Interactive Learning Environments
ISSN: 1049-4820 (Print) 1744-5191 (Online) Journal homepage: https://www.tandfonline.com/loi/nile20
Read-only participants: a case for student
communication in online classes
L. Nagel , A. S. Blignaut & J. C. Cronjé
To cite this article: L. Nagel , A. S. Blignaut & J. C. Cronjé (2009) Read-only participants: a case
for student communication in online classes, Interactive Learning Environments, 17:1, 37-51, DOI:
10.1080/10494820701501028
To link to this article: https://doi.org/10.1080/10494820701501028
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Interactive Learning Environments
Vol. 17, No. 1, March 2009, 37–51
Read-only participants: a case for student communication in online classes
L. Nagela*, A.S. Blignautb and J.C. Cronjéc
a
University of Pretoria, South Africa; bNorth-West University, South Africa; cCape Peninsula
University of Technology, South Africa
(Received 5 April 2007; final version received 25 May 2007)
The establishment of an online community is widely held as the most important
prerequisite for successful course completion and depends on an interaction between
a peer group and a facilitator. Beaudoin reasoned that online students sometimes
engage and learn even when not taking part in online discussions. The context of this
study was an online course on web-based education for a Masters degree in
computer-integrated education at the University of Pretoria. We used a mixed
methodology approach to investigate how online activity and discussion postings
relate to learning and course completion. We also investigated how student
collaborative behaviour and integration into the community related to success.
Although the quantitative indices measured showed highly significant differences
between the stratifications of student performance, there were notable exceptions
unexplained by the trends. The class harboured a well-functioning online learning
community. We also uncovered the discontent students in the learning community
felt for invisible students who were absent without reason from group assignments
or who made shallow and insufficient contributions. Student online visibility and
participation can take many forms, like read-only participants who skim over or
deliberately harvest others’ discussions. Other students can be highly visible without
contributing. Students who anticipate limited access due to poor connectivity, high
costs or other reasons can manage their log-in time effectively and gain maximum
benefit. Absent and seldom contributing students risk forsaking the benefits of the
virtual learning community. High quality contributions rather than quantity builds
trust among mature students. We suggest how to avoid read-only-participation:
communicate the required number of online classroom postings; encourage
submission of high quality, thoughtful postings; grade discussions and give
formative feedback; award individual grades for group projects and rotate members
of groups; augment facilitator communication with Internet-independent media to
convey important information. Read-only-participants disrupt the formation of a
virtual community of learners and compromise learning.
Keywords: higher education; web-based learning; participation; lurkers; virtual
community of learners
Background
As more formal education courses are available online, quality and non-completion
remain problems:
While online course enrolments continue to climb, retention and success rates in such
courses and programs are frequently reported as typically lower than those delivered in
*Corresponding author. Email: lynette.nagel@up.ac.za
ISSN 1049-4820 print/ISSN 1744-5191 online
Ó 2009 Taylor & Francis
DOI: 10.1080/10494820701501028
http://www.informaworld.com
38
L. Nagel et al.
a traditional classroom format; those of us in roles that support online students have a
role in reversing that trend! (Schreck, 2006)
Researchers often measure the success of online learning as students’ perception
of learning and course throughput rates. Drop-out rates for online courses range
from 20 to 50%, often 10–20% higher than for equivalent contact courses
(Bernard, Brauer, Abrami, & Surkes, 2004). Searching for a model to predict
student success in online learning, Bernard et al. (2004) found that students’ frame
of mind can predict readiness for learning and affect course outcomes, while ‘‘prior
achievement is still the best predictor of future achievement’’ (Bernard et al., 2004,
p. 44).
Research shows that online participation is necessary to ensure successful course
completion (Klemm, 1998; Rovai & Barnum, 2003; Swan, Shea, Fredericksen,
Pickett, & Pelz, 2000). Clark and Feldon (2005) concluded that a facilitator who
participates and interacts with students prevents them from abandoning their course.
Better cognitive outcomes occur when students engage and form a virtual
community of learners. The development of a community depends on online
interaction with their peers and the facilitator. Learner satisfaction, perseverance,
and cognitive outcomes characterize the formation of a virtual learning community.
Some contest participation as a prerequisite to learning, claiming students learn
sufficiently by observation (Beaudoin, 2002; Sutton, 2001), and lobby for leniency
towards lurking or read-only participation. This article responds to Beaudoin’s
(2002) article ‘‘Learning or lurking? Tracking the ‘invisible’ online student.’’ He
reasoned that students sometimes engage and learn even when not taking part in
online discussions with faculty and other students and showed that low profile
students:
spend a significant amount of time in learning-related tasks, including logging on, even
when not visibly participating, and they feel they are still learning and benefiting from
this low-profile approach to their online studies. (p. 147)
We investigated the importance of student online ‘‘visibility’’ apparent in the
quantity and quality of participation. We explored as a case study the successful
completion of a postgraduate online course by asking the following research
questions.
(1) How did online participation relate to learning and successful course
completion?
(2) How did participation influence the learning community?
Literature
The debate on online participation
Taking part in discussions
A learning management system (LMS) tracks progress and performance and
reveals students who do not log in to their online classroom or who log in without
participating. Klemm (1998) blamed classroom-based teaching where students
expect entertainment for conditioning them to passive learning. Therefore, they
seldom realize the benefits of participating actively in online discussions, naturally
Interactive Learning Environments
39
lurking. Well-facilitated online discussions can be more inclusive than classroom
discussions by including introverted students and enabling better quality
interaction (Cox, Carr, & Hall, 2004; Prammanee, 2003). Rovai and Barnum
(2003) claimed that passive online learning through ‘‘listening’’ without
participation produces no measurable increase in knowledge, as they could
predict perceived learning through the number of messages posted. Others have
also reported that distributed students who participate in dynamic discussions had
better course completion rates and that failing students interacted less frequently
(Davies & Graff, 2005; Swan et al., 2000). Active online participation also benefits
learning.
Improved learning
Deep cognitive learning (Prammanee, 2003) and high levels of interactivity are
possible in online discussions, as students can prepare well-considered contributions
(Kettner-Polley, 2005). According to Carr, Cox, Eden, and Hanslo (2004), students
who focused on building knowledge and collaborative interactions had a superior
average performance, as challenging online interactions promote understanding.
Interactive learning provides an instructor with insight into student misconceptions,
difficulties, conceptual problems, and verbal pitfalls. Asking leading questions elicits
insights into what students understand, more than simply telling them the answer.
Immediate feedback from their peers and instructors and social interaction built into
the online discussions contribute to learning (Collins, Brown, & Holum, 1991).
Collaborative learning activities contribute to deep learning, critical thinking skills, a
shared understanding, and long-term retention (Garrison, Anderson, & Archer,
2001).
Consistency in course design, interaction with course instructors, and active
discussion—have been consistently shown to significantly influence the success of
online courses. It is posited that the reason for these findings relates to the importance of
building community in online courses. (Swan et al., 2000, p. 513)
Community of learners
Interaction is conducive to the emergence of a community of practice (Collins et al.,
1991) and a virtual community of learners (Collison, Elbaum, Haavind, & Tinker,
2000). Learning from your peers in a structured way can ameliorate the social isolation
online students often experience (Boud, Cohen, & Sampson, 1999). Collaborative
learning groups solve problems while sharing and clarifying ideas (Cox et al., 2004). In
a collaborative learning environment students develop critical thinking skills and a
shared understanding and deep learning, while retaining learning over the long term
(Garrison et al., 2001). In a community of practice novices learn from experts by
observing authentic tasks and executing progressively more advanced tasks themselves
under an expert eye (Johnson, C. S., 2001). Complex tasks can be learnt in a
community of practice wherein ‘‘participants actively communicate about and engage
in the skills involved in expertise’’ (Collins et al., 1991, p. 16). Frequent, meaningful,
valued, and dynamic discussions in an online course lead to the formation of a virtual
learning community where students interact and support each other. According to
Collison et al. (2000), members of a healthy online community of learners post
regularly and collaborate with other participants, as well as teach and moderate the
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L. Nagel et al.
online discussions spontaneously. Group cohesion, trust, respect, and belonging further
characterize a community of learning (Kreijns, Kirschner, & Jochems, 2003). The
formation of a community cannot be taken for granted. Some students do not
participate fully.
The case for read-only participation
Legitimate non-participation
Non-participation may initially be legitimate, as peripheral online learners make
limited entrances into the community, remaining on the outskirts, observing the
activities of more advanced participants and learning from it (Collins et al., 1991).
Sutton (2001, p. 223) also reasons that ‘‘direct interaction is not necessary for all
students and that those who observe and actively process interactions between others
will benefit through the process of vicarious interaction.’’ As students increase their
expertise, they move from the periphery to the centre (Carr et al., 2004), with
increasing visibility. Beaudoin (2002) found that invisible students sometimes ‘‘spend
a significant amount of time in learning-related tasks, including logging on, even
when not visibly participating, and they feel they are still learning and benefiting
from this low-profile approach to their online studies’’ (p. 147). Williams (2004)
advocated using the term read-only participants (ROP) rather than the derogatory
lurker for non-participatory students and vicarious interactors. He cautioned that
while the ROPing students may be satisfied that their learning needs are met, they do
not contribute to the larger community.
Inadvertent non-participation
Students do not actively participate in online discussions because they procrastinate,
they feel isolated, or they’re unfamiliar with the technology. They may also miss the
course structure or control of discussions and therefore remain unconvinced of the
course’s benefits (Miller, Rainer, & Corley, 2003). Patterns of online participation
and interaction can vary across cultural groups. In many developing countries the
digital divide is increasing, due to an inadequate infrastructure and few Internet
subscriptions (Roycroft & Anantho, 2003). The exclusive use of English in nonEnglish speaking cultures, economic development, and available bandwidth also
affect student success.
Facilitator participation
Student interaction is not the only factor influencing collaboration, learning, and
successful course completion. Students become more involved in an online
conference when the facilitator participates as guide, providing extensive critique,
feedback, and encouragement (Collison et al., 2000). An effective learning
community requires an instructor with integrated social, cognitive, and teaching
presence (Cox et al., 2004). Facilitators should teach critical thinking, effective
communication, and problem-solving skills (Shavelson & Huang, 2003). The current
vogue to embrace a constructivist pedagogy where the instructor withdraws from the
online learning environment, allegedly to promote discovery and experimental
learning activities, is unsubstantiated (Kirschner, Sweller, & Clark, 2006).
Interactive Learning Environments
41
Automated e-learning or a lurking instructor presents an even greater impediment to
learning than do lurking students.
Context of this study
We presented an 8 week course on web-based distance learning to Masters
students on a computer-integrated education course at the University of Pretoria.
This was an elective course in a programme usually presented in blended contact
and online mode. We delivered this course entirely online using the WebCTTM
Campus Edition as the LMS. The delivery mode enabled enrolment of a diverse
cohort of 22 geographically distributed students with ages ranging from nearly 30
to nearly 50. The student ages represent baby boomers and generation X
(Oblinger, 2003). The course followed a constructivist approach and consisted
equally of theoretical and practical applications structured around eight salient
online learning topics. Each week the students had to research online scholarly
literature on the topic and post their contribution to the LMS discussions area,
where they also posted peer reviews. Concurrently, students had to create webbased artefacts applying the theory. We provided formative feedback during the
course and assessed students using integrated assessment of authentic tasks,
focusing on outcomes.
In the latter half of the course students also created two rounds of group
assignments in teams of five to seven, as experience of collaborative online work
was a course outcome. One of these was a rubric to score online collaborative
behaviour, strongly taking into account their contributions to group assignments.
Participating in discussions, replying to pleas for help and offering tips and advice
completed the tally. Students used this rubric to allocate a collaboration score for
each student that contributed 10% to their year mark. The other 90% derived
from research postings, web artefacts, peer review, and collaborative assessment.
The final course grade also included their reflective examination essays, depicting
their writing skills. Unlike Davies and Graff (2005), we did not use their final
course grade as an indication of success. Instead, we used the ongoing year mark
that reflected a wider spectrum of mastery and application.
We observed students’ experiences with online learning through multiple
windows. These consisted of their private blogs (only shared with the facilitator)
for reflection and self-assessment, open paragraph questions included in an online
quiz, a reflective essay, and feedback questions e-mailed to the students about one
month after completion of the course. The facilitator also documented observations
in a diary.
Methodology
The course presenters simultaneously conducted research, using a mixed
methodology (Sharp & Fretchling, 1997). A qualitative methodology allowed us
to probe the context of the non-participating students and the class’s perceptions
and reactions. We conducted content analysis using ATLAS.tiTM software on the
following primary documents: students’ blog postings, 1615 discussion posts, an
online quiz, and examination essays. Representative quotes from student postings
are in their original form, reflecting their use of English as a second language. We
validated the findings against the facilitator’s field notes and used multiple
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L. Nagel et al.
documents and perspectives. The researchers also facilitated the online course and,
as participant observers, ensured the reliability of the findings.
The student tracking tool in the LMS provided a quantitative view of student
activity in the course, including the numbers of original postings and replies. The
WebCT Campus edition student tracking tool maintains a record of the number of
times a student accesses the various course areas. The term ‘‘hits’’ is defined in the
WebCT help pages as ‘‘the number of times the student accessed the Homepage, a
tool [including the items read or posted in discussions], or a content module page.’’
We calculated their reply ratio by dividing the number of replies to others by their
own original posts. Table 1 ranks students according to their year mark and shows
the students’ numbers of hits in the LMS and discussion messages posted, their reply
ratio, collaboration score, and whether they returned the voluntary post-course
feedback. Unlike the rest, the collaboration score is a qualitative measurement
obtained by using a rubric to assess each student’s collaborative behaviour.
We represent student online activities using the assumptions of Davies and Graf
(2005), who categorized students according to final course grades. Our grade
categories reflected the assessment stratification used in South African Higher
Education. One student abandoned the course very early, and we did not include this
data. We stratified the rest of the class into three grade group categories: a Fail group
for students who did not complete the entire course or achieved less than 50%; a
Pass group of students who aggregated between 50% and 74%. Those with 75% or
more we called Distinction candidates. One student (subject 6) changed categories
after the final essay and passed the course. We used this stratification for all
statistics.
Table 1.
Summary of individual student grades and participation profile.
Subject no.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
a
Year mark
Hits
Messages
Reply ratio
posted
Collaboration
score
a
424
244
1161
1706
871
223
1406
966
776
844
1503
1758
1093
1487
1675
1810
963
1165
1226
1853
2980
24
14
30
50
50
10
68
54
30
36
73
58
37
104
53
126
43
68
68
148
112
0.8
1.8
0.4
0.9
1.4
0.1
1.5
1.3
1.0
1.3
1.1
1.5
1.5
2.7
2.3
3.2
1.0
1.8
2.0
2.7
1.7
0
0
2
6
3
0
3
7
8
5
8
3
9
9
8
10
8
9
9
10
9
a
a
a
38.8
48
53
60.2
60.9
61.6
63.1
64
66
66.3
70.2
80
80.3
80.9
83.8
85.4
88.5
Student voluntarily abandoned the course before submitting the final examination essay.
Feedback
submitted
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Interactive Learning Environments
Table 2.
43
Average number of hits, posts and follow-up posts per student in grade groups.
Grade group
N
Hits
Posts
Reply ratio
Collaboration
Feedback (%)
Fail
Pass
Distinction
H value/w2
Significance
6
9
6
771.5
1278.7
1666.2
H ¼ 26.3
4.001
30
57
94
H ¼ 34.5
4.001
1.06
1.43
2.06
H ¼ 24.7
4.001
2.2
6
9.2
H ¼ 52.8
4.001
17
44
83
w2 ¼ 47
4.01
Figure 1.
Average dimensions for each grade group.
Like Davies and Graff (2005), we used the Kruskall–Wallis non-parametric test
to investigate the significance of differences in online activities among these grade
groups. We also calculated the significance of the difference in return rates of
voluntary questions using w2 with two degrees of freedom, as shown in Table 2.
Figure 1 shows a graphical representation of the values given in Table 2. We show
the average value for each criterion for each of the grade groups.
Discussion
Student online visibility and learning success
Like Beaudoin (2002), we did some tracking of our ‘‘invisible’’ students, trying to
pinpoint reasons for their invisibility, as well its effects. We compared their online
participation profiles and indicators of their integration into the virtual community
with their success in completing the course. Interested in improving course
completion rates, we first identified the unsuccessful students, to see if their
participation differed from the others.
Student LMS hits
One can approximate students’ participation in the online classroom quantitatively
by the number of times they open pages, read discussions, or post, as shown in
Figure 1a. It shows that the student group that aggregated a failing grade or did not
complete the course opened significantly fewer pages than the successful students.
Their average of less than 800 implied that they saw only about half the online
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material in the course. Students who achieved distinctions read even more than did
the average students.
Learning success depends on the interaction with reliable technology (Swan, 2003).
The digital divide running through the infrastructure and economic and cultural
dimensions (Roycroft & Anantho, 2003) influences connectivity and participation.
Students whose infrequent log ins rendered them invisible compromised their success.
The blogs revealed that students employed in the e-learning industry had practically
unlimited bandwidth with state of the art computers and software. Others made do
with much less and singled it out in the quiz as their biggest challenge:
Costly and demanding financially, time consuming, stressful . . . . Not for the poor
people, under privileged students can be dropouts (Q).
Students experienced other technical problems that compounded their infrequent
Internet connectivity:
Sometimes my (dial-up) connection was not reliable. (Q)
There are moments during this module where in my area I experienced a number of
electricity cuts and this kept me anxious and waiting to get started with work. (E)
Some students showed resilience in coping with poor infrastructure, regular
electricity cuts, and poor connectivity; they managed successfully without
compromising their studies. For others, technological problems were overwhelming.
. . . first three weeks of the course I couldn’t work productively because of constant
trouble with my PC (wrong Internet Explorer program, needed Java program to read
and send information and finally got the Blaster virus). This made me very aware of the
high-dependency on technology in the e-learning world. No Computer—No learning—
No success. (E)
It is not always clear why some students persist against enormous odds while
others give up. Motivation possibly played a role for the last two students, as the
student with the electricity problems required the credits to graduate. Students
perceived connectivity as the reason for erratic peer contributions, as they did not
‘‘see’’ the lurkers, but noticed that some withheld contributions.
When some of the peers are struggling for access, their level of contribution is
hampered.
We are a nice bunch enrolled for this course. Some learners easily share and are
spontaneous, while others hold back.
Even opening numerous online pages (Table 1) does not always indicate
participation. Rovai and Barnum (2003) cautioned that attending courses without
participation produces no measurable increase in knowledge and students who wish
to pass just through attendance do not succeed. Learning requires interaction not
only with the content, but also with co-learners (Swan, 2003).
The number of discussion posts
The majority of Discussion posts were compulsory and provided a view on peer
group contributions. Figure 1b depicts the extent of student participation. Like hits,
there was a significant difference between the numbers of postings from the students
Interactive Learning Environments
45
in different grade groups. Students who failed or abandoned the course posted on
average significantly fewer discussions than their successful counterparts, confirming
Davies and Grafs (2005) results. We also observed a significant difference between
average and excellent students, a trend Davies and Graff could not indicate.
On average, the high performing students were also most active in the
discussions. There were also average performing students (subjects 7 and 14) who
posted a proliferation of messages, constituting ‘‘noise’’ in the discussions (Williams,
2004), and an excellent performer (subject 17) who posted few (Table 1), reminiscent
of vicarious or read-only participation. The number of posts, therefore, does not
reflect student involvement.
Ratio of replies to original posts per student grade group
This metric indicated a student’s style of participation, whether peer focused or selffocused, and is independent of participation quantities. From Figure 1c it is evident
that the more successful students more readily interacted with their peers. Successful
students replied two or three times more often to other posts than they initiated
original posts. The less successful students’ replied less often than they originally
posted. The difference between all groups is highly significant (Table 2). These
observations confirm that, after a minimum interaction establishing the necessary
support, the quality and dynamics of interaction further influenced online learning
(Davies & Graff, 2005). This metric still does not indicate the real quality of
contributions. To encourage rational discourse Klemm (1998) urged facilitators to
grade on the quality of the postings and not to settle for mere opinions. Absent
students and those who contribute little of value or virtually ‘‘nod’’ their approval in
threaded discussions do not deceive their peers (Collison et al., 2000).
Quality participation
Klemm (1998) proposed using peer groups to grade the value of each person’s
contribution. Therefore, we designed one team assignment to develop a rubric for
scoring online collaborative behaviour. The collaboration score (Figure 1d) is an
average of assessments by two peers and the facilitator using this rubric. While
rudimentary, it indicates how students rated others’ participation. Like all previously
discussed quantitative measurements of student activity in the online classroom, the
collaboration score showed highly significant differences among the three stratifications of students, as unsuccessful students had low collaboration scores and the
highly successful ones scored highest. Interpretation of the scores is problematic, as
again there are notable exceptions. Subjects 6 and 12 (Table 1) logged in often, but
they did not score high on collaboration and presented themselves as classic readonly participants.
We also used peer review extensively as a mechanism to improve interaction and
learn collaboratively (Boud et al., 1999). The transparent learning gave students
insight into each other’s work. Most students were positive about the peer
assessment process and realized the advantages:
With traditional learning, nobody really has access to your assignments, except if you
want them to. To me e learning proofed to be a very transparent way of learning. For
the first time in my life I had freely access to everybody else’s assignments. I were able to
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position myself, to compare my own writing and most important learn from others. I
was intrigued by the differing viewpoints from which the assignments were approached.
Peer assessment sharpens a student’s responses—the student knows he cannot ‘‘get
away’’ with lazy work.
While the non-contributing students may be satisfied that their learning needs are
met (Beaudoin, 2002), they do not contribute to the benefit of the community. We
contend that the quality of a student’s contributions to the course reflects integration
into the community.
Group participation
Cooperative group assignments encourage students to participate online. As
previous teamwork in this programme resulted in much unresolved conflict, we
scheduled group assignments in the latter half of the course and allocated a smal
portion of the grades to these activities. The rationale for using group work was
teaching students the challenges of working in distributed online groups. Despite
online support in the form of dedicated discussion groups and synchronous chat
rooms to ease the management of their assignments, some students participated
insufficiently and created discontent. Prodded in the quiz, numerous students
indicated teamwork as the biggest challenge in the course:
Collaborative work via the Internet (was) very difficult.
Team work—the response from people, ways of communicating within the group and I
‘‘think’’ the ability for people to ‘‘ignore’’ the postings in the hope that other people in
the group would do it.
The challenge of online teamwork also emerged as a prominent theme in students’
reflective essays at the end of the course.
The chat rooms were functioning well and the teams worked together beautifully.
Unluckily not all team members could participate here.
I really HATE working in a group. My attitude is not to depend on others, and to make
sure that I don’t need to rely on others. I trust myself and my own work most of all. This
all in all makes me a VERY bad team player!
As I expected, only three team members were actively involved during the group work
assignment. We were supposed to be seven in the team.
It was once again not a very satisfactory experience, because only a few group members
participated.
Team work, this proved to be a challenge. As the nominated team captain, I learnt a few
lessons; these being people are demanding, they wanted to know I was online and on
track. There were those people who tried to participate but when the chips were down
and timelines tight they were nowhere to be found. Then there were those people whom
I knew I could rely on, it seemed a bit of performance punishment, but they just got
more work to do, because I knew they would cope. Working in a team online, there are
still those who just don’t get the meaning of the word team.
Group membership rotated. In constructivist fashion, students self-organized
their groups and appointed their own leaders. Organization and leadership in online
teams exhibit distinct dynamics. ‘‘In contrast to face-to-face teams, the leadership
role of virtual teams is shared among team members’’ (Johnson, S. D., Suriya, Yoon,
Interactive Learning Environments
47
Berrett, & La Fleur, 2002, p. 379). When team members did not share responsibility,
problems arose. Some contributions to group assignments were late and unusable,
reflecting low quality planning discussions, consisting of little more than affective
messages. These students were very enthusiastic spectators, cheering from the
sidelines and afterwards congratulating the team on good work, even if they did not
expend much effort. Scott Johnson and his group (Johnson, S. D., et al., 2002)
suggested ‘‘Problems in the virtual teams came from a lack of willingness to
participate, lack of planning, conflicting schedules, or individual disagreements.
Most of these are social interaction issues’’ (p. 391). Not all our students were
adverse to online group work.
When the peers are encouraged to work together, they better realise their collective
potential.
Creating a rubric as a team was quite fascinating. I created my rubric and I felt good to
see my work joined with the work of others (E).
We worked so hard with my teammates . . . . I call this team the A team because of the
outstanding work we did (E).
Significantly, some of the accolades came from the very students that
others complained about and in their reflective essays accused of withholding
contributions. Many low performing students had poor metacognition of their
contribution.
Non-English speaking students can find it challenging to participate in fast-paced
synchronous chats (Carr et al., 2004). Some students participated erratically in
synchronous chats and some never mastered the tool, in spite of clear online
instructions. Some managed to log in but did not respond when other participants
repeatedly encouraged them to contribute. This adversely affected other students, as
they suspected those students might be spying.
The learning needs of some of the read-only participants were met, even if
they contributed minimally. Some thought that affective participation was sufficient. Diverse students understood their responsibility to the online community
differently.
Virtual community
Voluntary participation
After exploring many factors that influence successful course outcomes, we
investigated the role of the virtual community on learning and the effect of nonparticipation on the community. According to Collison et al. (2000), students in a
healthy online community support their community. Their concern became evident
when they contributed without expecting rewards. After concluding the course we
e-mailed a request for feedback to clarify some outstanding issues. We assumed that
voluntary responses would indicate prolonged involvement in their community. In
Figure 1e we display the results of the replies. As expected, the students who did not
successfully complete the course nearly unanimously ignored the request. The
difference between the average students and the distinction candidates was both
interesting and highly significant.
Figure 1 shows that the successful students were not only most active online, but
were also the most involved in the virtual community, contributing more posts,
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L. Nagel et al.
replying to a larger percentage of fellow student posts, displaying collaborative
behaviour, and readily providing voluntary feedback.
An integrated community
A core of students represented a high functioning, healthy online community (Collison
et al., 2000). The ethnography showed the concern and support that existed in this
community, with students informing their peers of imminent absence from discussions.
Reasons for absence were often work related, teachers attending conferences or
school tours, for example. Students were also willing to be vulnerable (Barab,
Thomas, & Merrill, 2001) and shared personal circumstances, like serious illness,
road accidents, and death among close associates. By extending support, close
affective bonds and a camaraderie developed. This resembled Barab et al.’s (2001,
p. 105) community, where ‘‘students readily shared their feelings, critically examined
course issues, extended their support in helping peers.’’ High quality (useful and
timely) contributions granted membership to the community. The community in
turn helped students to improve the quality of their contributions in a positive
feedback fashion. Our community was not inclusive. At its core students
participated often, while at the periphery individuals participated less.
Facilitator support
Some of the less connected students communicated with the facilitator by e-mail,
telephone, and short text messages. The distributed rural student with the
intermittent electricity supply reported these by short text message or telephone
and thus negotiated deadlines. A few communicated to the facilitator personal
circumstances that precluded class participation. We accommodated them by
allowing them to work separately. Their interaction with the facilitator possibly
contributed to their success (King, 2002).
Other low participation students used ordinary e-mail to communicate with the
facilitator or to submit assignments, thereby indicating that their lack of
communication and participation was not caused by poor connectivity but by
poor LMS attendance. E-mails consisted mostly of excuses for missing deadlines, but
we were often unable to respond due to overflowing mailboxes. The reasons for this
poor participation remains obscure, as they did not return telephone calls or e-mails,
nor did they reply to discussion postings. These poorly connected students seldom
made valuable contributions, as they frequently missed important instructions.
Online support went unheeded. They did not improve their work and did not
integrate into the online community of learners. Many of these invisible students had
poor completion rates and grades.
No amount of online coaching will improve the learning experience for
unconnected students. They illustrate Bernard et al.’s (2004) finding that frame of
mind and previous performance are the best indicators of online learning success.
Conclusions
We present evidence that in a predominantly participative class the number of times
students access the course, the number of contributions to discussions, the ratio of
replies to others’ posts, and integration into the learning community all
Interactive Learning Environments
49
significantly relate to successful course completion. These metrics, however, have
poor individual predictive value because the great diversity of students in the
cohort included numerous exceptions.
Low online visibility and participation can take many forms, with students
assuming different roles:
. read-only participants, merely skimming or deliberately harvesting much of
value from others’ discussions;
. highly visible without contributing much of value;
. poorly visible due to poor connectivity or high costs, although some manage
their log in time effectively and gain maximum benefit;
. absent for other reasons, but interacting with the facilitator and staying on
track;
. absent and non-reading, non-participating for undisclosed reasons, not sharing
the benefits of the virtual learning community.
Only students who contributed to the class or interacted with the facilitator
completed the course successfully. Our calculations confirm that students who are
at risk of not completing a course contribute less and their contributions are of
poorer quality, reflecting less interaction with fellow students and the facilitator.
Because of low frequency log ins these students miss out on crucial support needed
for success (Davies & Graff, 2005).
People also lurk in professional list servers, using content or ideas from a
discussion but contributing nothing in return (Klemm, 1998). While read-only
participants learn from others without visibly participating or adding value to the
discussions (Beaudoin, 2002), the dynamics in an online community of learners
depend strongly on diverse contributions from all its members. Other than Netgeneration students (Oblinger, 2003), mature students often resent dependence on
others, sentiments that may conflict with the necessity to post often and care for the
community (Collison et al., 2000). We found that high quality contributions rather
than quantity builds trust among mature students. In an online community students
spontaneously moderate the discussions and give cognitive feedback, allowing novice
members to grow into full participation. Non-participating students relinquish
coaching, feedback, and support from the facilitator and their peers, as the affective
dynamics in the community precludes non-participating members. We caution
against Beaudoin’s permissiveness towards lurkers. It is not in the interests of the
community if a large number of the class are read-only participants. This also deters
the isolated student.
To avoid read-only participation we endorse Klemm’s (1998) suggestions, and
further suggest a facilitator should:
. communicate the required log in frequency clearly;
. encourage the submission of high quality thoughtful postings and grade them
accordingly;
. grade all discussions initially and give formative feedback, in private if
necessary;
. grade individual contributions to group projects (peer or self-generated) and
do not give the same grade for all;
. rotate members of groups so that students are not stuck with non-participating
members or, when feasible, allow students to choose groups;
50
L. Nagel et al.
. foster collaboration dependent on content-related interactions;
. structure group assignments so that students work in parallel rather than serially,
such that inadequate contributions do not impair others in their peer group;
. convey important information via Internet-independent media, such as mobile
phone technology.
The problem of poorly performing online students and those that abandon their
course is complex. Students who did not contribute did not become part of the
community and did not benefit from facilitation, tutoring, or peer feedback. The other
students reacted to this behaviour. We foresee that a large number of lurking students
in an online class can prevent the formation of a virtual community of learners and
compromise everyone’s education.
Notes on contributors
Lynette Nagel recently completed her Ph.D. in Computer-Integrated Education. Her research
interests include the dynamics of student interaction in online classes. She is an instructional
designer at the University of Pretoria. Address: Department for Education Innovation,
University of Pretoria, Pretoria, South Africa.
Seugnet Blignaut is a Research Professor in Education at the North-West University. She
obtained a Ph.D. in Computer-Assisted Learning from the University of Pretoria, South
Africa. Her research interests include the role of the online instructor, gender issues in online
learning, and integrating computers in learning at school. Address: Education Sciences,
North-West University, Potchefstroom Campus, Private bag X6001, Potchefstroom, 2520
South Africa.
Johannes Cronjé is the Dean of the Faculty of Informatics and Design at the Cape Peninsula
University of Technology. He has been working in computers and education since 1994 and
has supervised more than 60 Masters and 27 Ph.D. students. His research interests include
communication patterns in online learning communities and the functioning of virtual
learning communities in multicultural contexts. Address: Cape Peninsula University of
Technology, PO Box 652, Cape Town, 8000 South Africa.
References
Barab, S.A., Thomas, M.K., & Merrill, H. (2001). Online learning: From information
dissemination to fostering collaboration. Journal of Interactive Learning Research, 12(1),
105–143.
Beaudoin, M.F. (2002). Learning or lurking? Tracking the ‘‘invisible’’ online student. Internet
and Higher Education, 5, 147–155.
Bernard, R.M., Brauer, A., Abrami, P.C., & Surkes, M. (2004). The development of a
questionnaire for predicting online learning achievement. Distance Education, 25(1), 31–47.
Boud, D., Cohen, R., & Sampson, J. (1999). Peer learning and assessment. Assessment &
Evaluation in Higher Education, 24(4), 413–426.
Carr, T., Cox, G., Eden, A., & Hanslo, M. (2004). From peripheral to full participation in a
blended trade bargaining simulation. British Journal of Educational Technology, 35(2), 15.
Clark, R.E., & Feldon, D.F. (2005). Five common but questionable principles of multimedia
learning. In R.E. Mayer (Ed.), Cambridge handbook of multimedia learning. Cambridge:
Cambridge University Press.
Collins, A., Brown, J.S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking
visible. American Educator, 15(3), 6–11.
Collison, G., Elbaum, B., Haavind, S., & Tinker, R. (2000). Facilitating online learning:
Effective strategies for moderators. Madison, WI: Atwood Publishing.
Interactive Learning Environments
51
Cox, G., Carr, T., & Hall, M. (2004). Evaluating the use of synchronous communication in
two blended courses. Journal of Computer Assisted Learning, 20, 183–193.
Davies, J., & Graff, M. (2005). Performance in e-learning: Online participation and student
grades. British Journal of Educational Technology, 36(4), 657–663.
Garrison, D.R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and
computer conferencing in distance education. The American Journal of Distance Education,
15(1), 7–23.
Johnson, C.S. (2001). A survey of current research on online communities of practice. Internet
and Higher Education, 4, 45–60.
Johnson, S.D., Suriya, C., Yoon, S.W., Berrett, J.V., & La Fleur, J. (2002). Team development
and group processes of virtual learning teams. Computers & Education, 39, 379–393.
Kettner-Polley, R.B. (2005). Virtual professor þ virtual student ¼ real education. Retrieved
January 18, 2005, from http://iiswinprd03.petersons.com/distancelearning/code/articles/
distancelearnprof10.asp
King, F.B. (2002). A virtual student. Not an ordinary Joe. Internet and Higher Education, 5,
157–166.
Kirschner, P.A., Sweller, J., & Clark, R.E. (2006). Why minimal guidance during instruction
does not work: An analysis of the failure of constructivist, discovery, problem-based,
experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.
Klemm, W.R. (1998). Eight ways to get students more engaged in online conferences.
Technological Horizons in Education Journal, 26(1), 62–64.
Kreijns, K., Kirschner, P.A., & Jochems, W. (2003). Identifying the pitfalls for social
interaction in computer-supported collaborative learning environments: A review of the
research. Computers in Human Behavior, 19, 335–353.
Miller, M.D., Rainer, R.K., & Corley, J.K. (2003). Predictors of engagement and participation
in an on-line course. Online Journal of Distance Learning Administration, 6(1), 13.
Oblinger, D. (2003). Boomers, gen-Xers & millennials. Understanding the new students.
Educause, 4, 37–47.
Prammanee, N. (2003). Understanding participation in online courses: A case study of
perceptions of online interaction. ITFORUM, 68, 16.
Rovai, A.P., & Barnum, K.T. (2003). On-line course effectiveness: An analysis of student
interactions and perceptions of learning. Journal of Distance Education, 18(1), 57–73.
Roycroft, T.R., & Anantho, S. (2003). Internet subscription in Africa: Policy for a dual digital
divide. Telecommunications Policy, 27, 61–74.
Schreck, V. (2006). It takes a virtual village: Practical strategies for improving online learning
retention rates. Retrieved January 6, 2007, from www.innovativeeducators.org/product_p/
38.htm
Sharp, L., & Fretchling, J. (1997). User-friendly handbook for mixed method evaluations.
Retrieved December 22, 2006, from www.ehr.nsf.gov/EHR/REC/pubs/NSF97-153/
START.htm
Shavelson, R.J., & Huang, L. (2003). Responding responsibly to the frenzy to assess learning
in higher education. Change, 35(1) (January/February), 10–19.
Sutton, L.A. (2001). The principle of vicarious interaction in computer-mediated
communications. International Journal of Educational Telecommunications, 7(3), 223–242.
Swan, K. (2003). Learning effectiveness online: What the research tells us. In J. Bourne & J.C.
Moore (Eds.), Elements of quality online education: Practice and direction (pp. 13–45).
Needham, MA: Sloan Center for Online Education.
Swan, K., Shea, P.J., Fredericksen, E.E., Pickett, A.M., & Pelz, W.E. (2000). Course design
factors influencing the success of online learning. Paper presented at the WebNet 2000
World Conference on the World Wide Web and Internet, San Antonio. Chesapeake, VA:
AACE.
Williams, B. (2004). Participation in on-line courses—how essential is it? Educational
Technology & Society, 7(2), 1–8.
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