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Critical Reading and Researching Main IdeasIn this Week’s Discussion, you reviewed reading strategies and examined your own research process. Now you will put these concepts and insights in practice for this Week’s Assignment.To prepare for this Assignment:Review the Learning Resource on Determining the Main Points of a Reading.Choose one of the preselected journal articles in this week’s Learning Resources.Applying the concepts from this week’s resources, critically read the journal article, taking notes or engaging in any research methods that you would like to try.The Assignment:Select three related main points from the journal article (excluding the Abstract), and directly quote them.In addition to the directly quoted main points, compose 1–2 sentences of rationale for each main point. Use these sentences to explain the reason you selected each point from the journal article. Consider the following questions in your rationale:Why are these three specific points the author’s main ideas?What makes them stand out to you as a critical reader?How are these main points related to one another?
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Computer-Supported Collaborative Learning (2012) 7:423–442
DOI 10.1007/s11412-012-9151-2
Online class size, note reading, note writing
and collaborative discourse
Mingzhu Qiu & Jim Hewitt & Clare Brett
Received: 25 March 2010 / Accepted: 14 June 2012 /
Published online: 22 July 2012
# International Society of the Learning Sciences, Inc.; Springer Science+Business Media, LLC 2012
Abstract Researchers have long recognized class size as affecting students’ performance in
face-to-face contexts. However, few studies have examined the effects of class size on exact
reading and writing loads in online graduate-level courses. This mixed-methods study
examined relationships among class size, note reading, note writing, and collaborative
discourse by analyzing tracking logs from 25 graduate-level online courses (25 instructors
and 341 students) and interviews with 10 instructors and 12 graduate students. The quantitative and qualitative data analyses were designed to complement each other. The findings
from this study point to class size as a major factor affecting note reading and writing loads
in online graduate-level courses. Class size was found positively correlated with total
number of notes students and instructors read and wrote, but negatively correlated with
the percentage of notes students read, their note size and note grade level score. In larger
classes, participants were more likely to experience information overload and students were
more selective in reading notes. The data also suggest that the overload effects of large
classes can be minimized by dividing students into small groups for discussion purposes.
Interviewees felt that the use of small groups in large classes benefited their collaborative
discussions. Findings suggested 13 to 15 as an optimal class size. The paper concludes with
a list of pedagogical recommendations and suggestions for new multimedia software
features to enhance collaborative learning in online classes.
Keywords Class size . Note reading . Note writing . Collaborative discourse . Mixed methods
study
M. Qiu (*) : J. Hewitt : C. Brett
Department of Curriculum, Teaching, and Learning, Ontario Institute for Studies in Education,
University of Toronto, Toronto, Canada
e-mail: [email protected]
J. Hewitt
e-mail: [email protected]
C. Brett
e-mail: [email protected]
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M. Qiu et al.
The study discussed here1 examined the relationship between class size and note reading
loads, note writing loads, and collaborative discussions in online graduate-level courses at a
Canadian institute using software WebKF. Specifically, it investigated three questions: “How
do different class sizes affect students’ and instructors’ participation in note reading and note
writing?” “What are students’ and instructors’ opinions about note reading and writing loads
related to class sizes?” “How do students and instructors make sense of online cooperation
and collaboration across different class sizes?” The findings from this study point to class
size as a major factor affecting note reading and writing loads in online graduate-level
courses. Although the specific findings of this study are not individually surprising to people
experienced with CSCL instruction, the discussion of their implication may contain a
perspective that could usefully be made available to the CSCL research and practitioner
community.
Class size has long been recognized as a factor affecting students’ achievement in face-toface instructional contexts, but has been little investigated in online courses. Some research
has shown that online class size certainly has important effects on information overload in
computer conferencing courses (Hewitt and Brett 2007; Lipponen and Lallimo 2004).
However, few studies have examined the effects of online class size on exact note reading
and writing loads and collaborative discourse, especially with mixed methods.
In face-to-face courses, students learn by attending class, listening to the instructors’
lectures and participating in discussions with classmates. They contribute by talking to share
ideas and opinions. In online courses, discussions are still primarily text-based. As a basic
precondition, online learners have to read the messages, ask questions, comment on messages, and answer questions (Hron and Friedrich 2003). Students read instructors’ and
classmates’ notes, and contribute by writing their own notes. Because note reading and
writing are fundamental online activities (Davie 1988), we can analyze these operations to
investigate how much students “listen” (read notes), and how much students contribute
(write notes) in their online discussions. More importantly, we can investigate how class size
correlated with students’ and instructors’ note reading and writing practices and their
perspectives. However, “online teaching should not be expected to generate larger revenues
by means of larger class sizes at the expense of effective instructional or faculty oversubscription” (Tomei 2006, p. 531). Online education will continue to shape the way some
people learn in the 21st century (Wuensch et al. 2008). While e-learning systems have
improved with time, they still have some problems that need to be resolved in order to
achieve a truly stimulating and realistic learning experience (Monahan et al. 2008).
Class size and challenges in online learning
There is a growing tendency for instructors who previously taught face-to-face classes to
teach online despite insufficient knowledge of online teaching. For example, Moore and
Kearsley (1996) found that some “distance education courses were developed and delivered
in a very piece-meal and unplanned fashion” (p. 6); a similar situation still exists. The
present study’s literature review found no set principles or detailed guidance for
instructors and students about how to cope with different situations and workloads
in different sizes of online classes. Educators need to build pedagogy or instructional
strategies to enhance the online educational experience for instructors and students
alike (Xu and Morris 2007).
1
The study is discussed in detail in Qiu 2009, on which this article is based.
Computer-Supported Collaborative Learning
425
Crucial to the success of online learning is active student participation and interaction with
both peers and instructors (Sutton 2001). A common approach to encourage student participation is some overt reward or punishment system (Masters and Oberprieler 2004). However,
such systems also create an authority structure which has a large impact on subsequent learning
and collaborative learning activities (Hubscher-Younger and Narayanan 2003), and may not be
effective in some online situations. For example, Bender (2003) found that one of the reported
feelings in Computer Mediated Communication is being overwhelmed brought on by a large
class size. Potentially, according to Hewitt and Brett (2007), the perception of information
overload could have a number of negative consequences, such as heightened student anxiety,
which can interfere with the amount of attention that participants dedicate to online learning.
This leaves shy students, especially those who lack confidence or withdraw upon rejection of
their initial ideas, with little chance to participate in discussions, a situation which may lead to
depersonalization and deindividuation (Bordia 1997). Hewitt et al. (2007) also found that CMC
students habitually engaged in practices like scanning, skimming, or reading new notes, and
those larger classes had higher “scanning” rates due to an increased information load.
To overcome such problems, Hron and Friedrich (2003) argue, appropriate class sizes
should be set in order to ensure for each class a minimum critical mass for participation
without overload, to reach the goals associated with collaborative learning, and to make it
easier to establish social presence and encourage greater interactivity (Aragon 2003). Studies
of class size for online courses should examine the optimum class size for quality education
and establish a discussion-board size that allows meaningful discourse (Frey and Wojnar
2004). Optimal class sizes “must be sufficiently large to encourage activity, but not so large
that the sense of group connectedness is lost” (Colwell and Jenks 2004, p. 7).
Online conferencing usually takes more time (Clouder et al. 2006), and a major challenge
in online learning settings is how to structure asynchronous online discussions in order to
engage students in meaningful discourse (Gilbert and Dabbagh 2005). Educational researchers need to find technologies which best contribute to making collaborative online learning
effective (Xu and Morris 2007). Hutchinson (2008) suggests that “the more effective
deployment of existing technologies may be part of the solution” (p. 357). The majority of
online education systems are still mainly text-based (Wuensch et al. 2008) with insufficient
features to allow effective, interactive discourse. Dohn (2009) studied some discrepancies
that lead to theoretical tensions and practical challenges when Web 2.0 practices are utilized
for educational purposes. In addition, advanced multimedia applications, such as graphs,
audio, and video are not much used, though some experts have suggested a movement “from
e-learning to m-learning” using streaming synchronous audio and video technologies (e.g.,
Keegan 2002).
Constructivism, knowledge building, cooperation, collaboration and class size
Social constructivism, knowledge building, cooperative learning, and collaborative learning
theories support the idea that students can learn from each other. They believe that explanation leads to deeper understanding and stress that the goal for students is to build
knowledge and negotiate meaning in a learning community. How people learn is strongly
influenced by social context, which in turn is the product of the interaction of individual
differences (Bransford et al. 1999). Knowledge building can be considered as deep constructivism that involves making a collective inquiry into a specific topic, and coming to a
deeper understanding through interactive questioning, dialogue, and continuing improvement of ideas. When learners are effectively motivated and actively try to achieve their
426
M. Qiu et al.
learning goals, deeper levels of thinking and learning are promoted (Scardamalia and
Bereiter 1994). This notion is consistent with Bruner’s (1986) observation that learning is
an active social process. Studies on teaching from a Vygotskian perspective (1978) emphasize creating more advanced social learning opportunities for students. Boettcher (1999)
states that knowledge has the best chance of flourishing in an environment that is rich,
supportive, encouraging, and enthusiastic.
Cohen (1994) stresses that cooperative learning can stimulate the development of higherorder thinking skills and that cooperative groups are particularly beneficial “in developing
harmonious interracial relations in desegregated classrooms.” (p. 17) Students receiving
individual feedback on cooperative group mates obviously increase their cooperation rate in
comparison to those receiving no feedback (Kimmerle and Cress 2008). However, cooperative groups differ from collaborative groups; the former tend to have a “divide and
conquer” mentality, where the group divides the work into chunks that can be done
independently (Graham and Misanchuk 2004). By contrast, collaboration involves the
mutual engagement of participants in a coordinated effort to solve the problem together
(Roschelle and Teasley 1995).
The commonsense starting point in Computer-Supported Collaborative Learning is that
learning is social in nature (Jones et al. 2006). Collaboration is especially important in online
learning (Pena 2004), where the learners tend to be isolated without the usual social support
systems found in on-campus or classroom-based instruction. Since the purpose of collaborative groups is to achieve consensus and shared classroom authority (Bruffee 1999),
individual accountability becomes central to ensuring that all the participants in the group
develop by learning collaboratively (Hutchinson 2008). In classrooms that adopt a collaborative approach, the basic challenge shifts from learning in the conventional sense to the
construction of collective knowledge (Scardamalia and Bereiter 2006; 2003). Hakkaranen
(2009) argued that “knowledge advancement is not just about putting students’ ideas into the
centre but depends on corresponding transformation of social practices of working with
knowledge.” (p. 213) With collaborative learning, the control of learning is turned over to
the students and the learning environment is student-centric. Learning takes place in a
meaningful, authentic context and is a social, collaborative activity, in which peers play an
important role in encouraging (Neo 2003). In order to establish and maintain an online
learning community, the learning environment needs to be effectively designed to provide
students with opportunities to practice collaboration, critical thinking, and teamwork skills
that are increasingly valuable in the information age (Kerka 1996). Though its benefits are
widely known, collaborative learning remains rarely practiced, particularly at the university
level (Roberts 2004).
Proper online instructional strategies could guide meaningful online discussion between
or among peers who co-construct knowledge; allowing learners to share and refine meaning
with peers in a social context (Tao and Gunstone 1999). Some writers (e.g., Weigel 2002)
have argued that combining traditional courses with online collaboration represents a
significant step forward in higher education. Laurillard (2008) argued that “New technologies invariably excite a creative explosion of new ideas for ways of doing teaching and
learning, although the technologies themselves are rarely designed with teaching and
learning in mind.” (p. 5) Online technology enables the transfer of content and feedback
(Neo 2003). Properly deployed, the technology can support and enhance learning, the
acquisition of knowledge, and the development of intellectual analysis and skills in the
information age (Collins and Halverson 2009), rather than serving merely as an added
medium for transmitting information. It can be very productive to marry appropriate
instructional strategies with online technology (Ingram and Hathorn 2004).
Computer-Supported Collaborative Learning
427
Researchers have proposed a number of different optimal sizes for online classes. Based
on their own online teaching experience, Aragon (2003) proposed 30 as an upper limit on
class size. This matches Bi’s (2000) suggestion that to optimize and allow for effective
feedback, fewer than 30 students should be enrolled in each class. Roberts and Hopewell
(2003) suggested that faculty keep the size of the class to 20 students, to allow for more
“workable” loads. This size is manageable without overwhelming the instructor or minimizing his effectiveness. Rovai (2002) argued that to guarantee effective online engagement
and interactions, 8–10 students were required. However, in general, students in smaller
classes tended to learn more (Glass and Smith 1979).
Method
Creswell (2005) states that “Mixed methods designs are procedures for collecting, analyzing,
and linking both quantitative and qualitative data in a single study or in a multiphase series
of studies” (p. 53). He points out that all research methods have limitations that in mixedmethods research the biases inherent in any single method could neutralize or cancel the
biases of other methods. Morse (2003) argues that the major strength of mixed methods
research is that it allows research to develop as “comprehensively and completely as
possible” (p. 189). In other words, the fundamental principle of mixed method research is
to collect multiple sets of data using different research methods in such a way that the
resulting mixture or combination has complementary strengths and non-overlapping weaknesses (Johnson and Christensen 2004). Results from one method can help develop or
inform the other method (Greene et al. 1989) and provide insight into different levels or
units of analysis (Tashakkori and Teddlie 2003). Mixed methods help researchers develop a
fuller understanding of the issues under investigation.
This study adopted a mixed methods design, using results from quantitative data analyses
and from qualitative interviews. Specifically, it used a mixed methods design in order to: (1)
develop stronger claims to test the hypothesis that different class sizes do affect note reading
and note writing; (2) examine the research questions from multiple perspectives, thus
providing greater diversity of positions and values; (3) understand online graduate-level
discussion loads more insightfully; and (4) develop more comprehensive, more complete,
and more enriched portraits of online graduate level discourse.
This study adopted purposeful criteria (Strauss and Corbin 1998) for selecting both quantitative and qualitative samples with maximum variation in the sampling of interview participants, taking into account the notion that participants must have experience (Morgan et al.
1998) of online group discussions in different sizes of classes. The samples for both quantitative
and qualitative data analyses were drawn from one Canadian institute, because of its diversity of
graduate online courses, its history of online education, its experienced faculty members and the
software (Web Knowledge Forum) used for threaded online discussions. Many studies suffer
from high attrition or otherwise wind up using statistical analyses with inadequate sample sizes
(Schoech 2000), which violate the underlying assumptions of the statistical methods. Here, the
sample for the quantitative analyses in this study was made larger than those for most
quantitative computer-mediated communication studies described in the literature (Schoech
2000). This study analyzed tracking logs from 25 graduate-level online courses (from fall 2003
to summer 2004) using software Web Knowledge Forum (25 instructors and 341 students) and
semi-structured interviews with 10 instructors and 12 graduate students who had diverse
backgrounds and extensive online teaching and learning experience. The actual class sizes in
this study range from 6 to 22 for the quantitative data and 6 to 25 for the interviews.
428
M. Qiu et al.
The quantitative and qualitative data analyses were designed to complement each
other. In the quantitative data analysis, a number of issues central to ensuring
maximum statistical power in the study were considered in order to minimize the
risk of Type II errors and to sufficiently protect against Type I errors with a
significance level of at least .05. We used two-tailed tests in the analysis, which
meant we required a larger sample in order to maximize the study’s power. The
sample size—341 students and 25 instructors in 25 courses—was large enough to
produce effective statistical power. First we conducted data cleaning and checking to
ensure the quality of the dataset. The descriptive statistical analyses compared means,
standard deviations, maximum, and minimum values of variables from the 25 course
datasets concerning note reading and note writing. We employed a Pearson Correlation, one-way ANOVA, t-test, ANCOVA, and multiple regression analyses.
The qualitative data analysis followed the principles and practices that Tesch (1990)
identified for grounded theory. As Denzin and Lincoln (2005) pointed out, “Grounded
theory is probably the most widely employed interpretive strategy in the social sciences
today” (p. 204). Following Tesch’s principles, the inductive analysis of the qualitative data
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