 2008 for online enrollments far exceeds the 1.2 percent growth of the
overall higher education student population (Allen & Seaman, 2008, p. 45). From 2004 to 2005
online enrollment increased by 36.5 percent (Allen & Seaman, 2008). Interestingly, 2005 was the
first year that the researcher taught an online course. As a percent of total enrollment in higher
education, online learning has grown from 9.6 percent in 2002 to 21.9 percent in 2007 (Allen &
Seaman, 2008).
Online learning programs are particularly important in the higher education
portfolio because of their capacity for extending access. They provide opportunity for
students who otherwise would be non-consumers if only traditional face to face programs
existed (Gearon, 2008). These students may be working adults, caretakers of dependents,

3

elderly, disabled, live in geographically remote areas, or be deployed in the US Military.
Since the beginning of the War on Terror, and even more after the start of the War in Iraq
in 2003, military deployments and missions have increased dramatically, translating into
a demand for more flexible, asynchronous learning schedules (J. Lyons, 2006). As much
as 75 percent of the higher education that military personnel pursue is now received via
distance learning formats (Lederman, 2008).
Demand for online learning will only increase. Already, statistics show 1/3 of all
college students are over the age of 24 and 40 percent of students are enrolled part-time
(U.S. Department of Education, 2006) with more adults than ever needing to return for
higher education due to increasing demand for a skilled or retooled workforce (Friedman,
2005). Trow (2001) has noted that since World War II, there has been a shift to ―mass higher
education‖ and that due to online opportunities, a shift to universal access to higher education is
at the doorstep. He believes ―the existence of the university as it is now and as we know it
is in doubt‖ (Trow, 2001, p. 112).
In January 2005, the researcher created and taught the first undergraduate History
course offered online by Island Ocean University (IOU), a private liberal arts college
located in Hawaii. By this time, many institutions had offered at least some courses
online for about ten years (Lucas, 2006, p. 320). Indeed, even at IOU, History was one of
the last disciplines to offer at least a general education course online. Resistance to
offering courses and programs online was strong among IOU faculty as is common
among private, liberal arts colleges (Allen & Seaman, 2006).

4

Pedagogical soundness was one of the concerns of the IOU faculty which, like
many in academia, worried that students would not learn as much as they should online
(J. F. Lyons, 2004). Others focused on the threat to existing traditional programs, while
some argued that they did not enter the profession to teach online and wanted nothing to
do with it because they thought they would miss the interaction of the traditional
classroom (J. F. Lyons, 2004). Still more feared that offering courses and programs
online would cause people to associate the school with ―online universities,‖ which suffer
a poor reputation in the academic world (Berg, 2005). However, there is much
scholarship that documents high levels of interactions online and that learning outcomes
can be met in a comparable manner online as well as in-class (U.S. Department of
Education, Office of Planning, Evaluation, and Policy Development, 2009). But all else
aside, the concern that people seem to struggle with the most, and that IOU grappled
with, was that offering courses online would compromise traditional face-to-face
relationships established among students as well as between students and teachers
(Brabazon, 2002).
Despite these skepticisms, social presence has been shown to be a significant
force in the online learning environment (Tu, 2002). The social presence of humans in
online courses has come with the good and the bad of human interaction and
relationships. In terms of the good, students can be challenged by multicultural
interaction to help them to learn and develop (Fluck, Clouse, & Shooshtari, 2007;
Limburg & Clark, 2006; Merryfield, 2001). Specifically, Limburg and Clark have shown
that identity development can occur online. Part of their case study course included

5

helping students ―come to grip first with [their] white identity…to help them learn how to
balance...interactions with diverse students‖ (2006, p. 51).
But if the good can come from this social presence and human interaction so can
the bad. Many may suppose ―that power and oppression dynamics that manifest‖
themselves in the traditional classroom ―-related to race gender and socioeconomic class‖
are invisible in online courses and do not need to be addressed (Limburg & Clark, 2006).
However Limburg and Clark have argued that oppression dynamics are not neutralized in
the online classroom simply by the course being virtual. For example, student names
lead ―to assumptions about race, ethnicity, nationality, and gender both by faculty and
other students‖ (Limburg & Clark, 2006, p. 49). They also show that even in an online
communication venue, white students, male students, and socio-economically-privileged
students usually speak (write) more prolifically, forcefully, and with greater confidence,
whereas students of color, female students, and working-class students tend to speak less,
more deferently, and with greater caution‖ (Limburg & Clark, 2006, p. 49). With these
power dynamics persisting, along with other traditional reasons humans are not always
kind, there is the potential for students to harass one another inside an online course or
for harassing behavior to originate from within an online course.
The term applied when one adult harasses another on the internet is cyber-
harassment ("State of New York Division of Criminal Justice Services," 2008). The
more commonly heard term of cyber-bullying is by definition only applied to cases where
one child targets another child ("WiredSafety.org — the world's largest Internet safety
and help group," 2008). Cyber-stalking could also become a possibility via an online

6

learning setting. Cyber-stalking refers to harassment moving from online to offline
―which can lead to the actual physical harm of the victim‖ ("State of New York Division
of Criminal Justice Services," 2008). The National Center for Victims of Crime (NCVC)
notes the following forms of cyber-harassment/stalking:
* Threatening, obscene and/or unsolicited e-mail, text messages or other
electronic communication.

* Spamming. Offenders sometimes sign their victims up at Web sites that will
"spam" them with pornographic or marketing material.

* Live chat harassment or "flaming" (online verbal abuse).

* Improper messages left on message boards or guestbooks.

* Sending malicious code. Viruses, spyware and hacking programs can be used
either to crash a victim's computer or to spy on the victim.

* Tracing another person's computer and Internet activity; identity theft. "The
hacking is a method to control and aggravate the victim," Fisher says. "It also
allows a perp to gain more knowledge of his/her victim.

The NCVC warns that cyber stalking can escalate into offline stalking, including
the behavior most people associate with stalking: abusive and/or excessive phone
calls, vandalism, threatening or obscene mail, trespassing and physical assault
(Miller, 2006).

Bullying is a serious problem in educational settings negatively affecting
development (Benbenishty & Astor, 2005; Olweus, 1993). Like traditional bullying,
much of the attention and research regarding bullying and harassment online have been
on teenagers and pre-teenagers. It has been well established in the last few years through
studies and statistics, that online harassment among teens and pre-teens, known as cyber-
bullying ("WiredSafety.org — the world's largest Internet safety and help group," 2008),
is a major problem. Studies conducted, some of which are discussed in more detail in

7

Chapter 2, show that the number of teenagers cyber-bullied is significant, ranging from
around 25% to 75% depending on the population and approach of the studies (Beran &
Li, 2005; Juvonen & Gross, 2008; Lenhart, 2007; Li, 2006, 2007; Shariff, 2008; Smith et
al., 2008). Also of note is that reporting of such incidents was found to be as low as 10%
(Juvonen & Gross, 2008).
Cyber-bullying can include attacks through such means as email/instant
messaging/text messaging harassment, stealing passwords, blogs (online journals where
information can be posted to tarnish another‘s reputation), web sites (created specifically
to insult and post humiliating photos or information about a student), sending pictures
through e-mail and cell phones, internet polling (to rate other students), interactive
gaming, sending malicious code (viruses), sending pornography and other junk e-mail
and IMs, and impersonation ("Stop Cyber-bullying," 2008). The researcher personally
experienced the last of these, impersonation, as a teen. Additionally, ―Unlike the
schoolyard bully of yesteryear, the cyberbully can hide behind online anonymity and
attack around the clock, invading the privacy of a teen's home‖ (Long, 2008). There is
seemingly no escape for the victim as a student cannot simply leave the school yard and
often the attacks are relentless and driven by multiple perpetrators ("Stop Cyber-
bullying," 2008). Complicating the matter is that those perpetrating the offenses may be
more likely to bully due to the perceived anonymity and perhaps a belief that the offense
is not as serious since the offenses are not carried out in ―real life‖ ("Stop Cyber-
bullying," 2008).

8

Cyber-bullying can also become physically dangerous. A 13 year old student
named Ryan Halligan
received e-mails and instant messages day and night from classmates ridiculing
him and calling him a loser. When a pretty girl at school pretended to like him
online but later revealed she was only joking, the taunting e-mails and instant
messages increased, only with even more venom. In October 2003, Ryan hanged
himself (Long, 2008).

In 2007,

Cyberbullying captured national attention…when the story broke of 13-year-old
Megan Meier, a Missouri girl who killed herself after an Internet hoax in which a
fictitious "cute boy" was created by the mother and sister of Megan's classmate.
The boy befriended Megan on the social networking site MySpace, but when he
suddenly ganged up on her online with her friends, Megan crumbled, reminding
everyone how vulnerable teenagers are to social pressure and how the agony of
being singled out escalates with the wider forum provided by technology (Long,
2008).

These are just two examples from a long list of tragedies in recent years. Not all
incidents end so tragically as these examples, but cyber-bullying certainly creates
unwelcome physical school environments where equal opportunities to learn are greatly
reduced (Shariff, 2003).
Another area where the seriousness of cyber-harassment is prompting study is that
of its law and justice implications. There are legal and ethical perspectives published on
the problem of cyber-harassment and cyber-stalking. While the terms cyber-harassment
and cyber-stalking are often used interchangeably and often use the same online tactics,
cyber-stalking ―is almost always characterized by the stalker relentlessly pursuing his\her
victim online and is much more likely to include some form of offline attack, as well‖
("WiredSafety.org — the world's largest Internet safety and help group," 2008). Tavani

9

and Grodzinky (2002) analyzed the ethical aspects of cyber-stalking using the Amy
Boyer/Liam Youens case of cyber-stalking wherein Boyer was murdered in 1999 by
Youens who had stalked her online. The three aspects analyzed were (a) the privacy of
stalking victims is being threatened because of the relatively unrestricted access to online
personal information; (b) the moral responsibility and legal liability for internet service
providers (ISPs) when stalking crimes occur in their jurisdiction; and (c) the moral
responsibility of internet users to inform persons whom they discover to be the targets of
cyber-stalkers. They conclude that although it seems ethical to protect victims in these
three aspects of cyber-stalking, current laws do not protect them because either (a) the
laws privilege free speech, and do not distinguish harassing speech from free speech; or
(b) the problem just has not been established in the courts yet. This lack of protection
from the law regarding cyber-harassment is a reoccurring them in the literature.
The University of Pennsylvania Law Review dedicated an entire issue of their
journal to ―Criminal Law in Cyberspace‖ in 2001. Cyber-stalking was one of the
categories covered. Some interesting assertions from the issue that differentiate cyber-
stalking from traditional stalking were that ―an anonymous stalker is harder to catch‖ and
―the lack of an in-person confrontation also makes intent harder to presume or ascertain‖
(Katyal, 2001, p. 1035). Of further note too, most laws that do exist to protect victims
from cyber-stalking protect them only from direct physical threats. They do not protect
them from perpetrators using the internet to solicit third party harassment of which many
cases have been reported. For example:

10

last year a former security guard pled guilty, under California law, to stalking and
solicitation of sexual assault for using the internet to solicit a rape. A woman had
rejected the guard's romantic overtures, and, in retaliation he impersonated her in
chat rooms, posting her phone number, address, and fake messages detailing how
she fantasized about being raped. As a result, on at least six occasions, at times
late at night, men knocked on her door saying they wanted to rape her (Katyal,
2001, p. 1035).

Shariff and Johnny (2007) reviewed case law in the U.S. and Canada and found
that the Canadian courts were more likely than U.S. courts to support cyber-bullied
victims. They found that Canadian courts have placed more emphasis on a positive
school environment (whether physical or virtual). They also found cases that confirmed
schools‘ authority to remove student privileges when school computers or websites are
used to cyber-bully. As in many of the articles, the authors recommended development
of policy guidelines that guide educators in navigating the complexities of cyber-bullying
(Shariff & Johnny, 2007).
A few studies and reports have also been assembled that conclude cyber-stalking
is a growing issue for law enforcement ("1999 Report on Cyberstalking: A New
Challenge for Law Enforcement and Industry,"; Bocij, 2004; D‘Ovidio & Doyle, 2003;
B. Fisher, Cullen, & Turner, 2000). They also suggest that a significant amount of
stalking incidents use cyber-stalking methods, perhaps 25%-40%. These studies agree as
in, traditional stalking that women are more at risk. Interestingly, Caucasians seem
overrepresented in cyber-crime. No conclusions are made but perhaps this is related to
higher socioeconomic status which may be connected to access to the tools for cyber-
crime.

11

There is also some literature that may be of interest to student affairs
administrators on college campuses. In a Chronicle of Higher Education article, O‘Neil
(2008) broached the implications cyber-harassment may have on higher education and
posed more questions than answers about what can be done to combat it and who, if
anyone, is liable. One disturbing website he mentioned in regards to potential cyber-
harassment was www.juicycampus.com , a website which advertised to college students
that it ―is the place to spill the juice about all the crazy stuff going on at your campus. It‘s
totally anonymous - no registration, login, or email verification required‖
("juicycampus.com," 2008). Juicycampus.com shutdown in February 2009 reportedly
due the economic recession but they were also under pressure from the state of New
Jersey for failing to comply with state regulations that ―users agree not to post abusive or
obscene content‖ ("College gossip web site shuts down, citing economy," February 5,
2009). However, other websites like it or ones created by students, themselves, still exist.
At least two studies have been conducted at college campuses to attempt to
capture the extent cyber-harassment/cyber-stalking on campus (Alexy, Burgess, Baker, &
Smoyak, 2005; Finn, 2004). Finn (2004) looked more at cyber-harassment and found 10-
15% of students had experienced it while Alexy (2005) focused on cyber-stalking and
found a victim rate of about 4%. These studies also reveal reporting as an issue.
Alarmingly, Finn (2004) determined that only 7% of those he found cyber-harassed had
reported.
In addition to harassment of students through online media, there has also been a
rise in the abuse of faculty and staff in online forums such as MySpace.com,

12

RateMyProfessor.com, YouTube, and other public sites (Shepherd, 2006). Binns (2007)
states,
ridiculing lecturers by criticizing their courses - and even their appearance and
personal habits - has entered a new dimension. The growth of user-generated
content websites, such as YouTube and MySpace, has propelled students' private
in-jokes into a very public sphere. What may have once amounted to graffiti on
the campus toilet for a small local audience has exploded online for all the world
to see. And the consequences for academics are certainly not funny.

Recently some prominent academics and their research were cyber-harassed for a three
year period before the perpetrator, the well educated son of a rival researcher of the same
topic, was caught (Kolowich, 2009). The cyber-criminal used impersonation, plagiarism
accusations, and ―an army of aliases in e-mail messages, blogs, online discussion groups,
and Wikipedia to discredit him and his work‖ (Kolowich, 2009).
In Community College Week, Goldsborough (2004) wrote an article outlining a
few organizations and resources people could go to for help if they were being cyber-
harassed. In addition some institutions are adding statements applicable to cyber-
harassment to their student handbooks.
It is clear that cyber-bullying, cyber-harassment, and cyber-stalking are serious
issues. Although still relatively new issues, they are not being taken lightly in middle and
high school settings and in the criminal and civil justice discourse. There is a burgeoning
quantity of scholarship in those areas in regards to these issues. There is also some study
and discussion of it in higher education. Although there has been some exploration of
cyber-harassment in these areas that help inform it, there is little scholarship specifically
in the fast emerging context of online learning in higher education.

13

Statement of the Problem
Bullying, more commonly known as harassment when among adults, does not
magically end when an individual graduates from high school. In fact, Chapell (2007)
has shown a ―positive correlation between having been bullied in college and in high
school.‖ Chapell‘s earlier study demonstrated that 18.5% of students have been bullied
at least once in college, a rate comparable to bullying rates among teens (Chapell et al.,
2004). That harassment does not disappear upon high school graduation is also
consistent with studies that show bullying among adults is common at the workplace
(Glendenning, 2001).
Since bullying does not end at high school graduation, since up to 72% of youths
have experienced cyber-bullying (Juvonen & Gross, 2008), and since as many as 60% of
people in general have experienced some form of cyber- bullying/harassment/stalking
("By the Numbers: Cyberbullying," 2005), it stands to reason that cyber-harassment
exists to some extent in the online learning environment at the college level as well.
Although the subject is commonly talked about, little research on it exists.

14

In his book about the legal ramifications of computers and the internet on campus,
Hawke (2001) helps to define the issue at least in terms of harassing speech in online
courses stating,
―Conflict may arise when electronic ―speech,‖ which is not proscribable,
nevertheless creates a hostile or offensive environment for a particular class of
users. Internet speech can be discriminatory on ethnic, racial, or religious
grounds; however, in light of the proliferation of sexual material available, a
dominant concern appears to be sexual harassment‖ (2001, p. 63).

Almost every book about how to teach online, such as Teaching Online: A
Practical Guide (Ko & Rossen, 2004) and The Virtual Student (Palloff & Pratt, 2003),
includes a few paragraphs on how to avert and deal with harassment or inappropriate
behavior that may develop in an online course. One of the most popular of such online
teaching guides is The Virtual Student by Palloff and Pratt (2003). They suggest
including guidelines for students about what constitutes harassment in one‘s online
course and making clear it will not be tolerated, along with providing the penalties for
violation. They also state that,
harassment is not limited to the discussion board of the online classroom.
Stalking and harassment have occurred through email. If a student reports that he
or she is being stalked by a classmate, swift action must be taken in order not only
to protect the student being stalked but to preserve the safety of the learning
community (Palloff & Pratt, 2003, p. 105).

Palloff and Pratt also warn of the problem of students posting pornography within an
online course and urge that it be removed as promptly as possible and disciplinary action
taken such as removing the guilty student‘s access to the course. They also offer advice
on ―flaming.‖ Flaming is essentially ―fighting words‖ in the virtual environment. Palloff
and Pratt state that when flaming occurs ―students generally report feeling unsafe and

15

insecure and that they can no longer express themselves freely for fear of retaliation‖
(2003, p. 106). They also argue that flaming could easily turn into a face to face
confrontation offline if not managed properly. Overall, they argue for learning to take
place, a sense of safety and security needs to be created in the course.
Machanic (October 1998, p. 3) addressed what she had recognized in the way of
―informal reports of flaming that got out of hand, as well as some forms of sexual
harassment‖ in online classrooms offering suggestions to address ―the emerging issues of
power, gender, and safety in online classrooms.‖ But like the online teaching guides
described above, her article is based on anecdotal evidence. Since no formal studies to
gauge the nature and extent of cyber-harassment in online courses exist, no one has a
clear picture of the problem. Compounding this problem, is that the studies that have
been done in other venues, indicate that incidents going unreported is an issue (Finn,
2004; Juvonen & Gross, 2008) which means that the phenomenon is getting even less
attention than deserved. As with teen and pre-teen cyber-bullying studies, data needed to
be collected and analyzed from online college courses to see to what extent cyber-
harassment is taking place in online learning and to investigate the nature of it.
Preventions and solutions can be more effectively devised if the exact character of the
problem is known.
Purpose of the Study
Although discussion of what to do about cyber-harassment in online courses is
common, there has been no previous systematic study on the nature and extent of the
problem. The purpose of this study was to explore the nature of and extent to which

16

students and faculty experience and report cyber-harassment in and as a result of the
online learning settings of colleges and universities.
Research Questions
Specifically, this study addresses the following:
1. To what extent is cyber-harassment (dependent variable) occurring within or
was the continuation/result of something that happened in an ONLINE
course?
a. How do incident rates vary by the victim‘s gender, race,
student/faculty status (independent variables)?
b. How do incident rates vary by type of harassment?
c. How does type of harassment vary by characteristics (gender, race,
status) of the victim?
2. At what rate is reporting of cyber-harassment within or was the
continuation/result of something that happened in an ONLINE course
occurring?
a. How does reporting vary by the victim‘s gender, race, student/faculty
status (independent variables)?
b. What types of cyber-harassment are most likely to be reported?
c. What are the barriers to reporting of the cyber-harassment?
Importance of the Study
It is valuable to learn the extent and nature of cyber-harassment and reporting of it
in the online education setting in college as currently they are largely unknown. For

17

online learning to be successful, ―then all participants must be interacting with each
other‖ (Machanic, 1998, p. 4). For that to occur, ―all participants must feel safe‖ and if
students ―do not feel safe, they will not interact fully in the online classroom, and less
learning will occur, not only for those who do not feel safe, but for those who are
deprived of hearing the different perspectives of those who are silenced‖ (Machanic,
1998, p. 4). This study is useful to universities around the world with significant or fast
growing online programs to illuminate what level of problem exists in this area. Only by
having information such as this will effective action be possible to alleviate cyber-
harassment in online learning and deeper study of the phenomenon be possible.
Assumptions
The following assumptions were made for this study: (a) participants were honest
when filling out the questionnaire; (b) participants were capable of understanding and
answering the questionnaire; (c) participants responded to the questionnaire to the best of
their ability; (d) participants had sufficient proficiency in English to fill out the survey;
(e) the measures were reliable and valid indicators of the constructs to be studied; (f) the
results were collected and analyzed with appropriate research protocols and statistical
methods; and (g) the results of this study were sufficiently comprehensive to include an
array of college students taking online courses.
Limitations
There are a few limitations of the study. First, the validity of the study is limited
to the reliability of the instruments used. The study was limited to subjects who agreed to

18

participate voluntarily. Finally, the study was limited to the number of subjects surveyed
and the amount of time available to conduct the study.
Delimitations
This study was delimited to a large population of students taking online courses at
a private liberal arts institution who agreed to participate voluntarily. Based on these
delimitations, the generality of the findings is limited.
Definition of Terms
Throughout this document a number of issue specific terms are used.
Complicating this matter is that the vocabulary used to discuss the issue is still relatively
new and full consensus does not yet exist on it. However, the researcher has attempted to
use the definitions for which there is the most consensus:
Avatar- A internet user‘s representation of themselves. It could refer to an image
but more often a 3-D manifestation of the user in a virtual world.

Cyber-bullying- When a child, preteen or teen is tormented, threatened, harassed,
humiliated, embarrassed or otherwise targeted by another child, preteen or teen
using the Internet, interactive and digital technologies or mobile phones. It has to
have a minor on both sides, or at least have been instigated by a minor against
another minor. Once adults become involved, it is plain and simple cyber-
harassment or cyber-stalking ("Stop Cyber-bullying," 2008).

Cyber-harassment- When one adult harasses another on the internet ("State of
New York Division of Criminal Justice Services," 2008) (note sometimes cyber-
harassment and cyber-stalking are used interchangeably in the literature)

Cyber-rape- Forced online sexual activity (Dibbell, December 23, 1993 ; Lynn,
May 4, 2007).

Cyber-stalking- Cyber-stalking refers to harassment or danger potentially moving
from online to offline which can lead to the actual physical harm of the victim
("State of New York Division of Criminal Justice Services," 2008).

19

Flaming- This is what happens when a discussion gets out of hand and one or
more of the parties involved uses derogatory, abusive or aggressive tactics
("WiredSafety.org — the world's largest Internet safety and help group," 2008).

Lurking- This is when someone reads messages posted to Newsgroups or Forums
without participating ("WiredSafety.org — the world's largest Internet safety and
help group," 2008).

Organization of the Study
Chapter 1 included the introduction, background of the problem, statement of the
problem, purpose of the study, research questions, the importance of the study,
assumptions, limitations, delimitations, and the definition of terms.
Chapter 2 is the review of the relevant literature. It addresses the following
topics: bullying and cyber-bullying, cyber-harassment in online learning, cyber-
harassment in terms of gender, race, age, socio-economic status (SES), and being a
student or faculty member and reporting rates and barriers to reporting based on these
characteristics.
Chapter 3 presents the methodology used in the study, including population and
sampling procedure, and the instruments and their selection or development. The chapter
also contains the procedures for data collection and analysis.
Chapter 4 presents the findings of the study organized by the research questions
as well as a discussion of those findings.
Chapter 5 includes a summary of the findings and draws together the conclusions
and implications resulting from the study.

20

Chapter 2:
Literature Review

Cyber-harassment is a serious problem and has drawn national headlines in recent
years. Known by its more popular name as cyber-bullying when it takes place between
teens and pre-teens, cyber-harassment and cyber-bullying can include attacks through
such means as email/instant messaging/text messaging harassment, stealing passwords,
blogs (online journals where information can be posted to tarnish another‘s reputation),
web sites (created specifically to insult and post humiliating photos or information about
a student), sending pictures through e-mail and cell phones, internet polling (to rate other
students), interactive gaming, sending malicious code (viruses), sending pornography and
other junk e-mail and IMs, and impersonation ("Stop Cyber-bullying," 2008). Cyber-
stalking refers to when cyber-harassment escalates into possible physical danger ("State
of New York Division of Criminal Justice Services," 2008). This study aims to explore
the extent and nature of cyber-harassment in online learning in higher education.
Included in this chapter is a review of the literature that informs the study and further
demonstrates why this study was needed.
The review begins with an overview and critique of the available studies and
literature which reveal the extent of the problem and informs why a study of cyber-
harassment in online learning is justified and how it should be conducted. It then looks at
how the relevant literature informs several variables that will be used to explore the
nature of cyber-harassment online starting with gender. Next are race, age, and SES
followed by how being a student or faculty member affects chances of victimization.

21

Lastly, relevant literature on reporting rates and reasons why cyber-harassment may not
reported is reviewed in the context of gender, race, age, SES, and faculty/student status.
Bullying and Cyber-bullying
Since no formal studies of cyber-harassment in online learning in college exist, the
most pertinent studies to inform such a study are the burgeoning number of studies on
cyber-bullying among teens and pre-teens and a few studies that have been conducted on
bullying and cyber-harassment in college. Olweus (1993) influences most of these
studies either in terms of his theory, his survey questionnaire, or both. He was the first to
systematically study bullying in the early 1970s. His concept of bullying includes three
main components:
1. Bullying is aggressive behavior that involves unwanted, negative actions.
2. Bullying involves a pattern of behavior repeated over time.
3. Bullying involves an imbalance of power or strength (Olweus, 1993).
Olweus lists nine forms that bullying can take. He now includes cyber-bullying as one
of those forms ("Olweus Bullying Prevention Program," 2009).
Qing Li has been involved in a number of scholarly studies of cyber-bullying. In
2005, Beran and Li conducted a study with 432 Canadian students from grades 7 to 9.
Beran and Li (2005) began their fifteen minute survey with Olweus‘ definition of
bullying and then ask if students had experienced this kind of harassment through
technology. Giving the students a description of what constituted bullying rather than
simply asking if they had been cyber-bullied seemed an effective approach. Closed
ended questions were used for frequency. 21% reported having been harassed several

22

times. Their study also aimed to find what mediums were used to cyber-bully using
open ended questions for that and questions with responses in a Likert scale to
determine what effect the harassment had. One quarter to one third of those harassed
reported feeling sad/hurt, anxious, embarrassed, expressed fear, and blamed
themselves and/ or cried. In 34% of the students, the bullying resulted in poor
concentration in school, 11% resulted in low school achievement, and 9% in
absenteeism. There was a 64% overlap between students who were harassed in
traditional setting and those who reported being harassed online (Beran & Li, 2005).
The sample seems valid, although in Canada, as 9 ―diverse‖ schools in the Calgary
area were randomly selected to participate. Li also conducted a study in 2007 where
177 students at 2 junior high schools were surveyed (2007). The results were
comparable but showed the cyber-bullying rates may be increasing as 25% of victims
reported being cyber-bullied. This time Li used the term cyber-bully in the
instrument, however, the schools were known for being ―high tech‖ so maybe it was
appropriate in this setting.
Juvonen and Gross (2008), in a study that included 1,254 students ages 12 to 17,
found that 72% of students surveyed experienced at least one online bullying incident.
19% cyber-bullied 7 or more times. 73% knew or think they knew the perpetrator. These
experiences were independently associated with increased social anxiety. Juvonen and
Gross (2008) did not mention cyber-bullying in their anonymous web-based survey.
Instead they use phrases such as ―mean things‖ instead to prevent self selection.

23

Smith (Smith et al., 2008) looked at 2 sets of data on 11-16 year olds in the United
Kingdom. 92 students from 14 schools and 533 students from 5 schools to assess the
generalizability of the findings and it was comparable. 22% had been cyber-bullied in
last couple months. A focus group finding from this study found that cyber-bullying was
most commonly committed for entertainment.
The Second Youth Internet Safety Survey was a national telephone survey of a
random sizable sample of 1,500 internet users between the ages of 10 and 17 years
conducted between March and June 2005 (Ybarra, Mitchell, Wolak, & Finkelhor, 2006).
It determined that 9% of respondents had been targets of online harassment but only
asked about the previous two months. Respondents needed only to have used the internet
once per month which seems a low criteria as many teens use the internet 30 hours per
week ("Many Teens Spend 30 Hours A Week On 'Screen Time' During High School,"
2008).
Since Olweus defines bullying as repeated, it is construed around 20%-30% is the
portion of teens and pre-teens who are being victimized by cyber-bullying on average
based on an average of the above studies.
Chapell (2004) surveyed total of 1,025 undergraduate students (151 freshmen, 250
sophomores, 295 juniors, and 329 seniors) at a northeastern public university (with a total
enrollment of 8,157 undergraduates) The survey was voluntarily. A total of 18.5%
reported having been bullied in college by another student once or twice, with 5% having
been bullied occasionally and 1.1% very frequently. Not too much less than teens. As a
result Chapell shows bullying graduates to college and recommends giving greater

24

attention to the problem. If bullying graduates to college then cyber-bullying likely does
as well.
Finn (2004) surveyed 339 students at the University of New Hampshire which has
a student body of about 10,000. The survey he used avoided the term cyber-stalking and
asked about behaviors they had experienced. He found that 10% to 15% of students
reported receiving repeated e-mail or instant messages that "threatened, insulted, or
harassed," and more than half of the students received unwanted pornography.
While none of these studies specifically addressed cyber-harassment in online
learning, they suggest that cyber-harassment is taking place in that context as well. They
also help define the problem, and offer theoretical and methodological approaches to
studying the problem in the context of online learning.
Cyber-harassment in Online Learning in College
Online teaching books (Ko & Rossen, 2004; Palloff & Pratt, 2003) and online
teaching workshops (Amada, 2009) on dealing with cyber-harassment abound. Some
forward thinking schools have adopted online learning student conduct policies ("Student
Conduct Policy for Online Programs," 2009). But what are these guidelines,
recommendations, and policies based on? It seems they are based only on common
anecdotal evidence and trial and error. No systematic studies of cyber-harassment in
online learning environments were located.
As noted in chapter one definitions of cyber-harassment in online learning exist.
Hawke (2001) helped define the issue writing, ―Conflict may arise when electronic
―speech,‖ which is not proscribable, nevertheless creates a hostile or offensive

25

environment for a particular class of users. Internet speech can be discriminatory on
ethnic, racial, or religious grounds; however, in light of the proliferation of sexual
material available, a dominant concern appears to be sexual harassment‖ (2001, p. 63).
Books that share best practices for training online and serve as a guide can be found at
any bookstore or library and most dedicate a small section to the problem of harassment
or disruptive students. In The Virtual Student, Palloff and Pratt (2003) suggest faculty
include guidelines for students about what constitutes harassment in an online course and
make clear it will not be tolerated along with providing the penalties for violation. They
also warn that harassment in an online class can escalate to stalking or violence offline
and that creating a safe environment is essential to learning (Palloff & Pratt, 2003, p.
105). Their view of the problem must be based on personal experiences as they cite no
studies or even case studies of cyber-harassment emanating from online courses.
Ko and Rossen (2004) is another popular guide on teaching online written by
experts. They offer advice on how to deal with behavioral problems online that range
from diffusing the situation with responding calmly to emails to notifying the department
head. They base the advice, or the need for it, on no evidence whatsoever as far as can be
seen from the book (Ko & Rossen, 2004, pp. 229-238).
A few schools have adopted student conduct policies for online programs to stave
off any problems. This suggests that there is a problem that university administrators
have been encountering and the problem needs to be researched or documented
systematically. The following student conduct policy for an online program comes from
Allan Hancock College:

26

In addition to the on-campus student conduct policy, students enrolled in the
online program are expected to demonstrate the same tolerance, respect, and
understanding that would prevail in any campus situation. All online users are
expected to support the same respect for individuals, commitment to issue and
problem resolution, and open communication and feedback as in the face-to-face
environment.

Specifically, online students are expected to:
 Accept responsibility and accountability for all use actions and content posted to
any online classroom, chat room or personal inbox (email).
 Maintain the same ethical standards expected in a collaborative, academic
environment.
 Demonstrate respect for all faculty, students, and staff regardless of age, race,
gender, religion, national origin, veteran‘s status, disability, or sexual orientation.

In the online environment, the following will not be tolerated:
 Harmful, threatening, libelous, or abusive content
 Profanity of any kind
 Copyright infringement or violation of patent, trademark, proprietary information,
or confidentiality agreements or plagiarism
 Misrepresentation of identity through alteration of inbox (email) names
 Posting unsolicited advertisements to public forums or private inboxes (no
spamming)
 Transferring computer viruses, intentionally or unintentionally, or other code that
disrupts or interferes with other users' use of the online environment or personal
computers, systems, or networks ("Student Conduct Policy for Online Programs,"
2009).

What is missing is a clear picture of the problem so faculty and administrators know
exactly what they are dealing and more effectively address it, ideally in a preventative
manner.
Gender
Gender is a category that should be looked at when exploring the nature of cyber-
harassment in online courses. In Beran‘s and Li‘s 2005 cyber-bully study noted earlier,
no statistically significant differences in victimization were found for gender (Beran &
Li, 2005). In 2006, Li performed a study specifically aimed at researching gender

27

differences in the nature of cyber-bullying (Li, 2006). The survey included two major
areas, demographic data and experience related to cyber-bullying. A survey study of 264
students from three junior high schools was conducted. 25% had been cyber-bullied but
there were no differences between male and females, which matched her 2005 study. She
did find that males were significantly more likely to cyber-bully (40% to 27%). In her
2007 study, also detailed earlier, Li did find that females were slightly more likely to be
cyber-bullied than males as 60% of the sample cyber-bullied were female. Additionally,
males were slightly more likely to say they cyber-bullied at 52% of the sample (Li,
2007). One conception reported often in the popular media is that girls are more likely to
cyber-bully because they prefer the less direct or less confrontational way to bully
(Winchester, 2009). Though Li (2006) and Smith (2008) both looked at this, neither
found evidence to support this belief in their studies.
The results from a nationally-representative phone survey of 935 teenagers by the
Pew Internet & American Life Project included a section called The Gender Gap
(Lenhart, 2007). It found girls are more likely than boys to say that they have ever
experienced cyber-bullying. 38% of online girls report being bullied compared with 26%
of boys. Taking all the studies on the whole though, it does not seem like cyber-bullying
affects one sex more than the other.
Shasheen Shariff (2008) argues, however, that a gender effect may just not be
coming out in all the studies because sexual harassment has been so normalized into
society. Shariff‘s recent monograph that synthesizes available scholarship, law and
policy, issues, and solutions on cyber-bullying addresses this issue (Shariff, 2008). She

28

states ―Females are more often targets of sexual harassment, cyber-threats, cyberstalking
and unsolicited pornographic materials.‖ (Shariff, 2008, p. 90) ―Sexual harassment is so
normalized that both genders often fail to report it.‖ She argues this is ―why narrow
definitions of bullying or cyber-bullying might miss important statistics on sexual,
homophobic, or racial harassment‖ (Shariff, 2008, p. 95).
Chapell‘s (2004; Chapell et al., 2007) bullying in college studies also inform the
topic of gender. He found males bullied other students significantly more than did
females (Chapell et al., 2004). Male and female college students reported having been
bullied equally (by other students as well as by teachers) (Chapell et al., 2004). Chapell
et al.‘s 2007 study, included a total of 119 undergraduates from a large eastern university
and used a definition of bullying at the beginning of the survey (Chapell et al., 2007).
They found no significant sex differences in verbal, physical, or social bullying by bullies
in college, high school or elementary school. Also, ―no significant sex differences in the
frequency of being a bully or a bully-victim in college, high school or elementary school‖
were found (Chapell et al., 2007). However, male students were bullied significantly
more than female students in elementary school and high school, a finding they states, ―is
consistent with many national studies of American public and private elementary and
high school students‖ (Chapell et al., 2007). Chapell et al.‘s findings suggest that
bullying of females seems to increase in adults whereas males had been bigger targets in
younger people.
In terms of the cyber-stalking studies from the Criminal Justice perspective,
women are considered to make up the vast majority of stalking victims ("1999 Report on

29

Cyberstalking: A New Challenge for Law Enforcement and Industry,"; Bocij, 2004).
However, Alexy (2005) found that although this was true, of those men who were
stalked, they were significantly more likely to have been cyber-stalked than women.
Machanic (1998) argues with strong theoretical backing regarding gender and
power that sexism and sexual harassment has extended from traditional settings onto the
internet and even online courses. She goes beyond text and even video communication
and invokes the case of 3-D worlds where physical proximity also needs to be controlled.
Second Life is one virtual world now being used for education where students and
instructors are represented by 3-D manifestations of themselves known as avatars
("Virtual Environments Enable New Models of Learning," 2009). Herring (October
2001) echoes Machanic stating,
Gender differences in online communication tend to disfavor women. In mixed-
sex public discussion groups, females post fewer messages, and are less likely to
persist in posting when their messages receive no response. Even when they
persist, they receive fewer responses from others (both females and males), and
do not control the topic or the terms of the discussion except in groups where
women make up a clear majority of participants forthcoming. The lesser
influence exercised by women in mixed-sex groups accounts in part for why
women-centered and women-only online groups are common whereas explicitly
designated men-only groups are rare.

Although the systematic studies on cyber-bullying in teens and pre-teens have not
picked up major gender differences in victimization, two major reasons seem to come
forward that it is still an important area to look at when studying cyber-harassment in
online college courses. One, harassment, especially sexual harassment, and stalking
become more commonly directed at females after adolescence and adulthood (Shariff,
2008), (Chapell et al., 2007) which is the population that will be studied. Two, sexism

30

and sexual harassment may be so normalized and institutionalized they may not have
been picked up on in earlier studies.
Race
Many of the cyber-bullying studies discussed in this chapter included sample
populations that were predominantly white. In Li‘s 2007 study, 60% of the cyber-bully
victims were white and 70% of the cyber-bullies were white. For the most part, these
demographics reflected ethnic distribution of the sample (Li, 2007). One of the largest
cyber-bullying studies discussed earlier also looked at and reported no difference in race
(Ybarra et al., 2006). Only one study reported what may be a significant difference in
terms of race in which 32% of white teens said bullying happens more often online, while
18% of African-American teens thought it happened more often online (Lenhart, 2007).
Benbenishty and Astor (2005, p. 43) seem to corroborate these in their work with
traditional bullying, finding that patterns of victimization within different racial groups
are consistent. This said, some of the cyber-stalking literature from the criminal justice
perspective suggested Caucasians were more likely to be victims of cyber-stalking
(Alexy et al., 2005; D‘Ovidio & Doyle, 2003), D‘Ovidio and Doyle (2003) reporting
85% of victims were Caucasian in New York City between 1996 and 2000. However,
theirs was an analysis of reported crimes and, though worth considering, is not directly
comparable. Although race does not point to being a major variable in the cyber-bullying
studies available, this may because many of the samples were not diverse enough to get at
any problem.

31

Although a common misconception may be that race is hidden online, Limburg
and Clark‘s (2006) work in multicultural education online has shown this is not the case.
Power dynamics manifest themselves in the assertiveness of those interacting online and
in names to enumerate some examples. Looking at victimization frequency by race may
be useful. The sample population for this study is also relatively diverse as it was
conducted in Hawaii.
Age and SES
Although age group and socio-economic status (SES) differences have not been
relevant in the teen and pre-teen cyber-bullying studies that inform this study, when
looking at cyber-harassment in online learning in college, it seems appropriate to look at
these characteristics as well to see if any relationships between them and cyber-
harassment exist. Household income is used to determine SES based on U.S. Census
categories. Of most significance to the age characteristics is whether the students are
traditional college age (18-22) or non-traditional. Also of interest is to see if students
who are much older (over 35) are as effected since internet use would not have been
common before they were adults.
Status: Student or Instructor
Whether a cyber-harassment victim is a teacher or student is also a useful lens with
which to look at cyber-harassment in online learning. In Canada, an Ontario Teachers
Association Survey found 84% of teachers had been defamed on social networking sites
(Shariff, 2008). Shepherd (2006) discusses the concern by academics of harassment
online where students post about professors on sites such as RateMyProfessor.com.

32

Binns (2007) cites a number of examples where harassment of faculty has taken place on
websites such as YouTube and MySpace. She states ―What may have once amounted to
graffiti on the campus toilet for a small local audience has exploded online for all the
world to see‖ (Binns, 2007). Faculty are clearly affected by cyber-harassment as well
and are an important aspect of finding out about the nature of it in online learning.
Interestingly, in Chapell‘s (2004) study, a total of 14.5% of college students
reported having been bullied by a college teacher once or twice. Looking at who
perpetrators are, however, is beyond the scope of this study.
Reporting Rates and Barriers
Determining to what extent cyber-harassment in online learning is being reported
and what barriers may exist to reporting is another aim of this study. Several studies
located and discussed below showed the failure to report cyber-harassment to some
authority is a problem. Ascertaining overall reporting rates as well as comparing
reporting frequency rates by gender, race, age, SES, and student/faculty status and
identifying the barriers to the reporting are crucial to creating effective policy to deal with
cyber-harassment in online learning.
Traditional reasons for a victim not reporting crimes are fear of legal
accountability, fear of humiliation, embarrassment, fear of retaliation by the offenders , or
normalization of the attack (Riedel & Welsh, 2008, p. 12). Additional reasons why
people do not report that may be relevant can be drawn from sexual assault literature and
include (a) they doubt that authorities can help (California Coalition Against Sexual
Assault, 2004; B. S. Fisher, Daigle, Cullen, & Turner, 2003), (b) most victims do not

33

report because they do not consider themselves a victim of a crime (B. S. Fisher et al.,
2003; Karjane, Fisher, & Cullen, 2002), and (c) they do not know where/how to report
victimization (Karjane et al., 2002).
In terms of the cyber-bullying studies, Li‘s 2007 study found 34% of victims said
they reported cyber-bullying. Li concluded the ones who did not report did not do so
primarily because they did not think adults would stop it as she also found from her
survey that 67.1% ―don‘t think adults will or can help‖ (Li, 2007). In the Juvonen and
Gross (2008) study that found 72% were cyber-bullied at least once, 90% of the sample
did not report incidents to adults. Many said they did not report because they felt they
―needed to learn to deal with it themselves‖(Juvonen & Gross, 2008). This lends itself to
the reason of they did not realize it was serious enough to report or had normalized the
harassment. In his cyber-harassment study of colleges students at the University of New
Hampshire, Finn (2004) found that only 6.8% of students reported their victimization.
He did not collect why they did not report but he did have a follow on question that
inquired if the authority they reported to resolved the situation to their satisfaction, 47.8%
responded, no, which would reinforce any belief that the authorities were unable to help.
From the literature currently available, the predominant reasons for not reporting
cyber-harassment appear to be that they doubt that authorities can help or they do not
consider themselves a victim of a crime.
In terms of gender, of the existing studies analyzed, Lenhart‘s (2007) study of
cyber-bullying among teens found older girls in particular are more likely to report being
bullied than any other age and gender group, with 41% of online girls ages 15 to 17 reporting

34

their experiences. Traditionally, females report crimes at a higher rate than males,
especially non violent crimes (U.S. Department of Justice, 1997). Looking at reporting rates
by gender is important.
It is well established that crime reporting rates can vary by race ("Bureau of Justice
Statistics,"). Therefore, although no evidence was available that it would be different when
reporting cyber-harassment, it is useful to inquire about frequency of reporting by race.
Reporting rates according to SES and age are also worth investigating.
In terms of faculty or student status effecting reporting rates, no relevant evidence
was located, but the researcher speculated faculty may be more apt to report because they
have less to fear in terms of retaliation. Either way, it is a useful demographic to assist in
better understanding any reporting differences.
Chapter Two Conclusion
There is a burgeoning quantity of scholarship and study of cyber-bullying and
even bullying in college that can inform a study on cyber-harassment in online learning in
higher education. There is also evidence to point to a problem of cyber-harassment in
online learning as definitions, guidelines and recommendations, and even institutional
policies exist. But no studies were located that give a clear picture of the extent or nature
of the problem.
Data needed to be collected and analyzed from online college courses the way it
has been done in teen and pre-teen cyber-bullying studies to see if cyber-harassment is a
problem, what frequency it occurs and to whom, and at what rate it is being reported and
if low, determine why.

35

CHAPTER 3:
Methodology

The purpose of this study is to explore the nature of and extent to which students
and faculty experience and report cyber-harassment in and as a result of the online
learning settings of colleges and universities. This chapter presents the research
questions and a description of the research methodology, including the sampling
procedure and population, instrumentation, and procedures for data collection and
analysis.
Research Questions
Specifically, this study will address the following:
1. To what extent is cyber-harassment (dependent variable) occurring within or
was the continuation/result of something that happened in an ONLINE
course?
a. How do incident rates vary by the victim‘s gender, race, age, SES,
student/faculty status (independent variables)?
b. How do incident rates vary by type of harassment?
c. How does type of harassment vary by characteristics (gender, race,
age, SES, student/faculty status) of the victim?
2. At what rate is reporting of cyber-harassment within or was the
continuation/result of something that happened in an ONLINE course
occurring?

36

a. How does reporting vary by the victim‘s gender, race, age, SES,
student/faculty status (independent variables)?
b. What types of cyber-harassment are most likely to be reported?
c. What are the barriers to reporting of the cyber-harassment?

37

Table 1
Summary of Analysis
________________________________________________________________________
Research
Questions
Test Variables Survey Items

DV IV

1a: Cyber-
harassment Incident
Rate vs.
Characteristics
Chi-Square
Cyber-
harassment
(any)
Gender, Race,
Status
(student/faculty)
DV-6.1-7
IV- 2.1-4, 3.1
1b: % of Cyber-
harassment by type
Frequency
Analysis
Email, Pornography, Live Chat,
Postings, Malicious Code,
Impersonation, Stalking
6.1-7
1c. Type of Cyber-
harassment vs.
Characteristics
Chi-Square
Email,
Pornography,
Live Chat,
Postings,
Malicious Code,
Impersonation,
Stalking
Gender, Race,
Status
(student/faculty)
DV-6.1-7
IV- 2.1-4, 3.1
2a. Reporting of
Cyber-harassment
vs. Characteristics
Chi-Square
Reporting of
Cyber-
harassment
Gender, Race,
Status
(student/faculty)
DV-6.1-7
IV- 2.1-4, 3.1
2b. % of Reporting
by Type of Cyber-
harassment
Frequency
Analysis
Email, Pornography, Live Chat,
Postings, Malicious Code,
Impersonation, Stalking
6.1-7
2c. Reasons not
reported by %
Frequency
Analysis

Did not know where to report
Doubted authorities could help
Did not think reportable offense
Embarrassed
Feared retaliation
Other
6.8
________________________________________________________________________

38

Design

This study drew data from a survey administered online. A quantitative approach
was utilized because the nature of the research questions focused on establishing
frequency patterns and determining the significance of relationships between variables. A
quantitative analysis allowed the frequencies and correlations to be measured
systematically using empirical evidence and minimized the subjectivity of the researcher.
Empirical evidence in the form of numerical indices of the extent and nature of cyber-
harassment in online learning did not yet exist. A quantitative study was useful not only
to provide that, but make the findings more generalizable and succinct to present to
institutions around the world (Patton, 2002, p. 14). The study is non-experimental since
there is no control group or multiple waves of measurement. The total frequency among
cyber-harassment and reporting was examined along with the relationship between each
of the independent variables (gender, race, age, SES and status: faculty/student) to the
dependent variables of cyber-harassment and reporting. The relationship between the
independent variables that are found to be significant will also be examined. Similar
studies have been conducted among teen and pre-teens regarding cyber-bullying and the
results have been useful and seem to be generalizable (Li, 2007).
Population and Sample
A large, private, non-profit, liberal arts university in Hawaii with a sizable online
program was the site of the study. The university has over 8,000 students and about 300
full and part-time faculty. About 2,500 of the students and over 100 of the faculty take or
teach online courses. The composition of the population at the university makes this

39

study relatively generalizable to online course takers at colleges and universities across
the United States. The university has students from all 50 states and over 100 countries.
Further, it has a significant number of military students pursuing associate degrees as
well as very well prepared and selectively admitted undergraduate and graduate students.
Although it is a private school, the tuition is kept low to compete with its primary
competitor in Hawaii which is the state‘s public institution. Needless to say, in Hawaii,
the student body is diverse. For these reasons the students are an excellent cross section
of students such as may be enrolled across the country at community colleges, public, and
private universities. Participants in the study will voluntarily self-select.
Instrumentation

The instrument used to collect the data for this study was a 15 question survey
(Appendix A). The questions first collected general demographic data (questions 2.1-
2.4), whether the participant is a student or faculty member (question 3.1), and the
number of online courses with which the participant has been involved (question 4.1 - 4.2
or 5.1-5.2). The characteristic variables are based on findings from earlier studies, which
suggested that victimization and reporting may vary by race, gender, age, SES, and
faculty/student status.
The dependent variable, cyber-harassment, was presented in the survey as a series
of behaviors. So instead of responding about whether they had been cyber-harassed,
participants were asked to indicate if they had ever experienced any of seven specific
behaviors (questions 6.1-6.7) on the survey (Appendix A). Past studies show this

40

question structure encourages more accurate disclosures about past experience with
traumatic behaviors (Juvonen & Gross, 2008; Riedel & Welsh, 2008, p. 28).
Whether victims made a formal report or not (the second dependent variable), was
also measured by questions 6.1- 6.7. Why they did not will be ascertained by question
6.8. Question 6.8‘s response choices were drawn from relevant literature on barriers to
reporting violence as discussed in Chapter 2. The study was pilot tested on one of the
online courses that the researcher teaches and several faculty who teach online to test
reliability and validity of the instrument. These independent variables were selected
because they seemed to be key issues in the literature or may be needed to understand the
overall findings. Reporting cyber-harassment was chosen as the second dependent
variable to help ascertain the nature of the problem and to see if it reflects the
underreporting of other crimes (Riedel & Welsh, 2008).
Data Collection
The survey was delivered via the internet, specifically, through the intranet of the
university which is accessible only to faculty and students. The link to the electronic
survey was sent to the institutional email of all current students who are taking or have
taken online courses (about 2,500) and all the faculty teaching online courses (about
100). The average survey return rate is lower in an online format than face-to-face
(Granello & Wheaton, 2004, p. 389), perhaps 20% instead of 50% but even a 10%
response rate would have yielded a sample of 260 participants. Institutional review
Board clearance was received from the institution where the survey was conducted as
well as the University of Southern California. The survey was delivered just after mid-

41

term of the fall semester. Participants were able to complete the survey in 5 to 10
minutes. The brief nature of the survey and the contribution it may make to the online
medium of course delivery, which the participants have a stake in, yielded a healthy
sample size. 283 surveys were completed; 225 by students and 56 by faculty between
October 27 and November 10, 2009 representing about 11% of the total online learning
and teaching population at the institution.
Data Analysis

The data analysis was conducted using SPSS, Version 12.0 for Windows.
First, descriptive statistics were used to summarize the data including frequencies
and percentages of both dependent variables overall and by each independent variable.
These data summary are presented in tables and charts.
Second, Pearson‘s Chi-square tests were performed on each independent variable
to test its statistical significance. This established the significant relationships of
characteristics to cyber-harassment and reporting. P-values were used to determine
statistical significance of the independent variables and the level of significance was set
at 0.05. For instance, if there is a significant relationship between cyber-harassment and
gender, the p value will be less than 0.05 (p < 0.05).
Lastly, the researcher sought to find if there was any interaction between
independent variables found to be significant. Since there were never more than two
significant independent variables, and one was always student/faculty status, Pearson‘s
Chi-square tests were performed to see if the other independent variable was also
significant or not within each status group (student and faculty).

42

CHAPTER 4:
Findings

This study aims to establish the frequency and nature of cyber-harassment in
online learning settings of colleges and universities as well as the reporting rates of such
harassment. This chapter presents the findings of the study organized by the following
research questions (also see Table 1):
1. To what extent is cyber-harassment (dependent variable) occurring within or
was the continuation/result of something that happened in an ONLINE
course?
a. How do incident rates vary by the victim‘s gender, race, age, SES,
student/faculty status (independent variables)?
b. How do incident rates vary by type of harassment?
c. How does type of harassment vary by characteristics (gender, race,
age, SES, student/faculty status) of the victim?
2. At what rate is the reporting of cyber-harassment within or was the
continuation/result of something that happened in an ONLINE course
occurring?
a. How does reporting vary by the victim‘s gender, race, age, SES,
student/faculty status (independent variables)?
b. What types of cyber-harassment are most likely to be reported?
c. What are the barriers to reporting of the cyber-harassment?
Data were analyzed using the SPSS v12.0 statistical program.

43

Description of the Sample
The study survey was delivered electronically through the intranet of the
university described in Chapter 3. The recruiting letter with a link to the survey was sent
to the institutional email of all current students who are taking or have taken online
courses (about 2,500) and all the faculty teaching online courses (about 100). To
minimize self-selection, the term cyber-harassment was not used in the recruiting letter.
The survey was delivered just after mid-term of the fall semester. Participants were able
to complete the survey in 5 to 10 minutes. Two hundred and eighty-three surveys were
collected (N = 283) between October 27 and November 10, 2009 representing about 11%
of the total online learning and teaching population at the institution. Table 2 gives a
breakdown of the characteristics of the sample.

44

Table 2
Characteristics of Sample
________________________________________________________________________
Gender

Male

Female

37%

63%

Race/Ethnicity

White Asian NHPI Mixed Race Hispanic Black N. Amer.
50% 18% 9% 7% 7% 4% 1%

Age Group

Under 23

23-35

Over 35

21%

39%

34%

SES (Household Income)

Below $25K $25K-50K

$50K-100K $100K +

20% 21%

33% 20%

Status

Student

Faculty

80%

20%

________________________________________________________________________

Thirty-seven percent (N = 105) identified themselves as male while 63% (N =
177) identified themselves as female. Only about 50% (N = 142) were classified as
White, 18% (N = 52) were Asian, 9% (N = 25) were Native Hawaiian/Pacific Islander
(NHPI), 7% (N = 21) were Mixed Race (most in this category were half white and half

45

Asian), 7% (N = 19) were Hispanic, 4% (N = 10) were Black, and 1% (N = 3) were
Native American. Twenty-one percent (N = 59) of participants were under the age of 23,
39% (N = 109) were 23 to 35, and 34% (N = 97) were over age 35. Twenty percent (N =
56) of participants had a household income of less than $25,000, 23% (N = 64) had a
household income of $25,000 to $50,000, 33% (N = 92) had a household income of
$50,000 to $100,000, and 20% (N = 57) had a household income of over $100,000.
Eighty percent (N = 225) of the participants were students while 20% (N=56) were
faculty. The 225 student participants represented about a tenth of all online students of
the institution while the 56 faculty participants represented about half of all faculty
teaching online at the institution making the faculty participants a much larger sample of
their total population relative to the student participant sample.
Research Question 1A:
Cyber-harassment Incident Rate vs. Characteristics

Overall, 18% (N = 50) of all participants had experienced cyber-harassment at
least once during or as a result of online courses. Five percent (N = 14) experienced
cyber-harassment more than once. Table 3 shows the percentage of participants who had
suffered cyber-harassment at least once by number of courses taken or taught.

46

Table 3
Cyber-harassment Rate by Number of Courses Taken Online
________________________________________________________________________
Number of Courses Cyber-harassment Rate N =
1 to 3 14% 11 out of 79
4 to 10 15% 16 out of 104
11 to 20 15% 8 out of 54
more than 20 32% 12 out of 38
________________________________________________________________________
As common sense would suggest, the rate of cyber-harassment increased with increased
exposure to online courses.
Table 4 shows the percentage of participants who had suffered some type of
cyber-harassment at least once by characteristic followed by a detailed analysis of the
findings.

47

Table 4
Cyber-harassment Rate by Characteristic
________________________________________________________________________
Characteristic % Experienced Any Cyber-harassment

Overall 18%

Male 19%

Female 16%

White 23%

Asian 14%

NHPI 12%

Mixed Race 14%

Hispanic 19%

Black 11%

Under 23 14%

23 to 35 11%

Over 35 25% *

Below $25K 18%

$25K-50K 9%

$50K-100K 16%

$100K + 28%

Student 12%

Faculty 39% *
______________________________________________________________________
* indicates statistically significant
Status
Student or faculty status quickly emerged in the findings as the most important
characteristic in relation to cyber-harassment therefore it seems appropriate to discuss it
first. After demographic information was collected by the survey, participants were

48

asked to identify themselves as completing the survey as a student or a faculty member
(Question 3.1). Eighty percent (N = 225) of the participants were students while 20%
(N=56) were faculty. The 225 student participants represented about a tenth of all online
students of the institution while the 56 faculty participants represented about half of all
faculty teaching online at the institution. Twelve percent (N = 27) of students had
experienced some type of cyber-harassment at least once while 39% (N = 22) of faculty
had. This finding is statistically significant,
2
(1, N = 281) = 0.000, p < .05.
Furthermore, faculty reported being harassed more than once at a rate of 16% (N = 9)
while only 2% (N = 5) of students reported being cyber-harassed more than once. The
researcher did not expect the much greater rate of cyber-harassment among faculty but in
light of the potential for harassment in professor rating sites and other outlets identified in
the literature review it is not surprising. Also, contributing to this statistic, however, is
that faculty have greater exposure to the online learning environment over the course of
time. While only 4% of students had been involved in 20 or more online courses, 50% of
faculty had. Nonetheless, the major finding of this study is the significant relationship of
faculty status to cyber-harassment in an online learning environment. In light of this
finding, additional analysis was conducted where appropriate not only on the entire group
but also by status group (student or faculty).
Gender
The gender of the participants was collected through a closed end question on the
survey (2.1). Male, Female, or Other were response choices. Thirty-seven percent (N =
105) identified themselves as male while 63% (N = 177) identified themselves as female.

49

None chose other. Of the students who participated, 32% (N = 71) were male while 68%
(N = 153) were female. Sixty percent of students at the institution are female so while the
female ratio may seem high, it is not. Of the faculty who participated, 57% (N = 32)
were male and 43% (N = 24) were female. Nineteen percent (N = 20) of males
experienced some type of cyber-harassment at least once. Overall, 16% (N = 29) of
females experienced some type of cyber-harassment at least once. Based on a Pearson‘s
Chi-square statistical test, the characteristic of gender was not statistically significant,
2
(1, N = 282) = 0.568, p > .05. Broken down by status, 11% (N = 8) of male students and
12% (N = 18) of female students and 34% (N = 11) of male faculty and 39% (N = 11) of
female faculty experienced cyber-harassment at least once. Both were comparable to the
overall rate of cyber-harassment for each status and neither were statistically significant.
Race

Hawaii‘s racial make-up is very different from any other U.S. state. Mixed racial
demographics are a common occurrence rather than an exception in Hawaii and often
include more than two races. To make participants comfortable to identify the racial or
ethnic make-up they most identify with, question 2.4 on the survey was open ended. The
researcher then used the census data categories used by the state of Hawaii to classify
them for the purpose of this data analysis. Fifty percent (N = 142) were classified as
White, 18% (N = 52) as Asian, 9% (N = 25) as Native Hawaiian/Pacific Islander (NHPI),
7% (N = 21) as Mixed Race (most in this category were half white and half Asian), 7%
(N = 19) as Hispanic, 4% (N = 10) as Black, and 1% (N = 3) as Native American.

50

However, 73% (N = 41) of the faculty were white making only 44% (N = 99) of student
participants white.
The rate at which each race experienced any type of cyber-harassment at least
once was as follows: 23% (N = 32) White, 13% (N = 7) Asian, 12% (N = 3) Native
Hawaiian/Pacific Islanders, 14% (N = 3) Mixed Race, 19% (N = 3) Hispanic, 11% (N =
1) Black. None of the three Native Americans reported cyber-harassment. Based on a
Pearson‘s Chi-Square test, race was not statistically significant,
2
(6, N = 272) = 0.588, p
> .05. Although a 23% rate among whites seems relatively high, if only white students
are considered and white faculty are excluded, the rate is 14% making the slightly higher
rate of 23% related more to the statistically significant finding that faculty were more
likely to be cyber-harassed than students since 73% (N = 41) of the faculty were White.
Race did not register as statistically significant for faculty either but since 41 out of the
sample of 53 were white this study did not capture any useful findings regarding how the
characteristic of race relates to cyber-harassment of faculty. The rate that White faculty
experienced cyber-harassment of 41% (N = 17) was comparable to the overall rate at
which faculty experienced cyber-harassment at 39%.
Age

Question 2.1 on the survey was an open ended question asking how old
participants were at the time they completed the survey. Ages were then classified into
three categories; under age 23 to represent students of traditional college age, ages 23 to
35 to represent non-traditional students but who had most likely grown up with at least
some exposure to the internet, and those over age 35 who would not have had experience

51

with the internet until they were adults. Twenty-one percent (N = 59) of participants
were under the age of 23, 39% (N = 109) were 23 to 35, and 34% (N = 97) were over age
35. Forty-three of the 51 faculty who reported their age were over 35. The relatively
large number of non-traditional aged students is no doubt due to online learning being
more popular to that group to allow more flexibility for work and family obligations
(Cercone, 2008). Fourteen percent (N = 8) of those under age 23 experienced some type
of cyber-harassment at least once. Eleven percent (N = 12) of those 23 to 35 experienced
cyber-harassment while 25% (N = 24) of those over age 35 experienced cyber-
harassment. Through a Pearson‘s Chi-Square test, age was determined to be a
statistically significant characteristic in regards to cyber-harassment,
2
(2, N = 265) =
0.024, p < .05. The statistical significance of the over 35 group at first glance seems it
may be tied to the statistically significant finding that faculty are more likely to be cyber-
harassed than students as 78% of the faculty were over age 35. However, if only students
over the age of 35 are considered, the rate is still 20% (N = 11) which is also statistically
significant when a Pearson‘s Chi-square test is calculated,
2
(2, N = 214) = 0.047, p <
.05.
SES
Household income was collected using a closed end question (1.3) with choices
that mirror U.S. Census categories. Twenty percent (N = 56) of participants had a
household income of less than $25,000, 23% (N = 64) had a household income of
$25,000 to $50,000, 33% (N = 92) had a household income of $50,000 to $100,000, and
20% (N = 57) had a household income of over $100,000. Eighteen percent (N =10) of

52

those with a household income of under $25,000 had ever been cyber-harassed, 9% (N =
6) of those $25,000 to $50,000, 16% (N = 15) of those $50,000 to $100,000, and 28% (N
= 16) of those over $100,000. A Pearson‘s Chi square test shows that this characteristic
is not significant in relation to cyber-harassment,
2
(3, N = 269) = 0.059, p > .05.
Although not quite statistically significant, the 28% for the highest income bracket does
seem relatively high but this may be once again due to the statistically significant finding
that faculty are more likely to be cyber-harassed than students as 46% (N = 24) of the
faculty were in the highest income bracket while only 15% (N = 32) of students had
household incomes of over $100,000.
Research Question 1B:
Rate of Cyber-harassment by Type

Table 5 reports the frequency of cyber-harassment in online courses by type.
Table 5
Rate of Cyber-harassment by Type
________________________________________________________________________
Type of Cyber-harassment Rate

Email 10%

Obscene (Pornography) 2%

Flaming 6%

Postings 3%

Malicious Code 3%

Impersonation 1%

Stalking (in-person) 1%
________________________________________________________________________

Rather than asking participants if they had ever been cyber-harassed since they may not
have been familiar with what constitutes the behavior, participants were asked if they had

53

ever experienced seven different behaviors in or as a result of an online course (Questions
6.1 – 6.7). The seven behaviors had been identified as a consensus of the related
literature earlier in this study. Ten percent (N =29) of participants had received
―unpleasant, hurtful, or threatening emails or text messages‖ (6.1). It is not surprising
that cyber-harassment manifested itself most commonly in email or other type of
asynchronous messages since it is the cornerstone style of internet communication.
Second most prominent was ―flaming,‖ online verbal abuse that took place ―during live
chat or other interactive activity‖ (6.3) at 6% (N = 18). Live chat is another component
of many online courses and becoming increasingly more popular form of communication
on the internet. The receipt of ―unwanted pornographic, obscene, or other inappropriate‖
content (6.2), 2% (N = 5), being the ―subject of improper messages, photos, or other
postings on message boards, websites or social networking sites‖ (6.4), 3% (N = 8), and
the receipt of ―malicious code (viruses etc.)‖ (6.5), 3% (N = 8) was less common. Least
common but no less concerning were that 1% (N =2) of respondents were ―impersonated
on the internet‖ (6.6) and 1% (N = 3) of respondents ―experienced harassment in-person
that escalated from online harassment‖ (6.7). When harassment via the internet crosses
into physical harassment, it is known as cyber-stalking ("State of New York Division of
Criminal Justice Services," 2008). Although only 3 participants experienced this, it is
very concerning as it could result in physical danger.
Research Question 1C:
Type of Cyber-harassment vs. Characteristics
Table 6 reports the rates of each type of cyber-harassment by characteristic.

54

Table 6
Type of Cyber-harassment Rates by Characteristics
______________________________________________________________________________
Characteristic Email
Obscene
(Porno-
graphy) Flaming Postings
Malicious
Code
Imper-
sonation
Stalking
(in-
person)

Male 12% 2% 4% 3% 3% 2% 1%
Female 8% 1% 7% 2% 3% 0% 1%

White 15% 2% 9% 5% 4% 1% 1%
Asian 13% 0% 4% 0% 0% 2% 0%
NHPI 4% 0% 4% 4% 0% 0% 0%
Mixed Race 0% 5% 5% 0% 5% 0% 0%
Hispanic 19% 6% 0% 0% 0% 0% 5%
Black 0% 0% 11% 0% 0% 0% 0%

Under 23 7% 0% 2% 0% 5% 0% 0%
23 to 35 8% 1% 5% 1% 0% 0% 0%
Over 35 15% 3% 3% 5% 2% 2% 3%

Below $25K 7% 2% 11% 4% 2% 2% 2%
$25K-50K 6% 2% 2% 0% 2% 0% 0%
$50K-100K 7% 3% 8% 3% 3% 0% 1%
$100K + 23%* 0% 8% 6% 2% 2% 2%

Student 6% 2% 5% 1% 1% 0% 1%
Faculty 33%* 2% 11% 11%* 3%* 2% 0%
________________________________________________________________________

* indicates statistically significant

No statistically significant differences stood out based on gender, race, or age group.
Faculty or student status again stands out as being the most important characteristic by
having high rates in certain types of cyber-harassment as it did in overall cyber-

55

harassment. Thirty-three percent of faculty (N = 14) suffered cyber-harassment via
―unpleasant, hurtful, or threatening emails or text messages‖ (6.1) while only 6% (N =
13) of students did. Based on Pearson‘s Chi-square testing, this finding is statistically
significant,
2
(1, N = 281) = 0.000, p < .05. Also, 11% (N = 6) of faculty were ―subject
of improper messages, photos, or other postings on message boards, websites or social
networking sites‖ (6.4) versus 1% (N = 2) of students. This finding is statistically
significant,
2
(1, N = 273) = 0.000, p < .05. Lastly, 3% (N = 5) of faculty received
―malicious code (viruses etc.)‖ (6.5) versus 1% (N = 3) of students. This was also
statistically significant,
2
(1, N = 271) = 0.003, p < .05. Interestingly, none of the three
cyber-stalking (escalated to in-person) victims (students = 1%) identified were faculty so
while they suffer the most nuisance, they do not appear to be in physical danger.
One other finding of note is regarding SES. Twenty-three percent (N =13) of
those who had a household income over $100,000 suffered cyber-harassment via
―unpleasant, hurtful, or threatening emails or text messages‖ (6.1) while those who made
less suffered at a rate of just 6 to 7%. This finding was statistically significant,
2
(3, N =
269) = 0.007, p < .05. This significance, however, appears to be related to the even more
statistically significant finding that faculty were much more likely to be cyber-harassed
by email as they were also much more likely to make over $100,000. 46% of faculty
were in the highest SES category while only 15% of students were. When just the
students are looked at in terms of income in relation to cyber-harassment via email, of
those with a household income over $100,000 only 6% (N = 2) were affected and when a
Pearson‘s Chi-square test was calculated the percentage was not anywhere close to

56

significant,
2
(3, N = 215) = 0.851, p > .05. Likewise, when just the faculty are looked at
in terms of income in relation to cyber-harassment via email, of those with a household
income over $100,000, 42% (N = 10) were affected and when a Pearson‘s Chi-square test
was calculated the percentage was also not significantly significant,
2
(2, N = 52) =
0.081, p > .05.
Research Question 2A:
Reporting of Cyber-harassment vs. Characteristics

Of those who suffered cyber-harassment in or as a result of an online course once
or more, Table 7 presents the percentage of those who reported it at least once overall and
by characteristic.

57

Table 7
Cyber-harassment Reporting Rate by Characteristic
________________________________________________________________________
Characteristic % Reported Any Cyber-harassment

Overall 44%

Male 40%

Female 48%

White 50%

Asian 30%

NHPI *

Mixed Race *

Hispanic *

Black *

Under 23 60%

23 to 35 33%

Over 35 50%

Below $25K 50%

$25K-50K 50%

$50K-100K 38%

$100K + 36%

Student 44%

Faculty 43%
_______________________________________________________________________

Overall, 44% reported it at least once. Pearson‘s Chi-square testing was performed and
no statistically significant differences in reporting rate was found based the differing
characteristics. In terms of race, other than White and Asian, there were not enough

58

participants who suffered cyber-harassment to present meaningful data on the rate of
reporting.
Research Question 2B:
Rate of Reporting Cyber-harassment by Type

Table 8 presents the reporting rate by type of cyber-harassment.
Table 8
Cyber-harassment Reporting Rate by Type
________________________________________________________________________
Type of Cyber-harassment Reporting Rate

Email 32%

Obscene (Pornography) 40%

Flaming 28%

Postings 38%

Malicious Code 75%

Impersonation 100%

Stalking (in-person) 67%

________________________________________________________________________

Impersonation (100%) and Cyber-stalking (67%) were reported at very high rates. This
makes sense, as these are the most serious types of cyber-harassment. Impersonation
could result in financial loss and cyber-stalking could result in physical danger. They are
also likely clearly perceived crimes whereas the other types of harassment may not be
perceived as a reportable offense. The recipients of malicious code also reported at a
relatively high 75%. The receipt of a computer virus also costs money and time.
Furthermore, there is little shame involved since it is less personal than the other type of
cyber-harassment so has one less barrier to reporting. It is interesting that email (32%)

59

and flaming (28%) are the least reported even though they are the most common types of
cyber-harassment. Perhaps they are not considered by many as a reportable offense since
no one is hurt physically or financially. The receipt of obscene materials (40%) and
postings (38%) fall in between.
Research Question 2C:
Barriers to Reporting Cyber-harassment

Of those who suffered cyber-harassment in an online learning environment but
did not report it, Table 9 presents the reasons why by percentage.
Table 9

Reasons Cyber-harassment not Reported by Percentage
______________________________________________________________________________
Reason Not Reported Percentage

Doubted authorities could help 43%

Did not think it was an offense 38%

Did not know where to report 10%

Feared retaliation 10%

Embarrassed 0%
________________________________________________________________________

As the existing studies of cyber-harassment in per-teens and teens suggested, the doubt
that authorities could help (43%) and did not think it was a reportable offense (38%) were
the major reasons why victims did not report. Victims not knowing where to report
(10%) and the fear of retaliations (10%) also deterred several from reporting. No one
indicated they did not report because they were embarrassed by the situation.

60

Chapter Four Conclusion
This study found 18% of survey participants experienced cyber-harassment in or
as a result of online learning in college at least once (see Table 4) and 5% had
experienced it more than once. The major finding of this study is the significant
relationship of faculty status to cyber-harassment in or as a result of an online learning
environment. Thirty-nine percent of faculty reported being cyber-harassed at least once
and 16% reported being cyber-harassed more than once while only 12% of students had
been cyber-harassed once and only 2% had been cyber-harassed more than once. The
gap between faculty and student cyber-harassment rate was surprising but that cyber-
harassment by postings on internet was statistically significant for faculty at a rate of 11%
over the students‘ rate of 1% was not (see Table 6) as the literature suggested this type of
harassment was common for faculty as will be further discussed in Chapter 5. Faculty
were also dramatically more likely to be cyber-harassed by email and were also more
likely to be sent computer viruses.
Few differences in the rate of cyber-harassment victimization in and as a result of
online courses were found based on the characteristics of Gender, Race, or SES which is
not in disagreement with previous relevant studies. The study found that age is a
significant factor, however. Of the three age groups, traditional college age students
under 23, ages 23-35, and over 35, those over 35 were harassed at significantly higher
rate whether or not they happened to be student or faculty status.
In regards to the reporting of cyber-harassment experienced in or as a result of an
online educational setting, those who had been cyber-harassed reported it at least once at

61

a rate of 44%. The most common type of cyber-harassment to be reported (see Table 8)
were the most serious and the least common (see Table 5); cyber-stalking (in-person) and
impersonation. The least reported were the most common type of cyber-harassments;
email and flaming. Of those that did not report, 43% doubted authorities could help and
38% did not think it was a reportable offense (see Table 9).
Explanations, possible solutions, and areas for further study regarding these
cyber-harassment rates and reporting phenomenon will be explored in Chapter 5.

62

CHAPTER 5:
Conclusions

As this study was conducted, stories of cyber-bullying and cyber-harassment and
the awareness of the phenomenon grew. Days before this study was initially presented,
there was another suicide seemingly directly linked to cyber-bullying in Massachusetts
(Wheeler, 2010). There is burgeoning scholarship relating to cyber-bullying in middle
and high school settings. When adults harasses other adults on the internet it is known as
cyber-harassment ("State of New York Division of Criminal Justice Services," 2008) and
it is is also clearly on the rise in society and on college campuses. Online higher
education has exploded in terms of growth in the last several years. Related literature
including university policies and online teaching guides suggest there may be a cyber-
harassment problem in online learning in higher education as well although no
quantitative evidence of cyber-harassment in online learning in higher education
currently exists. The purpose of this study was to explore the nature of and extent to
which students and faculty experience and report cyber-harassment in and as a result of
the online learning settings of colleges and universities with a quantitative approach.
Preventions and solutions can be more effectively devised if the exact character of the
problem is known.
Discussion

As suspected based on current literature showing cyber-harassment existing in
high schools, colleges, and other institutions, and since online education programs have
adopted policies and online teaching guides address the phenomenon, cyber-harassment

63

in and as a result of online learning settings does exist. This study has provided some
quantitative data on the extent to which it exists and its nature as well as the trends in
reporting it. Cyber-harassment studies of other populations, mostly teens, showed a
cyber-harassment rate of 20% to 75% depending on the how the scope of cyber-
harassment was defined. If Olweus‘ traditional definition of bullying (1993) is applied
the rate was closer to 20% to 30%. This study found 18% of people had experienced
cyber-harassment in or as a result of online courses at least once (see Table 4) and only
5% had experienced it more than once. Although Chapell found that bullying does not
decrease much from high school to college (2004), the lower cyber-harassment rate
makes sense because this study was of a limited setting. It was not about the population
experiencing cyber-harassment in general but only in this particular setting of online
courses or resulting from online courses.
The most common type of cyber-harassment found in or as result of cyber-
harassment was the 10% of participants had received ―unpleasant, hurtful, or threatening
emails or text messages‖ (6.1). It is not surprising that cyber-harassment manifested
itself most commonly in email or other type of asynchronous messages since it is the
cornerstone style of internet communication. Second most prominent was ―flaming,‖
online verbal abuse that took place ―during live chat or other interactive activity‖ (6.3) at
6%. Live chat is another component of many online courses and becoming increasingly
more popular form of communication on the internet. The receipt of ―unwanted
pornographic, obscene, or other inappropriate‖ content (6.2), 2%, being the ―subject of
improper messages, photos, or other postings on message boards, websites or social

64

networking sites‖ (6.4), 3%, and the receipt of ―malicious code (viruses etc.)‖ (6.5), 3%
was less common. Least common but no less concerning were that 1% of respondents
were ―impersonated on the internet‖ and 1% of respondents ―experienced harassment in-
person that escalated from online harassment‖ (6.7). When harassment via the internet
crosses into physical harassment, it is known as cyber-stalking ("State of New York
Division of Criminal Justice Services," 2008).
The major finding of this study is the significant relationship of faculty status to
cyber-harassment in or as a result of an online learning environment. Thirty-nine percent
of faculty reported being cyber-harassed at least once and 16% reported being cyber-
harassed more than once while only 12% of students had been cyber-harassed once and
only 2% had been cyber-harassed more than once. Part of this much higher rate of
experiencing cyber-harassment in or as a result of online courses is explained by the
faculty‘s relatively continuous exposure to the setting but nonetheless, faculty or student
status is the major factor. The gap between faculty and student cyber-harassment rate
was surprising but that harassment by postings on internet was statistically significant for
faculty at a rate of 11% over the students‘ rate of 1% was not (see Table 6). A study in
Canada had shown 84% of teachers had been defamed on social networking (Shariff,
2008) sties while other stories of harassment of faculty on RateMyProfessor.com,
YouTube, and MySpace abound (Binns, 2007; Shepherd, 2006). Faculty were also
dramatically more likely to be cyber-harassed by email and to be sent computer viruses.
Interestingly, none of the three cyber-stalking victims surveyed were faculty so while
they suffer the most nuisance, they do not appear to be in physical danger.

65

Like the relevant studies in chapter 2, this study seems to show that cyber-
harassment does not affect one sex more than the other. Even in regards to the specific
types of harassment of receiving obscene or pornographic materials and cyber-stalking
which Shariff suggests females are more often the targets of (2008, p. 90) there was
almost no difference. The behaviors that make up cyber-harassment were intentionally
made broad for this study to capture any gender effect. However, if sexual harassment is
a problem in terms of cyber-harassment; like previous relevant quantitative studies, this
study failed to capture it.
Although this sample was more diverse than any of the relevant studies reviewed
in Chapter 2, at least in terms of the student population, no major differences in cyber-
harassment rate in online courses among race were found overall or by type of
harassment. SES did not appear to be a major factor either. Although the participants in
the highest income brackets experienced higher rates of cyber-harassment overall and by
email, it was due to faculty dominating the highest income bracket and the statistically
significant finding that they suffered the highest cyber-harassment rate. When looked at
as separate groups, SES did not have a significant relationship with cyber-harassment for
students or faculty.
Of the three age groups, traditional college age students under 23, ages 23-35, and
over 35, those over 35 were clearly more likely to experience cyber-harassment in or as a
result of an online course, whether or not they happened to be student or faculty status.
The reasons for this can only be speculated on but it may be an area for further study.
Clearly, this group would not have grown up with the internet and would have only

66

learned to use it as adults suggesting that they may not be as well adjusted as the younger
age groups to online activity. It could be a matter of what they are willing to tolerate
compared to the younger age groups or it could be that something about the age group‘s
actions or skills lends itself to experiencing cyber-harassment.
In terms of reporting cyber-harassment in or as a result of online learning settings,
those who had been cyber-harassed reported it at least once 44% of the time which
appears to be higher in this setting than any of the relevant cyber-harassment studies
reviewed in Chapter 2 which ranged from 7% (Finn, 2004) to 34% (Li, 2007). The
higher reporting rate may be due to the lines of authority being clearer in an online
education setting than on the internet in general. Interestingly, there were no statistically
significant differences among reporting by characteristic, although females reported at a
higher rate than males at 48% to 40% which goes along with the fact that females are
more likely to report crimes than males (U.S. Department of Justice, 1997). It is
interesting that virtually no difference in reporting rate occurred between students and
faculty (see Table 7). The most likely type of cyber-harassment to be reported (see Table
8) were the most serious and the least common (see Table 5); cyber-stalking (in-person)
and impersonation probably because they could easily result in physical danger or
financial loss. The least reported were the most common type of cyber-harassments;
email and flaming probably because victims did not consider them reportable since no
physical or financial harm resulted.
From the existing literature reviewed in Chapter 2, the predominant reasons for
not reporting cyber-harassment appear to be that victims doubted authorities could help

67

or they did not consider themselves a victim of a reportable offense and that is exactly
what this study found as well in the online learning setting. Of those that did not report,
did not do so because 43% doubted authorities could help and 38% did not think it was a
reportable offense. Other common reasons for not reporting crimes such as not knowing
who to report to, fearing retaliation, or embarrassment were less of an issue (see Table 9).
Implications for Practice
This may be the first quantitative study conducted on cyber-harassment in online
learning settings in higher education and thus it may be valuable for universities around
the world to consider before implementing policy to prevent and solve cyber-harassment
issues within or stemming from their online learning programs. From the findings of this
study, it is clear that cyber-harassment does exist in the online learning environment in
higher education. More importantly it informs the extent and nature of cyber-harassment
and its reporting in the setting. Those over age 35, and especially faculty, have the
highest chance of getting cyber-harassed although across the board there is enough cyber-
harassment to impel institutions to take action. Most online programs already have a
powerful weapon which already mitigates the rate of cyber-harassment. That is most
learning platforms are closed and members of the class are identified via a username and
password before logging in. The school knows exactly who is logged into an online
course at all times. As this study shows, though, the lack of anonymity does not
necessarily do the trick not to mention that the cyber-harassment that may start in the
course or as a result of information about others garnered in the course sometimes
continues outside an online course with a potentially greater degree of anonymity.

68

The online learning environment is still new. There are not hundreds of years of
norm setting already behind it. In a traditional classroom, the students generally know
what they are supposed to do and what behaviors are expected of them and faculty also
know what they should do and how to act appropriately. In the online learning setting,
however, norms are not yet universally agreed upon. There is a lot of variation on what
constitutes an online course form school to school and even instructor to instructor.
Universities and instructors must be much more explicit, not only in giving academic
directions, but also in setting expectations for exchanges and behavior. All schools with
online curriculum should have student conduct policies for online programs to set clear
expectations for behavior in online learning settings. One college that has such a policy
in place is Allan Hancock College ("Student Conduct Policy for Online Programs,"
2009). Although unpleasant, hurtful, or threatening emails or text messages‖ (6.1). and
―flaming,‖ online verbal abuse that took place ―during live chat or other interactive
activity‖ were the most common types of cyber-harassment, at a minimum all seven
cyber-harassment behaviors identified (see Table 5) by this study should be addressed by
those policies as each type of cyber-harassment had claimed victims. Schools should also
include educating students and faculty about these issues in their orientation programs.
Faculty should also imbed such policies or course specific behavioral guidelines into their
syllabi. Another general idea may be the monitoring of online courses to lookout and
head off such behavior. The idea may sound intrusive at first but no one questions
having at least some form of campus security on a physical campus. Since most
interactions are recorded in online courses, unless erased, another strategy may be when

69

cyber-harassment is identified as having happened in an online course, the record can be
looked at to determine why it happened to better inform future solutions.
It seems there is little that can be done about from a policy perspective regarding
one of the significant ways which faculty suffer cyber-harassment resulting from online
courses in the way of postings on professor rating sties or other public websites which are
often anonymous other than try to foster a culture of mutual respect on campus between
faculty and students.
In regards to facilitating the reporting of cyber-harassment, to address the 38%
rate of not reporting due to not thinking it was a reportable offense, clear guidelines of
what constitutes cyber-harassment in online learning should be made very clear. The
student conduct policies for online programs referenced above should fulfill this
objective but how they are disseminated is also important. This increased awareness
training will also increase reporting and may make it look like the campus has an
increasing problem for a time but authorities should recognize this increase will only be
an increase of reporting not of incidents.
The 43% rate of not reporting due to doubt that authorities could help may be
partially due to the type of incident where explicit training as mentioned in the previous
paragraph could help, but the campus climate could also have a major effect on not
reporting for this reason. To create a campus climate which encourages reporting, the
consequences and actions authorities will take if the student conduct policies for online
programs are violated should be made clear and applied speedy and consistently so
students and faculty know when they report cyber-harassment and it is verified, it will be

70

resolved. The best way to show that action will be taken is to publish outcomes (not
names) of cases that do occur. In general, educating students and faculty about what to
do and making it easier for them to report such as having a feedback or assistance button
inside an online course would also help to increase reporting and likely also to prevent
cyber-harassment from happening in or as a result of online courses.
Implications for Research
Future research on cyber-harassment in online learning settings in colleges and
universities may wish to use this study as a jumping off point. Future studies should
focus on finding out more about the most important characteristics related to cyber-
harassment in online learning identified by this study: age and faculty status. Only then
can solutions specific to those students be identified. A study that focuses more on age is
needed to find out why older students seem to have more trouble with cyber-harassment.
Is it caused by some sort of generational gap and therefore primarily an issue of
perception? People that grew up with the internet are definitely used to more informal
forms of written communication. Are older generations less tolerant of certain kinds of
online behavior? Is it a result of the ―facebook generation‖ not being able to code shift;
that is younger students bringing extremely informal or even rude behavior from one
online venue to the much more formal one of an online academic course? To help
answer this, a study determining the age of the perpetrators versus the victims would be
needed. Or are there just certain behaviors or characteristics about those currently over
age 35 that result in them having a greater chance of being cyber-harassed in or as a

71

result of an online learning setting? These are just some questions that may help form the
direction of future studies.
A study done purely on faculty is warranted to explore what seems to be a major
problem further. Most university faculty are over age 35 (including 84% of the faculty in
this study) so it could be that some of the same issues related to age are contributing to
the problem with faculty as well. Also, if the problem is as real as it seems, is it primarily
due to their greater exposure? A study that made the number of online courses taught a
key piece of the data collection and analysis could reveal exposure‘s exact importance. If
the problem is deeper, could it have to do with the status of the faculty member and their
power to give grades and thus is a greater target of retaliation? Or could it be related to
the above discussion on the expectations of formalities? Could a faculty‘s status
orientation be playing a role, in other words, their expectation for due respect for their
title and position which are not being satisfied as much online as in the classroom?
Another interesting question would be if age is the major factor or years of experience
teaching? The researcher favors the former at this point but the latter could not be ruled
out without a testing of the hypothesis. How the cyber-harassment rate varies for faculty
by discipline would also be useful to determine.
Some other general recommendations could be suggested for future studies of
cyber-harassment in and as a result of online settings in higher education. This study did
not find race to be a significant factor in the rate a student or faculty experienced cyber-
harassment in an online course but a larger size sample of a similarly diverse group
would be more effective in further verifying that finding. Also, this study did not track

72

multiple incidents of cyber-harassment beyond ―more than once.‖ A future study should
be designed to find out more about the nature of multiple incidents. Lastly, it was beyond
the scope of this study to find out whom the perpetrators of cyber-harassment in and as a
result of online learning were but finding out their age as noted above as well as their
status and demographics would help inform our understanding of the phenomenon.
Detecting whether perpetrators knew their victims personally would also be useful.
Finding out perpetrator characteristics and motivations is the other part of formulating a
clear picture of cyber-harassment in online learning in higher education.
Chapter Five Conclusion
Just as cyber-harassment has become a problem in middle schools, high schools,
and on college campuses, it has also made a negative impact within online learning in
higher education. This study found that 12% of students and 39% of faculty have been
the victims of cyber-harassment in or as a result of taking part in online education. This
much higher rate of cyber-harassment for faculty is statistically significant. Those
students and faculty over the age of 35 also suffered cyber-harassment at a higher and
statistically significant rate in or as a result of online courses. Less than half of those
cyber-harassed in this context reported it. As online learning continues to grow and
develop rapidly, hopefully this first set of quantitative data on the extent, nature, and
reporting of cyber-harassment in online courses will arm faculty and administrators with
information to help curtail it. Hopefully future scholars can also build upon these
findings.

73

References
1999 Report on Cyberstalking: A New Challenge for Law Enforcement and Industry.
U.S. Department of Justice.
Alexy, E. M., Burgess, A. W., Baker, T., & Smoyak, S. A. (2005). Perceptions of
Cyberstalking Among College Students. Brief Treatment and Crisis Intervention,
5(3), 279-289.
Allen, I. E., & Seaman, J. (2006). Making the Grade: Online Education in the United
States: The Sloan Consortium.
Allen, I. E., & Seaman, J. (2008). Online Education in the United States: The Sloan
Consortium.
Amada, G. (2009). Coping with Seven Disruptive Personality Types in the Classroom.
The Teaching Professor Online Seminar Retrieved 4/20, 2009, from
http://www.magnapubs.com/calendar/311.html?s=tm&p=email
Benbenishty, R., & Astor, R. A. (2005). School Violence in Context: Culture,
Neighborhood, Family, School, and Gender: Oxford Press.
Beran, T., & Li, Q. (2005). Cyber-Harassment: A Study of a New Method for an Old
Behavior. Journal of Educational Computing Research, 32(3), 265-277.
Berg, G. A. (2005). Lessons from the edge: For-profit and nontraditional higher
education in America New York: Praeger Publishers.
Binns, A. (2007). Staff suffer bullying by students on the web. Times Higher Education
Supplement(1783), 8-9.
Bocij, P. (2004). Cyberstalking: Harassment in the Internet Age and How to Protect Your
Family. Westport, CT: Praeger.
Brabazon, T. (2002). Digital hemlock : Internet education and the poisoning of teaching:
Sydney University of New South Wales.
Bureau of Justice Statistics. Retrieved 5/15/09, from www.ojp.usdoj.gov/bjs/
By the Numbers: Cyberbullying. (2005). Counseling Today, 47(12), 3-3.

74

California Coalition Against Sexual Assault. (2004). 2004 report: Research on rape and
violence. Sacramento, CA: California Coalition Against Sexual Assault.
Cercone, K. (2008). Characteristics of Adult Learners with Implications for Online
Learning Design. AACE Journal, 16(2), 137-159.
Chapell, M., Casey, D., Cruz, C. D. l., Ferrell, J., Forman, J., Lipkin, R., et al. (2004).
Bullying in College by Students and Teachers. Adolescence, 39(153).
Chapell, M., Hasselman, S. L., Kitchin, T., Lomon, S. N., MacIver, K. W., & Sarullo, P.
L. (2007). Bullying in Elementary School, High School, and College.
Adolescence, 41(164).
College gossip web site shuts down, citing economy [Electronic. (February 5, 2009).
Version]. USA Today. Retrieved 2/5/2009, from
http://www.usatoday.com/tech/webguide/internetlife/2009-02-05-
juicycampus_N.htm
D‘Ovidio, R., & Doyle, J. (2003). A study on cyberstalking: Understanding investigative
hurdles. FBI Law Enforcement Bulletin, 72(3), 10-17.
Dibbell, J. (December 23, 1993 ). A Rape in Cyberspace: How an Evil Clown, a Haitian
Trickster Spirit, Two Wizards, and a Cast of Dozens Turned a Database Into a
Society [Electronic Version]. The Village Voice. Retrieved February 13, 2009,
from http://www.juliandibbell.com/texts/bungle_vv.html
Finn, J. (2004). A survey of online harassment at a university campus. Journal of
Interpersonal Violence, 19(4), 468(416).
Fisher, B., Cullen, F. T., & Turner, M. G. (2000). The sexual victimization of college
women. Washington, DC: U.S. Dept. of Justice, Office of Justice Programs,
National Institute of Justice.
Fisher, B. S., Daigle, L. E., Cullen, F. T., & Turner, M. G. (2003). Reporting sexual
victimization to the police and others: Results from a national-level study of
college women. Criminal Justice and Behavior, 30(1), 6-38.
Fluck, U., Clouse, S. F., & Shooshtari, N. H. (2007). Reducing ethnocentrism in
international business students with an online multicultural supplement. Journal
of Teaching in International Business, 18(2/3).
Friedman, T. L. (2005). The World is Flat: A Brief History of the 21st Century. New
York: Farrar, Straus, and Giroux.

75

Gearon, C. J. (2008). Back-to-School Days, for Adults, U.S. News & World Report (Vol.
144, pp. 46-48): US News & World Report.
Glendenning, P. M. (2001). Workplace bullying: Curing the cancer of the American
workplace. Public Personnel Management, 30, 269-286.
Goldsborough, R. (2004). Taking a Bite Out of Cyber Crime. Community College Week,
16(25), 19-19.
Granello, D. H., & Wheaton, J. E. (2004). Online Data Collection: Strategies for
Research. Journal of Counseling & Development, 82(4), 387-393.
Hawke, C. S. (2001). Computer and Internet use on Campus : A Legal Guide to Issues of
Intellectual Property, Free Speech, and Privacy. San Francisco, CA: Jossey-Bass.
Herring, S. (October 2001). Gender and Power in Online Communication [Electronic
Version], from http://rkcsi.indiana.edu/archive/CSI/WP/WP01-05B.html
Hopkins, K. (January 19, 2005). 'Cyber-rape' outlawed: N.J. cracks down on computer-
aided crime [Electronic Version]. Computer Crime Research Center, , . Retrieved
February 13, 2009, from http://www.crime-research.org/news/19.01.2005/910/
juicycampus.com. (2008). Retrieved 10/24/08, 2008, from www.juicycampus.com
Juvonen, J., & Gross, E. F. (2008). Extending the School Grounds? Bullying Experiences
in Cyberspace. Journal of School Health, 78(9), 496-505.
Karjane, H. M., Fisher, B., S., & Cullen, F. T. (2002). Campus sexual assault: How
America’s institutions of higher education respond. Retrieved October 2, 2008.
from http://www.hhd.org/products/mso44.pdf
Katyal, N. K. (2001). Criminal Law in Cyberspace. University of Pennsylvania Law
Review, 149(4), 1003-1114.
Ko, S., & Rossen, S. (2004). Teaching Online: A Practical Guide Boston: Houghton
Mifflin.
Kolowich, S. (2009, March 13). Academic 'Cyberbully' Is Accused of Harassing Judaic
Researchers. The Chronicle of Higher Education.
Kotler, S. (May 14, 2009). Cyberbullying Bill Could Ensnare Free Speech Rights
[Electronic Version]. FOXnews.com. Retrieved 5/14/09, from

76

http://www.foxnews.com/politics/2009/05/14/cyberbullying-ensnare-free-speech-
rights/
Lederman, D. (2008, February 22). What makes a college ‗military friendly‘? Inside
Higher Education.
Lenhart, A. (2007). Data memo: Cyberbullying and online teens. Retrieved 4/19, 2009,
from
http://www.pewinternet.org/~/media/Files/Reports/2007/PIP%20Cyberbullying%
20Memo.pdf.pdf
Li, Q. (2006). Cyberbullying in Schools: A Research of Gender Differences. School
Psychology International, 27(2), 157-170.
Li, Q. (2007). New bottle but old wine: A research of cyberbullying in schools.
Computers in Human Behavior, 23(4), 1777-1791.
Limburg, F., & Clark, C. (2006). Diversity initiatives in higher education: Teaching
multicultural education online. (Higher Education). Multicultural Education,
13(3), 49-55.
Long, C. (2008). Silencing Cyberbullies. NEA Today, 26(8), 28-29.
Lucas, C. (2006). American Higher Education: A History (2 ed.). New York: Palgrave
Macmillan.
Lynn, R. (May 4, 2007). Virtual Rape Is Traumatic, but Is It a Crime? [Electronic
Version]. Wired. Retrieved March 10, 2009, from
http://www.wired.com/culture/lifestyle/commentary/sexdrive/2007/05/sexdrive_0
504
Lyons, J. (2006). An exploration into factors that impact upon the learning of students
from non-traditional backgrounds. Accounting Education, 15(3), 325-334.
Lyons, J. F. (2004). Teaching U.S. History online: Problems and prospects. The History
Teacher, 37(4).
Machanic, M. (1998). Gender and power issues in on-line learning environments. Paper
presented at the 1st International Conference on the Social Impacts of
Technology.

77

Machanic, M. (October 1998). Gender and power issues in on-line learning
environments. Paper presented at the 1st International Conference on the Social
Impacts of Technology.
Many Teens Spend 30 Hours A Week On 'Screen Time' During High School [Electronic.
(2008). Version]. Science Daily. Retrieved 4/19/2009, from
http://www.sciencedaily.com/releases/2008/03/080312172614.htm
Merryfield, M. M. (2001). The paradoxes of teaching a multicultural education course
online. Journal of Teacher Education, 52(4), 283-299.
Miller, C. (2006). Cyber harassment: its forms and perpetrators. Law Enforcement
Technology, 33(April 2006), 26.
O'Neil, R. M. (2008). It's Not Easy to Stand Up to Cyberbullies, but We Must. Chronicle
of Higher Education, 54(44), A23-A23.
Olweus Bullying Prevention Program. (2009). Retrieved 4/18/09, from
http://www.olweus.org/public/bullying.page
Olweus, D. (1993). Bullying at School: What we Know and What We Can Do.
Cambridge, MA: Blackwell.
Palloff, R. M., & Pratt, K. (2003). The virtual student: a profile and guide to working
with online learners. San Francisco, CA: Jossey-Bass.
Patton, M. Q. (2002). Qualitative Reasearch and Evaluation Methods. Thousand Oaks,
CA: Sage Publications, Inc.
Riedel, M., & Welsh, W. (2008). Criminal Violence: Patterns, Causes and Prevention (2
ed.). New York: Oxford University Press.
Shariff, S. (2003). A System on Trial: Identifying Legal Standards for Educational,
Ethical and Legally Defensible Approaches to Bullying in Schools. Unpublished
Doctoral Dissertation. Simon Fraser University.
Shariff, S. (2008). Cyber-bullying: Issues and Solutions for the School, the Classroom
and the Home. New York: Routledge.
Shariff, S., & Johnny, L. (2007). Cyber-Libel and Cyber-Bullying: Can Schools Protect
Student Reputations and Free-Expression in Virtual Environments? Education
Law Journal, 16(3), 307-342.

78

Shepherd, J. (2006). Staff fear bullying on student rating site. Times Higher Education
Supplement(1736), 60-60.
Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008).
Cyberbullying: its nature and impact in secondary school pupils. Journal of Child
Psychology and Psychiatry, 49(4), 376-385.
State of New York Division of Criminal Justice Services. (2008). Retrieved 10/5, 2008,
from http://criminaljustice.state.ny.us/missing/i_safety/track_stalk.htm
Stop Cyber-bullying. (2008). Retrieved 10/24, 2008, from
http://www.stopcyberbullying.org
Student Conduct Policy for Online Programs. (2009). Retrieved 4/20/09, from
http://www.hancockcollege.edu/DistanceLearners/conduct.asp
Tavani, H. T., & Grodzinsky, F. S. (2002). Cyberstalking, personal privacy, and moral
responsibility. Ethics and Information Technology, 4(2).
Trow, M. (2001). From mass higher education to universal access: The American
advantage. In P. G. Altbach, P. J. Gumport & D. B. Johnstone (Eds.), In Defense
of American Higher Education (pp. 110-143). Baltimore and London: Johns
Hopkins University Press.
Tu, C.-H. (2002). The measurement of social presence in an online learning environment.
International Journal on E-Learning, 1(2), 34(12).
U.S. Department of Education. (2006). A test of leadership: Charting the future of U.S.
higher education. Washington, DC.
U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.
(2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-
Analysis and Review of Online Learning Studies. Washington D.C.
U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics.
(1997). Special Report, Sex Differences in Violent Victimization, NCJ-164508
Virtual Environments Enable New Models of Learning. (2009). from
http://secondlifegrid.net/slfe/education-use-virtual-world
Wheeler, M. (2010, February 3). Cyberspace, Bullying And Suicide -- Again. Huffington
Post, from http://www.huffingtonpost.com/madeline-wheeler/cyberspace-
bullying-and-s_b_447034.html

79

Winchester, D. (2009, March 3, 2009). Cyberbullying on the rise. St. Peterburg Times,
WiredSafety.org — the world's largest Internet safety and help group. (2008). Retrieved
10/5, 2008, from http://www.wiredsafety.org/
Ybarra, M. L., Mitchell, K. J., Wolak, J., & Finkelhor, D. (2006). Examining
characteristics and associated distress related to internet harassment: Findings
from the second youth internet safety survey. Pediatrics, 118, 1169-1177.

80

Appendix: Survey Questions

81

82

83

84

85

86

87

88

Abstract (if available)

Abstract There is burgeoning scholarship relating to cyber-bullying in middle and high school settings. When cyber-bullying occurs among adults it is known as cyber-harassment. Online higher education has exploded in terms of growth in the last several years. Related literature including university policies and online teaching guides suggested there may be a cyber-harassment problem in online learning in higher education as well although no quantitative evidence of cyber-harassment in online learning in higher education currently exists. The purpose of this study was to use a quantitative approach to explore the nature of and extent to which students and faculty experience and report cyber-harassment in and as a result of the online learning settings of colleges and universities. The study was conducted at a large, private, non-profit, liberal arts university in Hawaii. The study is based on 225 online student participants and 56 online faculty participants. Of the participants it was found that 12% of students and 39% of faculty were the victims of cyber-harassment in or as a result of an online course. This much higher rate of cyber-harassment for faculty is statistically significant. Those students and faculty over the age of 35 also suffered cyber-harassment at a higher and statistically significant rate in or as a result of online courses. Less than half of those cyber-harassed in this context reported it. Preventions and solutions for practice and prescriptions for future research are recommended.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

A comparative study of motivational predictors and differences of student satisfaction between online learning and on-campus courses

PDF

Secondary school violence and Hawai’i’s mahu population

PDF

Online, flipped, and traditional instruction: a comparison of student performance in higher education

PDF

Investigating differences in online & traditional student perceptions of value and satisfaction with five key educational elements

PDF

The journey: Asian American females in higher education administration

PDF

Recruiting and hiring online learning teachers for online high schools

PDF

African-American/Black students’ experience and achievement in asynchronous online learning environments at a community college

PDF

Full-time distance education faculty perspectives on web accessibility in online instructional content in a California community college context: an evaluation study

PDF

Reducing statistics anxiety among learners in online graduate research methods courses

PDF

A gap analysis of course directors’ effective implementation of technology-enriched course designs: An innovation study

PDF

Preparing university faculty for distance education: an evaluation study

PDF

Perspectives of learning in synchronous online education

PDF

The implementation of online learning for ESL programs: factors and perspectives

PDF

Internationalization of higher education: a case study of three Korean private universities

PDF

Impact of technology on teaching and learning practices at high‐technology use K-12 schools: a case study

PDF

A methodology for transforming the student experience in higher education: a promising practice study

PDF

Creating effective online educational content: an evaluation study of the influences affecting course developers’ abilities to create online content with engaging learning strategies

PDF

The relationship of students' self-regulation and self-efficacy in an online learning environment

PDF

Ensuring faculty success in effective online instruction

PDF

Exploring community in an online doctoral program: a digital case study