ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
JARI LARU*Faculty of Education, University of Oulu, Snellmania,Oulu, P.O. Box 2000, 90014 University of Oulu, [email protected]+358-40-5118478http://www.claimid.com/jarilaru
PIIA NÄYKKI, SANNA JÄRVELÄFaculty of Education, University of Oulu, Snellmania,Oulu, P.O. Box 2000, 90014 University of Oulu, [email protected], [email protected]
Abstract: In this single-case study, small groups of learners were supported by use of multiple social software tools and face-to-face activities in the context of higher education. The aim of the study was to explore how designed learning activities contribute to students’ learning outcomes by studying probabilistic dependencies between the variables. Explorative Bayesian classification analysis revealed that the best predictors of good learning outcomes were wiki-related activities. According to the Bayesian dependency model, students who were active in conceptualizing issues by taking photos were also active blog reflectors and collaborative knowledge builders in their group. In general, the results indicated that interaction between individual and collective actions likely increased individual knowledge acquisition during the course.
Keywords: Case study, Cloud-based social software, Explorative analysis, Higher education, Small-group collaboration
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
1. Introduction
Technology is one of the most significant mechanisms currently transforming the
learning process. Over the course of history, a range of artefacts has been produced (e.g.,
invention of the chart) that has modified the way in which people learn in various situated
practices (Pea, 1993). In particular, representational tools such as calculators and mind maps
have dramatically changed our daily practices in many spheres of life (Säljö, 2003). New
technologies provide opportunities for creating learning environments that extend the
possibilities of old technologies (e.g., books, blackboards, television, radio) and offer new
prospects for multiple social interactions (Bransford, Brown, & Cocking, 1999).
In recent years, a plethora of digital and networking tools has been established on the
Internet. These digital applications—which enable interaction, collaboration and sharing
among users—are frequently referred to as Web 2.0 (Birdsall, 2007) or social software tools
(Kesim & Agaoglu, 2007). These applications are further assumed to be a step change in the
evolution of Internet technology in higher education (Wheeler, 2009), which has evolved from
being primarily used to distribute course materials, communicate and evaluate to being used
to enhance educational processes that support collaborative learning and knowledge building
(Collins & Halverson, 2010; Cress & Kimmerle, 2008; Schroeder, Minocha, & Schneider, 2010).
Much has been written on the benefits of blogs (Halic, Lee, Paulus, & Spence, 2010; Hemmi,
Bayne, & Land, 2009; Wheeler, 2009; Xie, Ke, & Sharma, 2008),wikis (Cress & Kimmerle,
2008; Hemmi et al., 2009; Wheeler, 2009) and social networking sites (Arnold & Paulus,
2010) in education. However, very little formal research focusing on the integration of
multiple social software tools in higher education pedagogy has been published as of yet
(Uzunboylu, Bicen, & Cavus, 2011; Wheeler, 2009).
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Crook (2008) and Meyer (2010) have argued a need for more empirical research on
the educational use of Web 2.0, its adoption and its impact on higher education. In this single-
case study, small groups of learners were supported by multiple social software tools and
face-to-face activities in the context of higher education. The purpose of this study was to
explore how designed learning activities contribute to students’ learning outcomes by
studying the probabilistic dependencies between the variables.
2. Theoretical background
2.1. Social software to support individual reflection
One activity that can promote the use of blogs in education is self-reflective practise
(Sharma & Fiedler, 2007; Xie et al., 2008). Self-reflecting is a central concept in metacognitive
learning in which students are encouraged to construct explanations, pose questions and
provide further information to each other (Cohen & Scardamalia, 1998). While constructing
explanations, the students become aware of their thought processes, gaps in knowledge and
lack of understanding (Webb, 1989). Through contributing their ideas and making their
thought processes visible, the students are able to reflect on their cognitive processes and
discuss with others what they do or do not know and understand.
Previous research (Xie et al., 2008) has shown that reflection is effortful action that
requires external support in order to engage students for extended periods of time. For
example, Xie et al. (2008) have introduced various strategies for encouraging reflection, and
they have concluded that blog-writing activities, journaling and peer feedback are all
appropriate reflection strategies.
Weblogs are popular journaling tools that offer students a means of externalising their
reasoning and reflecting on their experiences (Xie et al., 2008). Hence, Weblogs can be used as
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
‘learning logs’ that capture the cumulative history of a learning project in action and record
personally meaningful material that can foster and facilitate reflective practices such as
conversations with oneself and others (Halic et al., 2010; Hemmi et al., 2009; Sharma &
Fiedler, 2007; Xie et al., 2008). The main idea of blogging is similar to that of network
discussions: The students make their thinking visible and externalize their thinking by
periodically posting journal entries to their personal or collaborative blogs, allowing other
learners to comment on their learning blogs (Xie et al., 2008).
Second, in addition to self-reflective blog writing, peer feedback can provide a different
perspective and help students to assimilate and accommodate their thinking. Blogs can
facilitate reflective thinking, because people can easily access different points of view by
looking at peers’ blogs or comments (Xie et al., 2008). Furthermore, Really Simple Syndication
(RSS) offers novel ways to increase access to different points of view by enabling various
contributions to be aggregated, even though they may have originated from diverse sources
(e.g., blogs, file-sharing tools, and wikis) (Crook, 2008; Lee, Miller, & Newnham, 2008).
2.2. Social software to support collaborative learning
The potential of collaborative learning groups has been strongly supported by the
literature, which emphasizes students’ possibilities for constructing knowledge and
experiencing shared understanding through these groups (Dillenbourg, Baker, Blaye, &
Malley, 1996; Dillenbourg, 1999).
Social software applications (e.g., wikis) provide new opportunities for collaborative
learning and knowledge building (Cress & Kimmerle, 2008; Dohn, 2009). Moreover, they
present significant challenges to the views of knowledge (Cress & Kimmerle, 2008; Dohn,
2009), learning (Crook, 2008; Ravenscroft, 2009) and goals of the procedures implicit in Web
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
2.0 practises on the one hand (Collins & Halverson, 2010; Crook, 2008; Dohn, 2009) and the
educational system on the other (Collins & Halverson, 2010; Dohn, 2009).
Dohn (2009) has stressed that Web 2.0 and/or educational practises must be reshaped
to fit each other, given that they originate in different contexts. From the perspective of
collaboration within Web 2.0 tools, who contributes is less important than the fact that
contributions are made and that they stand a chance of being revised by adding, deleting or
changing their components until the outcome corresponds to group direction and consensus
(Dohn, 2009).
Alternatively, Cress and Kimmerle (2008) see an imminent connection between
collaborative knowledge building in wikis and learning; from their perspective, one person’s
individual knowledge can serve as a resource for the learning of others. In their seminal paper
on knowledge building with wikis, they describe how people make use of each other’s
knowledge through collaborative knowledge building with artefacts. When interacting with a
wiki, individuals can learn as a result of either externalization or internalization. This learning
can take place by assimilation (extending knowledge by simply adding new information) or by
accommodation (modifying and creating new knowledge).
In this study, the pedagogical ideas behind the design are grounded in collaborative
learning, and special effort has been placed on enhancing and supporting collaborative
learning as a cognitive and social activity (Teasley, 1997). The students’ learning tasks,
including social and individual activities, were supported by designing learning assignments
that consisted of recurrent individual and collective phases in which students used Web 2.0
tools in concert to perform the designed tasks.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
In sum, all these activities to be undertaken with social software tools were also
aligned in such way that Web 2.0 characteristics (Dohn, 2009) were taken into account. For
example, Web-mediated resources were largely utilised; all created content was open, and
wiki pages had distributed authorship; different materials were reproduced and transformed
from multiple individual or collaborative learning spaces; and open-endedness and lack of
finality were actively promoted to all participating students.
3. Aims of the study
In this single-case study, small groups of learners were supported using multiple social
software tools and face-to-face activities in the context of higher education. The aim of the
study was to explore how designed learning activities contribute to students’ learning
outcomes by studying the probabilistic dependencies between the variables. The research
questions are as follows: 1) How much did students learn during the course? 2) Which social
software and face-to-face variables were the best predictors for identifying differences
between high- and low-performing groups of students? 3) What was the impact of social
software and face-to-face sessions on individual students’ learning gain?
4. Methods
This study followed the principles of the case study method. A case study is defined as
an empirical study that investigates a contemporary phenomenon within its real-life context,
especially when the boundaries between the phenomenon and the context are not evident
(Yin, 2003).
In practise, the research design of the current study employed a single-case study with
embedded multiple units of analysis. As multiple social software tools and face-to-face
activities were used to support learning in a higher education course, the behaviour of
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
students within phases of the learning design and students’ learning outcomes were
considered as the embedded units.
These units were analysed using quantitative techniques as the primary approach. In
order to return to larger units of analysis, Bayesian methods (Jensen, 2001) were used to
classify and model the complex dependencies between the different variables.
4.1. Participants and the research setting
The research participants were 21 undergraduate students in a five-year teacher
education programme in the Faculty of Education at the University of Finland. All of the
students were enrolled in a required course titled Future Scenarios and Technologies in
Learning during the spring semester of 2009. The 21 participants included 16 females (76%)
and 5 males (24%). The prevalence of females reflects the gender ratio of education majors at
the university.
4.1.1. The task
The participants worked in groups of four to five students for 12 weeks. Groups were
required to complete a wiki project by the end of the semester. In order to complete the wiki
project, students needed to participate in recurrent solo and collective phases mediated by
the use of social software tools and face-to-face meetings in their respective sessions (see
Figure 1).
On the first day of the course, in a campus computer lab, the instructor gave all
participating students pre-configured accounts to social software services and mobile devices
needed for photo-taking activities (see Section 4.1.2).
After ensuring that the students in their respective groups understood the instructions
provided, no further support was provided during the tasks. In other words, the assignments
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
required the students not only to learn and apply content knowledge, but also to generate
their own learning objectives and to determine what information to include in their final
contribution in their group wiki to be presented to the class.
--- Insert Figure 1 about here ---
The pedagogical design of this course was as follows:
A. Ground [Lecture] (weeks 1-3 and 6-8): Each of six one-week working periods started with
a lecture in which students were grounded in main theoretical concepts. The specific
themes were in the following order: 1. Learning infrastructure, 2. Learning communities,
3. Metacognition, 4. Self-regulated learning, 5. Learning design, and 6. Social Web as a
learning environment.
B. Reflect [Discussion] (weeks 1-3 and 6-8): The purpose of this collaborative phase was to
reflect on the lecture topic in groups and to formulate a problem to be solved based on the
group members’ shared interests during the following solo learning phases. Groups were
advised to set their own learning objectives based on the topic and to write down these
objectives in their personal blogs for further reflection.
C. Conceptualize [Photo-taking] (weeks 1-3 and 6-8): In this solo phase, individual students
were required to conceptualize their group members’ shared interests. In order to do so,
they were required to identify and capture situated pictorial metaphors describing their
shared interests. In practise, their tasks were to explore their everyday working and living
environments and take photos with a camera phone.
D. Reflect and elaborate [Blogging] (weeks 1-3 and 6-8): The task of this phase was to further
reflect and elaborate on photos in the students’ personal blogs. First, they were required
to analyse collected visual representations in order to discard ideas that were not
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
relevant to their groups’ shared learning objectives. Second, they were required to write
blog entries about chosen photos in which they further elaborated associations between
photos, group-level objectives and students’ everyday situated practises.
E. Review and evaluate [Discussion] (weeks 4 and 9): The first task of this collaborative face-
to-face activity was to review group members’ Weblogs from the previous three-week
period. The second activity was to evaluate the usefulness of blog entries in the context of
their shared learning objectives and to discard irrelevant ideas. The outcome of this phase
was used as material for co-construction of knowledge in the groups’ wikis.
F. Co-construct knowledge [Wiki work] (weeks 4-12): The task in this collaborative
assignment was focused on integrating each group’s chosen blog entries and visual
representations into a cohesive and comprehensive product of all course topics. In other
words, the given goal was to formulate what they had learnt ‘in their own words’ and
produce it as uniform material that could be put to authentic use.
G. Monitor peer students’ contributions [Monitor] (whole course): This was not an assignment
per se, but it enabled students to obtain different perspectives by seeing what others were
doing with social software tools, and it helped students to assimilate and accommodate
their thinking. In practise, monitoring activities were done by using cloud-based
syndication tools (RSS).
4.1.2. Tools
The idea of making use of each other’s knowledge was operationalized in a socio-
technical design. It consisted of recurrent individual and collective phases in which students
used multiple Web 2.0 tools and mobile phones in concert to perform designed tasks (Figure
2).
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
First, all students received a personal mobile multimedia computer, which was
integrated with features including a 3.2 megapixel digital camera, 3G connectivity and an
Internet browser. The mobile device was the main tool for the students in Phase C, who were
required to identify and capture situated pictorial metaphors describing their group’s shared
interests.
The device was equipped with a ShoZu cloud-based file-sharing tool, which was used as
a bridge to connect mobile phones to the Flickr cloud-based file-sharing service for photos.
ShoZu offered functions to add tags, titles and descriptions before putting photos on the Flickr
photostream. In addition, the phone’s Web browser was configured to show students’
accounts on the Google Reader Mobile cloud-based RSS aggregator. This service was used to
show all of the course-related content on the mobile phones at the students’ disposal (Figure
2).
Second, an individual Wordpress.com account was created for each student. This
blogging service was used as a personal learning diary for the students in which they
individually reflected further on their ideas by writing journal entries regarding the
respective pictures/videos sent to blogs via the Flickr file-sharing service (Phase C). The
students’ blogs were used as a storage facility for their group’s shared working problems
(Phase B) and as an anchor resource in the review and evaluate phase (Phase E). In addition,
the blogging service was the platform for course-level activities, a place for course-related
announcements.
The cloud-based Wikispaces wiki service was also used for two purposes: First, it
offered collaboration tools for the groups to use (i.e., empty wiki page and discussion tool) in
order to support their collaborative knowledge co-construction (Phase F). Second, it was used
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
at the course level for distributing resources (i.e., course curricula, lecture slides, hyperlinks
and how-to guides) and displaying syndicated content from Flickr (student accounts) and
WordPress (course blog, student blogs).
In addition, the FeedBlendr and FeedBurner RSS services were used to merge
individual, group and class-level feeds from multiple Flickr, WordPress and Wikispaces
accounts. In practise, these merged feeds were included as RSS widgets in a sidebar of the
respective blog or wiki. These tools enabled the students to combine social software tools, and
they may be seen as additional collaborative tools that facilitated relationships between
different task phases, the students, the content they produced and the tools used in this study
(See Lee et al., 2008).
--- Insert Figure 2 about here ---
4.2. Data collection
The data was composed of video recordings, social software usage activity and pre-
and post-tests of students’ conceptual understanding. Respective data variables are stored in
parentheses embedded into the descriptions below (see also Appendix I).
4.2.1. Conceptual knowledge test
To assess their conceptual understanding, the students completed identical paper-and-
pencil pre- and post-tests with a pre-test/post-test quasi-experimental design. Specifically,
the conceptual-knowledge measure consisted of six constructed-response questions that were
developed based on the key concepts of the course. Students were asked to write definitions
of the lecture themes, meaning that each theme was also connected to the learning design
described in Section 4.1.1. and was thus used for measuring the students’ learning outcomes
(gain) in a particular week of the course.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
4.2.2 Video data
Video recordings captured each group’s six collaborative reflection sessions
(B.discussion) and two collaborative reviewing and evaluation sessions (E.discussion) (42
hours of video data). The duration of those sessions was determined by each group, and the
average duration of one session was 44 minutes (where the duration ranged from 13 minutes
to 86 minutes).
4.2.3 Social software activity data
Social software usage activity data was collected at the student level through multiple
sources. First, the total number of Flickr photos per weekly topic and the average number of
photos for all topics (C.photo) were calculated.
Second, the total number of words in each blog entry and the number of blog entries
were measured for each weekly topic. Then, the average values of these were calculated for all
topics (D.blog.posts; D.blog.words/post) to be used in the Bayesian multivariate analysis.
Third, activity measures of the students’ wiki usage were calculated by using adds and
deletes as coding categories for cumulative history data. A measure of student cumulative
involvement in the wiki was given by the sum Activity(u) = add(u) + delete(u), called the edit
activity of author u, providing the total number of words (F.wiki.wc.activity) or edits
(F.wiki.edits.activity) that u touched by adding or deleting them. This value was used to
calculate students’ active use of their respective group wikis and their interactions in the wiki
discussion forum and embedded comments in the wiki (F.wiki.edits.comments;
F.wiki.wc.comments). A further characterization of how an author u contributed to the group
wiki was given by the difference Net added (u) = add(u) – delete(u), called the net number of
words added or edits performed, providing the total number of words or edits by which u
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
increased the length of the text (F.wiki.wc.net) or the number of edits (F.wiki.edits.net) when
words or edits that u deleted were deducted. This value was used to calculate the amount of
new content students contributed to the wiki.
Finally, the total number of read RSS items was measured by using statistics collected
automatically by Google Reader (G.rss.monitor).
4.3 Data analysis
Data was analysed using a quantitative paired samples t-test for the conceptual
knowledge tests, qualitative on-task analysis for video recordings and multivariate Bayesian
methods for the dependencies between social software usage, face-to-face activities and
learning gain.
4.3.1. Quantitative analysis of conceptual knowledge tests
In the first stage of analysis, a conceptual knowledge test was analysed in order to
answer the first research question: How much did students learn during the course?
Three independent researchers (including the first and second authors of this paper)
developed the criteria and marked the learning tests (points 0-3). The criteria were as
follows: 0 points represented low understanding (the student had no understanding of the
concept). One point represented some level of understanding (the student had some
understanding (i.e., knew what the concept was connected to) but no detailed knowledge of
it). Two points represented a basic level of understanding (the student understood what the
concept was connected to and knew some details about the concept). Finally, 3 points
represented the highest level of understanding (the student had a deep understanding of the
concept and knew very specific details about the concept).
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
The tests were analysed by marking points from 0 to 3 for individual answers. This was
done by three researchers who first independently marked the tests and then compared the
results and negotiated possible differences. According to the test results, all of the students’
understanding of the main concepts increased during the course. However, there were
differences between their levels of understanding of the different concepts.
To analyse the learning outcomes through the pre-test/post-test scores, a paired
samples t-test was conducted, and a normalized learning gain was calculated (Hake, 1998).
Next, the average normalised gain score was used to identify high-performing and low-
performing students for further explorative Bayesian analysis. Note that contrasting the
activity and artefacts of high performers to those of low performers is intuitively appealing
(Jonassen, Tessmer, & Hannum, 1999) and has been shown to reveal important
characteristics and aspects that are not uncovered using other approaches (Wyman & Randel,
1998).
4.3.2. Qualitative analysis of videotaped face-to-face sessions
In the second stage, video data transcripts were analysed in order to clarify individual
students’ activity levels in collaborative face-to-face assignments. Results of this analysis were
used as an activity measure of face-to-face activities for descriptive analysis of learning
phases and explorative Bayesian analysis (research questions 2 and 3).
This analysis was adapted from the method that focuses on the duration of on-task and
off-task episodes (for further details of the method, see Järvelä, Veermans, & Leinonen, 2008).
In this analysis, the focus was placed on the number of task-related utterances, which were
used as a measure of on-task activities, while off-task activities, such as discussions about
their evening plans, were coded in an independent off-task category.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
4.3.3. Descriptive analysis of social software and face-to-face activity variables
In the third phase, a descriptive analysis was carried out for all the variables in the
course design. First, the average values of an individual student’s face-to-face and social
software activities were calculated for Bayesian analysis (research questions 2 and 3). Second,
the mean, standard deviation and max-min values for all students (both high- and low-
performing students) were calculated in order to assist in the interpretation of the results of
Bayesian classification modelling and to provide an overview of the students’ activities during
the course (See Appendix).
4.3.4. Bayesian multivariate analysis of the impact of social software and face-to-face sessions
on learning outcome
In the fourth phase, Bayesian analysis (Jensen, 2001) was conducted to study the
probabilistic dependencies between the variables (research questions 2 and 3) described in
Section 4.2. In practise, the analysis was conducted with the Web-based online data analysis
tool B-Course1, which allowed users to analyse their data using two different techniques:
Bayesian dependency and classification modelling.
In general, Bayesian methods have many benefits for explorative analysis, as
summarized in Congdon (2003). For this study, the most relevant benefits were as follows: 1)
The theoretical minimum for the sample is zero, 2) Different kinds of multivariate variables
and distributions are accepted, and 3) It gives statistically robust tools to visualize and
categorize complex dependencies between variables. In short, Bayesian methods enabled us
to conduct statistical analyses of learning phases in our learning design.
1 http://b-course.cs.helsinki.fi/obc/
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
The first stage of Bayesian analysis involved conducting classification modelling
(Silander & Tirri, 1999) in order to answer the second research question: Which social
software and face-to-face variables were the best predictors for determining differences
between high- and low-performing groups of students? In the classification process, the
automatic search looked for the best set of variables to predict the class variable for each data
item. This procedure is akin to the stepwise selection procedure in traditional linear
discriminant analysis (Huberty, 1994).
The second stage of Bayesian analysis involved building a Bayesian network (Jensen,
2001) in order to answer the third research question: What was the impact of social software
and face-to-face sessions on individual students’ normalized learning gain? Such a Bayesian
network was the visualised result of Bayesian dependency modelling, in which the most
probable statistical dependency structure between variables was calculated.
A graphical visualization of a Bayesian network given by the B-Course program
(Myllymäki, Silander, Tirri, & Uronen, 2002) contains three components (See Figure 3 and
Table 3): 1) collected data as ellipses, 2) dependencies visualised as lines between nodes and
3) strength of each dependency as a ratio value in the table (see Table 3) and as a colour in the
network. The darker the line, the stronger the statistical dependency between the two
variables and the more important (higher ratio value) the dependency. A variable is
considered independent of all other variables if there is no line attached to it.
5. Results
First, results of the paired samples t-test will be presented to show how much students
learned during the course. Second, the best predictors for pointing out differences between
high- and low-performing groups will be explored using Bayesian classification analysis.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Third, the results of Bayesian dependency modelling showing probability dependencies
between the social software, face-to-face sessions and individual students’ normalized
learning gain shall be presented.
5.1 How much did students learn during the course?
A paired samples t-test was conducted to compare pre-test and post-test means.
Results showed that students gained higher scores in the post-test (M=7.95) than in the pre-
test (M=3.95), t(21)=8.33, p<.000. The effect size (Cohen’s d) was 1.69.
--- Insert Table 1 about here ---
Table 1 presents the mean values for pre-test and post-test raw scores and pre-post
normalized gain scores. Using the average normalized gain score (M=0.29; SD=0.16), high-
performing and low-performing students were identified for explorative Bayesian
classification analysis.
5.2. Which social software and face-to-face variables were the best predictors for determining
differences between high- and low-performing groups of students?
The second analysis explored which variables measuring social software usage and
face-to-face activities were the best predictors for pointing out differences between high- and
low-performing students. The model for classifying data contained items according to the
class variable level of the normalized learning gain (low performers and high performers)
with 12 variables of learning activities (descriptive values are shown in Appendix I, and items
are described in Section 4.2). The estimated classification accuracy for the model was 81.82%.
Table 2 lists the variables ordered by their estimated classification in the model. The
strongest variables—that is, those that best discriminate the independent variables—are
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
listed first. The percentage values attached to each variable indicate the predicted decrease in
the classification performance if the variable were to be dropped from the model. The table
shows that all variables in the model are equally important; that is, if we were to remove any
of the variables from the model, it would weaken the performance by 90.91%.
--- Insert Table 2 about here ---
Results from the classification analysis showed that the best predictors of higher
learning gains were wiki-related activities.
First, the mean number of wiki edits (F.wiki.edits.activity; M=68.64; SD=77.90) was
two times higher among high performers than low performers (M=34.55; SD=21.16). Second,
the high performers were 1.5 times more involved in the wiki editing activities (M=3427.73;
SD=3810.10) than the low performers (M=2151.10; SD=2074.12) when the number of words
(F.wiki.wc.activity) that they touched by adding or deleting was taken into account. Third,
high-performing students increased the length of the text (F.wiki.wc.net) in their groups’
wikis about 1.4 times more often on average (M=1173.91; SD=444.70) than low-performing
students (M=856.45; SD=507.49).
In short, the descriptive analysis above shows that high performers were more active
in organizing wiki content in a new way and in adding new information. The latter of these
contribution categories is an example of assimilation, a process in which information coming
from the wiki is perceived and modified in a way that makes it fit into the individual’s
knowledge. The former category is an example of an activity in which students do not simply
assimilate new information into existing knowledge but actually change knowledge in order
to better understand the wiki and its information (Cress & Kimmerle, 2008).
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
5.3. What was the impact of social software and face-to-face sessions on individual students’
normalized learning gain?
The next stage of the analysis involved building a Bayesian network out of the 12 items
measuring students’ learning activities during the course (descriptive values are shown in
Appendix I, and items are described in Section 4.2). The rationale for this procedure was to
examine dependencies between variables by both their visual representation and the
probability ratio of each dependency in order to answer the third research question.
A Bayesian search algorithm evaluated the dataset in order to find the model with the
highest probability. During the extensive search, 174,987 models were evaluated. Figure 3
shows a visualization of the network, which contains two components: 1) collected data as
ellipses and 2) dependencies visualised as lines between nodes. As mentioned, the darker the
line, the stronger the statistical dependency between the two variables and the more
important the dependency. Table 3 shows the strength of each dependency as ratio values in
the probability table.
In practise, if one removes the arc from the model with the high probability ratio, it
decreases the probability of the model by the same amount. However, in many dependencies
in the model, removing the arc between nodes would not change the probability of the final
model (listed at the bottom of the probability table).
--- Insert Figure 3 about here ---
--- Insert Table 3 about here ---
The Bayesian dependency model shows 7 strong (probability ratio >1,000,000) and 25
weaker relationships between variables. However, based on the analysis, only one strong
dependency between activities and learning gain was found: the connection between
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
assimilative wiki editing activities (F.wiki.wc.activity) and learning gain (gain), which
triangulates with the results in the Bayesian classification model presented above.
Furthermore, there was one weak dependency, the one between monitoring other students’
work via syndication services (G.rss.monitor) and learning gain (gain). Additionally, there
were two other connections between other variables (B.discussion, C.photo) and normalized
learning gain (gain) included in the visual network model, but their probabilities were so low
that they were dropped from the dependency table automatically. It is worth noting that the
wiki activities described above were strongly related to commenting on wiki content.
When the Bayesian model is further explored, it reveals that the average number of
blog posts (D.blog.posts) is the central variable in the model, as it has strong statistical
relationships to both assimilative (F.wiki.wc.net; F.wiki.edits.net) and accommodative wiki
activities (F.wiki.wc.activity; F.wiki.edits.activity). In practice, it can be said that students who
were actively reflecting and elaborating were also active in inserting and modifying
information in the wikis. This variable (D.blog.posts) also has a central role in the chain of
strong relationships, including all virtual activities in the study design (see Figure 1.): C.
Conceptualize, (C.photos), D. Reflect and elaborate (D.blog.posts), F. Co-construct knowledge
(F.wiki.wc.activity), and learning gain (Gain). This result demonstrates the successful use of
Web 2.0 characteristics in this study, an example of a series of activities in which intermediate
learning products were reproduced and transformed. Furthermore, it shows how higher
education course students can make use of each other’s knowledge through collaborative
knowledge building (Cress & Kimmerle, 2008).
There were also several weaker dependencies in the Bayesian model. First, results
showed that active following of RSS feeds was slightly related to an increased number of
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
situated visual representations (C.photos), an increased number of wiki editing activities
(F.wiki.*) and learning gain (gain). However, no connection was found between usage of RSS
feeds and blogging. Second, both collaborative face-to-face phases (B.discuss, D.discuss) were
slightly related to social software usage (D.blog.*; F.wiki.*) except the phase in which students
had to take photos.
6. Discussion
In our case, we found that using social software tools together to perform multiple
tasks likely increased individual knowledge acquisition during the course. Multivariate
Bayesian classification analysis revealed that the best predictors of good learning outcomes
were wiki-related activities. In addition, according to the Bayesian dependency model,
students who monitored their peers’ work via syndication services and who were active by
adding, modifying or deleting text in their group’s wiki obtained higher scores. The model also
shows that many other learning activities were indirectly related to learning outcome.
First, learning scores from pre-test to post-test were statistically significant with high
learning effect, indicating a substantial gain in conceptual knowledge test scores from pre-test
to post-test. This finding provides support for the learning design used in this study and for
the use of multiple cloud-based social software tools in a higher education context, and it was
further used to contrast high performers and low performers in the following explorative
Bayesian analysis.
Second, results from the Bayesian classification analysis revealed differences between
high performers and low performers and showed that the best predictors of higher learning
gain were wiki-related activities. Descriptive analysis of chosen predictor variables showed
that high performers were more active in organizing wiki content in a new way (mean
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
number of wiki edits was two times higher and mean word length of edited content was two
times higher when compared to low performers) and in adding new information (mean length
of inserted words was 1.4 times higher than that of low-performers). The latter of these
contribution categories is an example of assimilation, a process in which information coming
from the wiki is perceived and modified in a way that makes it fit into an individual’s
knowledge. The former category is an example of an activity in which students do not simply
assimilate new information into existing knowledge but actually change knowledge in order
to better understand the wiki and its information (Cress & Kimmerle, 2008).
After 174,987 models were calculated, the final Bayesian dependency model included
7 strong relationships and 25 weaker relationships between variables. Interestingly, the only
strong dependency between activities and learning outcome was found between assimilative
wiki editing activities and learning gain, which triangulates with results in Bayesian
classification modelling. Furthermore, there was one weak dependency, between monitoring
other students’ work via syndication services and learning outcome. There were two other
connections between other variables and learning gain included in the network model, but
their probabilities were so low that removing them would not change the probability of the
final model, and therefore, those were dropped automatically from the final model during the
analysis. It is also worth noting that the wiki activities described above were strongly related
to commenting on wiki content.
When the Bayesian model is further explored, it reveals that the average number of
blog posts per student is the central variable in the model, as it has strong statistical
relationships to both assimilative and accommodative wiki activities. In practise, it can be said
that students who were actively reflecting and elaborating on visual representations in their
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
own blogs were also active in inserting and modifying knowledge in the wikis. This can be
considered an example of learning that is both reflective and collaborative at the juxtaposition
of community and personal spaces (Wheeler, 2009).
This blog post variable also has a central role in the chain of strong relationships,
including almost all social software-related tasks in this study: average number of photos
taken and shared by each student, average number of blog posts, total sum of wiki activity,
and learning gain. This chain of activities demonstrates the successful use of Web 2.0
characteristics in this study, an example of a series of activities in which intermediate learning
products were reproduced and transformed by performing structured collaborative
assignments using Web 2.0 tools. It also shows how higher education course students can
make use of each other’s knowledge through collaborative knowledge building (Cress &
Kimmerle, 2008).
The remaining variables were weaker than those presented above. First, the results
showed that monitoring who does what (implicit peer feedback for individual reflection)
using syndication tools (RSS) was slightly related to an increased number of situated visual
representations (photos), number of wiki editing activities and learning gain. However, the
model did not show connections between blog and syndication variables. Therefore, it can be
argued that different perspectives on the form of syndicated content did not contribute to
reflective blog-writing activities. Instead, the results showed that active monitoring of the
activities of others using different social software tools increased students’ number of wiki
activities. Generally, these results further reinforced the findings of Jermann and Dillenbourg
(2008), who determined that the tools can provide information to foster group members’
reflections of their contributions: ‘what to do’ and ‘who does what’. Second, the results
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
revealed that the explicit peer feedback that students received by participating in
collaborative face-to-face sessions (sense-making session and meaning-making session)
slightly increased social software usage activities.
7. Conclusion
It can be concluded that the carefully crafted pedagogical activities and Web 2.0 tools
used together to perform designed tasks likely increased students’ individual knowledge
acquisition during the course. This is in accordance with Meyer’s (2010) claim regarding how
assignments should be structured and orchestrated to encourage learning to occur. It also
reinforces findings of Halic et al. that a “technological tool works better when it’s coupled with
compatible pedagogical conceptions,” and yet “interaction is insufficient to achieve cognitive
engagement. Some type of facilitation in online environments may be necessary” (2010, p.
211).
The findings of our case study, together with the described socio-technical design,
illustrate practical implications for designing the use of multiple social software tools to
support collaborative learning in higher education. Therefore, by providing an explicit socio-
technical example, this study can contribute to pedagogical practices when educators are
considering how they should use cloud-based social software as a learning platform
(Schroeder et al., 2010; Wheeler, 2009). First, the findings from this study contribute to the
emerging body of studies surrounding the empirical research regarding the educational use of
Web 2.0 and its adoption and impact (Crook, 2008). Second, this article is also a timely and
rare contribution to the emerging discussions on how to design and integrate the use of
multiple Web 2.0 tools in higher education contexts in a pedagogically meaningful way
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
instead of using legacy virtual learning environments (Hemmi et al., 2009; Schroeder et al.,
2010; Uzunboylu et al., 2011; Wheeler, 2009).
This case study was limited by the single-case design and the lack of other student
groups completing the same tasks with the same socio-technical design. The rationale for the
single-case design is that it is a revelatory case (Yin, 2003). In practise, this study is a rare
contribution to the empirical analysis of integrating face-to-face situations and social software
in higher education. In addition, the course in which the data collection was conducted was
the first implementation of the described socio-technical design at the university.
Furthermore, this study used embedded multiple units of analysis in order to
qualitatively collect and analyse complex dependencies between different learning phases and
students’ learning outcome, which raises concerns of a small sample size within subunits (Yin,
2003). To overcome the problems raised by the relatively small sample size, data was
analysed using Bayesian methods, which do not have theoretical minimums for sample sizes
and offer other benefits for explorative data analysis (Congdon, 2003; Jensen, 2001).
It also has been argued that research designs in authentic contexts inevitably provide
principles that can be localised for others to apply to new settings and to produce
explanations of innovative practises (Fishman, Marx, Blumenfeld, Krajcik, & Soloway, 2004).
Therefore, research investigations conducted in authentic contexts are still needed as a first
step to understand these new opportunities in terms of learning interaction and collaboration
that social software can provide.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Acknowledgements
This research was supported by the Doctoral Programme for Multidisciplinary Research on
Learning Environments, Finland, and a grant from the Finnish Cultural Foundation.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
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ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Table 1. Pre-test and post-test raw scores and normalized gain scores
Table 2. Importance ranking of the social software usage and learning activity variables by the level of normalized gain score
Class variable: The level of normalized gain scoreDropa
low-performers < 0.29
high-performers > 0.29
Predictor variablesb % M SD M SD
F.wiki.wc.activity90.91 2151.09
2074.12 3427.73
3810.10
F.wiki.wc.net
90.91 855.45 507.49 1173.91 444.70
F.wiki.edits.activity90.91 34.55 21.16 68.64 77.90
Note. In the classification modelling process (Silander & Tirri, 1999), the automatic search looked for the best set of variables to predict the class variable for each data item.a. Decrease in predictive classification if item is dropped from the classification model.b. Classification accuracy is 81.82%.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Table 3
DependencyProbability ratio
D.blog.posts -> F.wiki.wc.activity 1:1.000.000.000D.blog.posts -> F.wiki.wc.netD.blog.posts -> F.wiki.edits.activityF.wiki.edits.activity -> F.wiki.edits.comments 1:1.000.000D.blog.posts -> F.wiki.edits.netF.wiki.wc.activity -> GainGain -> F.wiki.wc.commentsC.photos -> D.blog.posts 1:2254G.rss.monitor -> F.wiki.wc.activity 1:975G.rss.monitor -> F.wiki.wc.net 1:975G.rss.monitor -> F.wiki.edits.activity 1:931G.rss.monitor -> F.wiki.wc.comments 1:880G.rss.monitor -> F.wiki.edits.net 1:798D.blog.words/post -> E.discussion 1:797G.rss.monitor -> C.photos 1:72E.discussion -> F.wiki.wc.activity 1:44E.discussion -> F.wiki.wc.net 1:44B.discussion -> F.wiki.wc.activity 1:44B.discussion -> F.wiki.wc.net 1:44E.discussion -> F.wiki.edits.net 1:44B.discussion -> F.wiki.edits.net 1:44E.discussion -> F.wiki.edits.activity 1:44B.discussion -> F.wiki.edits.activity 1:44B.discussion -> F.wiki.wc.comments 1:44B.discussion -> D.blog.posts 1:31C.photos -> F.wiki.wc.comments 1:26G.rss.monitor -> Gain 1:17G.rss.monitor -> F.wiki.edits.comments 1:14G.rss.monitor -> E.discussion 1:4.91D.blog.words/post -> C.photos 1:3.62G.rss.monitor -> B.discussion 1:2.69Note. The probability ratio describes the strength of statistical dependency between the two variables and the importance of the dependency for the model.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Appendix
Table 1. Descriptive statistics of students’ activities during the course
Descriptive statistics for face-to-face, social software activity and learning gain variables
All students (n=21) High-performers (n=10) Low-performers (n=11)Unit Mean Stdev Max Min Mean Stdev Max Min Mean Stdev Max Min
B. Reflect [discussion]B.discussion utterances 74.27 28.17 118 23 78.18 23.48 107 49 70.36 32.89 118 23
C. Conceptualize [photo-taking]C.photos photos 3.86 1.25 6 2 3.73 1.10 5 2 4.00 1.41 6 2
D. Reflect and elaborate [blogging]D.blog.posts posts 3.99 1.25 6 1.8 4.05 1.03 5.3 1.8 3.93 1.48 6 1.8
D.blog.words/postwords/post 88.09 37.76 153 9 101.27 40.11 153 30 74.91 31.67 128 9
E. Review and evaluate [discussion]E.discussion utterances 219.86 80.44 390 74 202.64 69.47 327 81 237.09 90.06 390 74
F. Co-construct knowledge [wiki-work]
F.wiki.edits.activity edits 51.59 58.37 271 4 68.64 77.90 271 5 34.55 21.16 72 4F.wiki.edits.net edits 16.86 14.71 59 2 19.91 17.47 59 3 13.82 11.36 42 2
F.wiki.wc.activity words2789.41 3064.02 12830 320 3427.73 3810.10 12830 355 2151.09 2074.12 6654 320
F.wiki.wc.net words1014.68 493.33 2067 122 1173.91 444.70 1854 353 855.45 507.49 2067 122
F.wiki.edits.comments edits 14.09 9.72 34 2 15.82 11.76 34 2 12.36 7.31 26 2F.wiki.wc.comments words 277.08 235.46 841 0 252.46 220.18 701 0 301.70 258.10 841 0
G. Monitor peer students’ contributions [monitor]G.rss.monitor read items 120.09 199.83 701 0 76.09 124.81 428 0 164.09 253.03 701 0
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
Normalized learning gainGain pre-post
gain0.29 0.16 0.60 0.00 0.42 0.08 0.6 0.31 0.16 0.08 0.27 0
Note. Mean, standard deviation and max-min values for all students (both high- and low-performing students) were calculated in order help interpret the results of Bayesian classification modelling and to provide an overview of the students’ activities during the course.
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration
ACCEPTED MANUSCRIPT Jari Laru, Piia Näykki, Sanna Järvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context, The Internet and Higher Education, Available online 28 August 2011, ISSN 1096-7516, 10.1016/j.iheduc.2011.08.004.(http://www.sciencedirect.com/science/article/pii/S1096751611000546)Keywords: Case study; Cloud-based social software; Explorative analysis; Higher education; Small-group collaboration