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Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context
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Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context

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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 …

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.

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  • 1. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationSupporting small-group learning using multiple Web 2.0 tools: A case study in the highereducation contextJARI LARU*Faculty of Education, University of Oulu, Snellmania,Oulu, P.O. Box 2000, 90014 University of Oulu, Finlandjari.laru@oulu.fi+358-40-5118478http://www.claimid.com/jarilaruPIIA NÄYKKI, SANNA JÄRVELÄFaculty of Education, University of Oulu, Snellmania,Oulu, P.O. Box 2000, 90014 University of Oulu, FinlandPiia.naykki@oulu.fi, sanna.jarvela@oulu.fiAbstract: In this single-casestudy, small groups of learners were supported by use of multiplesocial software tools and face-to-face activities in the context of higher education. The aim ofthe study was to explore how designed learning activities contribute to students’ learningoutcomes by studying probabilistic dependencies between the variables. Explorative Bayesianclassification analysis revealed that the best predictors of good learning outcomes were wiki-related activities. According to the Bayesian dependency model, students who were active inconceptualizing issues by taking photos were also active blog reflectors and collaborativeknowledge builders in their group. In general, the results indicated that interaction betweenindividual and collective actions likely increased individual knowledge acquisition during thecourse.Keywords: Case study, Cloud-based social software, Explorative analysis, Higher education,Small-group collaboration
  • 2. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration1.Introduction Technology is one of the most significant mechanisms currently transforming thelearning 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 situatedpractices (Pea, 1993). In particular, representational tools such as calculators and mind mapshave dramatically changed our daily practices in many spheres of life (Säljö, 2003). Newtechnologies provide opportunities for creating learning environments that extend thepossibilities of old technologies (e.g., books, blackboards, television, radio) and offer newprospects for multiple social interactions(Bransford, Brown, & Cocking, 1999). In recent years, a plethora of digital and networking tools hasbeen established on theInternet. These digital applications—which enable interaction, collaboration and sharingamong users—are frequently referred to as Web 2.0 (Birdsall, 2007) or social softwaretools(Kesim & Agaoglu, 2007). These applications arefurther assumed to be a step change in theevolution of Internet technology in higher education(Wheeler, 2009), which has evolved frombeing primarily used to distribute course materials, communicate and evaluate to being usedto enhance educational processes that support collaborative learning and knowledgebuilding(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 ofmultiple social software tools in higher education pedagogy has been published as ofyet(Uzunboylu, Bicen, & Cavus, 2011; Wheeler, 2009).
  • 3. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration Crook (2008)and Meyer(2010)have argued a need for more empirical research on theeducational use of Web 2.0, its adoption and its impact on higher education. In this single-casestudy,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 explorehow designed learning activities contribute to students’ learning outcomes by studying theprobabilistic dependencies between the variables.2. Theoretical background2.1. 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 metacognitivelearning in which students are encouraged to construct explanations, pose questions andprovide further information to each other(Cohen & Scardamalia, 1998). While constructingexplanations, the students become aware of their thought processes, gaps in knowledge andlack of understanding(Webb, 1989). Through contributing their ideas and making theirthought processesvisible, the students are able to reflect on their cognitive processes anddiscuss with others what they do or do not know and understand. Previous research (Xie et al., 2008)has shown that reflection is effortful action thatrequires external support in order to engage students for extended periods of time. Forexample, Xie et al. (2008)have introduced various strategies for encouraging reflection, andthey have concluded that blog-writing activities,journaling and peer feedback are allappropriate reflection strategies. Weblogs are popular journaling tools that offer students a means ofexternalising theirreasoning and reflecting on their experiences(Xie et al., 2008). Hence, Weblogs can be used as
  • 4. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration‘learning logs’ that capture the cumulative history of a learning project in action and recordpersonally meaningful material that can foster and facilitate reflective practices such asconversations 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 networkdiscussions: The students make their thinking visible and externalize their thinking byperiodically posting journal entries to their personal or collaborative blogs, allowing otherlearners to comment on their learning blogs(Xie et al., 2008). Second, in addition to self-reflective blog writing, peer feedback can provide a differentperspective and help students to assimilate and accommodate their thinking. Blogs canfacilitate reflective thinking, because people can easily access different points of view bylooking 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 variouscontributions 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 theliterature, which emphasizes students’ possibilities for constructing knowledge andexperiencing shared understanding through these groups(Dillenbourg, Baker, Blaye, & Malley,1996; Dillenbourg, 1999). Social software applications (e.g., wikis) provide new opportunities for collaborativelearning and knowledge building (Cress & Kimmerle, 2008; Dohn, 2009). Moreover, theypresent significant challenges to the views of knowledge(Cress & Kimmerle, 2008; Dohn,2009), learning(Crook, 2008; Ravenscroft, 2009) and goals of the proceduresimplicit in
  • 5. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationWeb2.0 practises on the one hand(Collins & Halverson, 2010; Crook, 2008; Dohn, 2009) andthe educational system on the other(Collins & Halverson, 2010; Dohn, 2009). Dohn(2009) has stressed that Web 2.0 and/or educational practises must be reshapedto fit each other, given that they originate in different contexts. From the perspective ofcollaboration within Web 2.0 tools, who contributes is less important than the fact thatcontributionsare made and that they stand a chance of being revised by adding, deleting orchanging their components until the outcome corresponds to group direction andconsensus(Dohn, 2009). Alternatively, Cress andKimmerle(2008) see an imminent connection betweencollaborative knowledge building in wikis and learning; from their perspective,one person’sindividual knowledge can serve as a resource for the learning of others. In their seminal paperon knowledge building with wikis, they describe how people make use of each other’sknowledge through collaborative knowledge building with artefacts. When interacting with awiki, individuals can learn as a result of either externalization or internalization. This learningcan take place by assimilation (extending knowledge by simply adding new information) orby accommodation (modifying and creating new knowledge). In this study, the pedagogical ideas behind the design are grounded in collaborativelearning, and special effort has been placed on enhancing and supporting collaborativelearning as a cognitive and social activity(Teasley, 1997). The students’ learning tasks,including social and individual activities, were supported by designing learning assignmentsthat consisted of recurrent individual and collective phases in which students used Web 2.0tools in concert to perform the designed tasks.
  • 6. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration In sum, all these activities to be undertaken with social software tools were alsoaligned in such way that Web 2.0 characteristics (Dohn, 2009)were taken into account. Forexample, Web-mediated resources were largely utilised; all created content was open, andwiki pages had distributed authorship; different materials were reproduced and transformedfrom multiple individual or collaborative learning spaces; and open-endedness and lack offinality were actively promoted to all participating students.3.Aims of the study In this single-casestudy, small groups of learners were supportedusing multiple socialsoftware tools and face-to-face activities in the context of higher education. The aim of thestudy was to explore how designed learning activities contribute to students’ learningoutcomes by studying the probabilistic dependencies between the variables.The researchquestions are as follows: 1) How much did students learn during the course? 2) Which socialsoftware and face-to-face variables were the best predictors for identifying differencesbetween high- and low-performing groups of students? 3) What was the impact of socialsoftware 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 asan empirical study that investigates a contemporary phenomenon within its real-life context,especially when the boundaries between the phenomenon and the context are notevident(Yin, 2003). In practise, the research design of the current study employed a single-case study withembedded multiple units of analysis. As multiple social software tools and face-to-faceactivities were usedto support learning in a higher education course, the behaviour of
  • 7. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationstudents within phases of the learning design and students’ learning outcomes wereconsidered as the embedded units. These units were analysed using quantitative techniques as the primary approach. Inorder to return to larger units of analysis, Bayesian methods (Jensen, 2001) were usedtoclassify 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 teachereducation programme in the Faculty of Education at the University of Finland. All of thestudents were enrolled in a required course titled Future Scenarios and Technologies inLearning 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 atthe university.4.1.1.The task The participants worked in groups of four to five students for 12 weeks. Groups wererequired to complete a wiki project by the end of the semester. In order to complete the wikiproject, students needed to participate in recurrent solo and collective phases mediated bythe use of social software tools and face-to-face meetings in their respective sessions (seeFigure 1). On the first day of the course, in a campus computer lab, the instructor gave allparticipating students pre-configured accounts to social software services and mobile devicesneeded for photo-taking activities (see Section 4.1.2). After ensuring that the students in their respective groups understood the instructionsprovided, no further support was provided during the tasks. In other words, the assignments
  • 8. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationrequired the students not only to learn and apply content knowledge, but also to generatetheir own learning objectives and to determine what information to include in their finalcontribution in their group wiki to be presented to the class. ---InsertFigure 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
  • 9. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; 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 studentsused multiple Web 2.0 tools and mobile phones in concert to perform designed tasks (Figure2).
  • 10. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration First, all students received a personal mobile multimedia computer, which wasintegrated with features including a 3.2 megapixel digital camera, 3G connectivity and anInternet browser.The mobile device was the main tool for the students in Phase C, who wererequired to identify and capture situated pictorial metaphors describing their group’s sharedinterests. The device was equipped with a ShoZu cloud-based file-sharingtool, whichwas used asa 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 theFlickrphotostream. 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 toshow all of the course-related content on the mobile phones at the students’ disposal (Figure2). Second, an individual Wordpress.com account was created for each student. Thisblogging service was used as a personal learning diary for the students in which theyindividually reflected further on their ideas by writing journal entries regarding therespective pictures/videos sent to blogs via the Flickrfile-sharing service (Phase C). Thestudents’ blogs were usedas 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-relatedannouncements. The cloud-based Wikispaceswiki service was also used for two purposes: First, itoffered collaboration tools for the groups to use (i.e., empty wiki page and discussion tool) inorder to support their collaborative knowledge co-construction (Phase F). Second, it was used
  • 11. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationat the course level for distributing resources (i.e., course curricula, lecture slides, hyperlinksand how-to guides) and displaying syndicated content from Flickr (student accounts) andWordPress (course blog, student blogs). In addition, the FeedBlendrand FeedBurnerRSS services were used to merge individual,group and class-level feeds from multiple Flickr, WordPress and Wikispacesaccounts. Inpractise, these merged feeds were included as RSS widgets in a sidebar of the respective blogor wiki. These tools enabled the students to combinesocial software tools, and theymay beseen as additional collaborative tools that facilitated relationships between different taskphases, 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 inparentheses 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 thatwere developed based on the key concepts of the course. Students were asked to writedefinitions of the lecture themes, meaning that each theme was also connected to the learningdesign described in Section 4.1.1. and was thus used for measuring the students’ learningoutcomes (gain) in a particular week of the course.
  • 12. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration4.2.2 Video data Video recordings captured each group’s six collaborative reflection sessions(B.discussion) and two collaborative reviewing and evaluation sessions (E.discussion) (42hours of video data). The duration of those sessions was determined by each group, and theaverage duration of one session was 44 minutes (where the duration ranged from 13 minutesto 86 minutes).4.2.3 Social software activity data Social software usage activity data was collected at the student level through multiplesources.First, the total number of Flickr photos per weekly topic and the average number ofphotos for all topics (C.photo) were calculated. Second, the total number of words in each blog entry and the number of blog entrieswere measuredfor each weekly topic. Then, the average values of these were calculated for alltopics (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 anddeletes as coding categories for cumulative history data. A measure of student cumulativeinvolvement in the wiki was given by the sum Activity(u) = add(u) + delete(u), called the editactivity 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 tocalculate students’ active use of their respective group wikis and their interactions in the wikidiscussion 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 groupwiki was given by the difference Net added (u) = add(u) – delete(u), called the net number ofwords added or edits performed, providing the total number of words or edits by which u
  • 13. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationincreased the length of the text (F.wiki.wc.net) or the number of edits (F.wiki.edits.net) whenwords or edits that u deleted were deducted. This value was used to calculate the amount ofnew content students contributed to the wiki. Finally, the total number of read RSS items was measured by using statistics collectedautomatically by Google Reader (G.rss.monitor).4.3 Data analysis Data was analysed using a quantitative paired samples t-test for the conceptualknowledge tests, qualitative on-task analysis for video recordings and multivariate Bayesianmethods for the dependencies between social software usage, face-to-face activities andlearning gain.4.3.1.Quantitative analysis of conceptual knowledge tests In the first stage of analysis, a conceptual knowledge test was analysed in order toanswer 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 wereas follows:0 points representedlow understanding (the student hadno understanding of theconcept).Onepoint representedsome level of understanding (the student hadsomeunderstanding (i.e., knew whatthe concept wasconnected to) but no detailed knowledge ofit).Twopoints represented abasic level of understanding (the student understoodwhat theconcept wasconnected to and knewsome details about the concept). Finally, 3 pointsrepresentedthe highest level of understanding (the student hada deep understanding of theconcept and knewvery specific details about the concept).
  • 14. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration The tests were analysed by marking points from 0 to 3 for individual answers. Thiswas done by three researchers who first independently marked the tests and then comparedthe results and negotiated possible differences. According to the test results, all of thestudents’ understanding of the main concepts increased during the course. However, therewere differences between their levels of understanding of the different concepts. To analyse the learning outcomes through the pre-test/post-test scores, a pairedsamples 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 theactivity and artefacts of high performers to those of low performers is intuitively appealing(Jonassen, Tessmer, & Hannum, 1999) and has been shown to reveal importantcharacteristics 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 individualstudents’ activity levels in collaborative face-to-face assignments. Results of this analysis wereused as an activity measure of face-to-face activities for descriptive analysis of learningphases and explorative Bayesian analysis (research questions 2 and 3). This analysis was adapted from the method that focuses on the duration of on-task andoff-task episodes (for further details of the method, seeJärvelä, Veermans,&Leinonen, 2008).In this analysis, the focus was placed on the number of task-related utterances, which wereused as a measure of on-task activities, while off-task activities, such as discussions abouttheir evening plans, were coded in an independent off-task category.
  • 15. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration4.3.3. 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 thecourse design. First, the average values of an individual student’s face-to-face and socialsoftware 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 ofBayesian classification modelling and to provide an overview of the students’ activities duringthe course (See Appendix).4.3.4. Bayesian multivariate analysis of the impact of social software and face-to-face sessionson learning outcome In the fourth phase, Bayesian analysis (Jensen, 2001) was conducted to study theprobabilistic dependencies between the variables (research questions 2 and 3) described inSection 4.2. In practise, the analysis was conducted with the Web-based online data analysistool 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, assummarized inCongdon (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 variablesand distributions are accepted, and 3) It gives statistically robust tools to visualize andcategorize complex dependencies between variables. In short, Bayesian methods enabled usto conduct statistical analyses of learning phases in our learning design.1 http://b-course.cs.helsinki.fi/obc/
  • 16. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; 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 socialsoftware and face-to-face variables were the best predictors for determining differencesbetween high- and low-performing groups of students? In the classification process, theautomatic search looked for the best set of variables to predict the class variable for each dataitem. This procedure is akin to the stepwise selection procedure in traditional lineardiscriminant 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 softwareand face-to-face sessions on individual students’ normalized learning gain? Such a Bayesiannetwork was the visualised result of Bayesian dependency modelling, in which the mostprobable 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 andTable 3): 1) collected data as ellipses, 2) dependencies visualised as lines between nodes and3) strength of each dependency as a ratio value in the table (see Table 3) and as a colour in thenetwork.The darker the line, the stronger the statistical dependency between the twovariables and the more important (higher ratio value) the dependency. A variable isconsideredindependent 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 studentslearned during the course. Second, the best predictors for pointing out differences betweenhigh- and low-performing groups will be explored using Bayesian classification analysis.
  • 17. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationThird, the results of Bayesian dependency modelling showing probability dependenciesbetween the social software, face-to-face sessions and individual students’ normalizedlearning 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-postnormalized gain scores. Usingthe average normalized gain score (M=0.29; SD=0.16), high-performing and low-performing students were identified for explorative Bayesianclassification analysis.5.2. Which social software and face-to-face variables were the best predictors for determiningdifferences between high- and low-performing groups of students? The second analysis explored which variables measuring social software usage andface-to-face activities were the best predictors for pointing out differences between high- andlow-performing students. The model for classifying data contained items according to theclass 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 itemsare 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. Thestrongest variables—that is, those that best discriminate the independent variables—are
  • 18. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationlisted first. The percentage values attached to each variable indicate the predicted decrease inthe classification performance if the variable were to be dropped from the model. The tableshows that all variables in the model are equally important; that is, if we were to remove anyof 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 higherlearning gains were wiki-related activities. First, the mean number of wiki edits (F.wiki.edits.activity; M=68.64; SD=77.90) wastwo 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-performingstudents (M=856.45; SD=507.49). In short, the descriptive analysis above shows that high performers were more activein organizing wiki content in a new way and in adding new information. The latter of thesecontribution categories is an example of assimilation, a process in which information comingfrom the wiki is perceived and modified in a way that makes it fit into the individual’sknowledge. The former category is an example of an activity in which students do not simplyassimilate new information into existing knowledge but actually change knowledge in orderto better understand the wiki and its information(Cress & Kimmerle, 2008).
  • 19. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration5.3. 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 itemsmeasuring students’ learning activities during the course (descriptive values are shown inAppendix I, and items are described in Section 4.2). The rationale for this procedure was toexamine dependencies between variables by both their visual representation and theprobability 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 thehighest probability. During the extensive search, 174,987 models were evaluated. Figure 3shows a visualization of the network, which contains two components: 1) collected data asellipses and 2) dependencies visualised as lines between nodes. As mentioned, the darker theline, the stronger the statistical dependency between the two variables and the moreimportant the dependency. Table 3 shows the strength of each dependency as ratio values inthe probability table. In practise, if one removes the arc from the model with the high probability ratio, itdecreases the probability of the model by the same amount. However, in many dependenciesin the model,removing the arc between nodes would not change the probability of the finalmodel (listed at the bottom of the probability table). --- InsertFigure 3 about here --- --- InsertTable 3 about here --- The Bayesian dependency model shows 7strong (probability ratio >1,000,000) and 25weaker relationships between variables. However, based on the analysis, only one strongdependency between activities and learning gain was found: the connection between
  • 20. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationassimilative wiki editing activities (F.wiki.wc.activity) and learning gain (gain), whichtriangulates with the results in the Bayesian classification modelpresented 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, therewere two other connections between other variables (B.discussion, C.photo) and normalizedlearning gain (gain) included in the visual network model, but their probabilities were so lowthat they were dropped from the dependency table automatically. It is worth noting that thewiki activities described above were strongly related to commenting on wiki content. When the Bayesian model is further explored, it reveals that the average number ofblog posts (D.blog.posts) is the central variable in the model, as it has strong statisticalrelationships to both assimilative (F.wiki.wc.net; F.wiki.edits.net) and accommodative wikiactivities (F.wiki.wc.activity; F.wiki.edits.activity). In practice, it can be said that students whowere actively reflecting and elaborating were also active in inserting and modifyinginformationin the wikis. This variable (D.blog.posts) also has a central role in the chain ofstrong 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 ofWeb 2.0 characteristics in this study, an example of a series of activities in whichintermediatelearning products were reproduced and transformed. Furthermore, it shows how highereducation course students can make use of each other’s knowledge through collaborativeknowledge building(Cress & Kimmerle, 2008). There were also several weaker dependencies in the Bayesian model. First, resultsshowed that active following of RSS feeds was slightly related to an increased number of
  • 21. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationsituated visual representations (C.photos), an increased numberofwiki editing activities(F.wiki.*) and learning gain (gain). However, no connection was foundbetween usage of RSSfeeds and blogging. Second, both collaborative face-to-face phases (B.discuss, D.discuss) wereslightly related to social software usage (D.blog.*; F.wiki.*) except the phase in whichstudentshad to take photos.6.Discussion In our case, we found that using social software tools together to perform multipletasks likely increased individual knowledge acquisition during the course. MultivariateBayesian classification analysis revealed that the best predictors ofgood learning outcomeswere wiki-related activities. In addition, according to the Bayesian dependency model,students who monitored their peers’ work via syndication services and who were active byadding, modifying or deleting text in their group’s wiki obtainedhigher scores. The model alsoshows that many other learning activities were indirectly related to learning outcome. First, learning scores from pre-test to post-test were statistically significant with highlearning effect, indicating a substantial gain in conceptual knowledge test scores from pre-testto post-test. This finding provides support for the learning design used in this study and forthe use of multiple cloud-based social software tools in a higher education context, and it wasfurther used to contrast high performers and low performers in the following explorativeBayesian analysis. Second, results from the Bayesian classification analysis revealed differences betweenhigh performers and low performers and showed that the best predictors ofhigher learninggain were wiki-related activities. Descriptive analysis of chosen predictor variables showedthat high performers were more active in organizing wiki content in a new way (mean
  • 22. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationnumberof wiki edits was two times higher and mean word length of edited content was twotimes higher when compared to low performers) and in adding new information (mean lengthof inserted words was 1.4 times higher than thatof low-performers). The latter of thesecontribution categories is an example of assimilation, a process in whichinformation comingfrom the wiki is perceived and modified in a way that makes it fit into an individual’sknowledge. The formercategory is an example of an activity in whichstudents do not simplyassimilate new information into existing knowledge but actually change knowledge in orderto better understand the wiki and its information(Cress & Kimmerle, 2008). After 174,987 models were calculated, the final Bayesian dependency modelincluded 7strong relationships and 25 weaker relationships between variables. Interestingly, the onlystrong dependency between activities and learning outcome was found between assimilativewiki editing activities and learning gain, which triangulates with results in Bayesianclassification modelling. Furthermore, there was one weak dependency, between monitoringother students’ work via syndication services and learning outcome. There were two otherconnections between other variables and learning gain included in the network model, buttheir probabilities were so low that removing them would not change the probability of thefinal model, and therefore, those were dropped automatically from the final model during theanalysis. It is also worth noting that the wiki activities described above were strongly relatedto commenting on wiki content. When the Bayesian model is further explored, it reveals that the average number ofblog posts per student is the central variable in the model, as it has strong statisticalrelationships to both assimilative and accommodative wiki activities. In practise, it can be saidthat students who were actively reflecting and elaborating on visual representations in their
  • 23. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationown blogs were also active in inserting and modifying knowledge in the wikis. This can beconsidered an example of learning that is both reflective and collaborative at the juxtapositionof 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 photostaken 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 2.0characteristics in this study, an example of a series of activities in whichintermediate learningproducts were reproduced and transformed by performing structured collaborativeassignmentsusing Web2.0 tools. It also shows how higher education course students canmake use of each other’s knowledge through collaborative knowledge building(Cress &Kimmerle, 2008). The remaining variables were weaker than those presented above. First, the resultsshowed that monitoring who does what (implicit peer feedback for individual reflection)using syndication tools (RSS) was slightly related to an increased number of situated visualrepresentations (photos), numberof wiki editing activities and learning gain. However, themodel did not show connections between blog and syndication variables. Therefore, it can beargued that different perspectives on the form of syndicated content did not contribute toreflective blog-writing activities. Instead, the results showed that active monitoring of theactivities of others usingdifferent social software tools increased students’ number ofwikiactivities. Generally, these results further reinforcedthe findings ofJermann and Dillenbourg(2008), who determinedthat the tools can provide information to foster group members’reflections of their contributions: ‘what to do’ and ‘who does what’. Second, the results
  • 24. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationrevealedthat the explicit peer feedback that students receivedby participating in collaborativeface-to-face sessions (sense-making session and meaning-making session) slightly increasedsocial software usage activities.7. Conclusion It can be concluded that the carefully crafted pedagogical activities and Web 2.0 toolsused together to perform designed tasks likely increased students’ individual knowledgeacquisition during the course. This is in accordance with Meyer’s (2010) claim regarding howassignments should be structured and orchestrated to encouragelearning to occur. It alsoreinforces findings ofHalic et al.that a “technological tool works better when it’s coupled withcompatible pedagogical conceptions,” and yet “interaction is insufficient to achieve cognitiveengagement. 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 toolstosupport collaborative learning in higher education. Therefore, by providing an explicitsocio-technical example, this study can contribute to pedagogical practices when educatorsare considering how they should use cloud-based social software as a learningplatform(Schroeder et al., 2010; Wheeler, 2009). First, the findings from this study contributeto the emerging body of studies surrounding the empirical research regarding the educationaluse of Web 2.0 and its adoption and impact(Crook, 2008). Second, this article is also a timelyand rare contribution to the emerging discussions on how to design and integrate the use ofmultiple Web 2.0 tools in higher education contexts in a pedagogically meaningful way
  • 25. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationinstead 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 studentgroups completing the same tasks with the same socio-technical design. The rationale for thesingle-case design is that it is a revelatory case(Yin, 2003). In practise, this study is a rarecontribution to the empirical analysis of integrating face-to-face situations and social softwarein higher education. In addition, the course in which the data collection was conducted wasthe first implementation of the described socio-technical design at the university. Furthermore, this study used embedded multiple units of analysis in order toqualitatively collect and analyse complex dependencies between different learning phasesand students’ learning outcome, which raises concerns of a small sample size withinsubunits(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 forsample 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 provideprinciples that can be localised for others to apply to new settings and to produceexplanations of innovative practises(Fishman, Marx, Blumenfeld, Krajcik, & Soloway, 2004).Therefore, research investigations conducted in authentic contexts are still needed as a firststep to understand these new opportunities in terms of learning interaction and collaborationthat social software can provide.
  • 26. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration AcknowledgementsThis research was supported by the Doctoral Programme for Multidisciplinary Research onLearning Environments, Finland, and a grant from the Finnish Cultural Foundation.
  • 27. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration ReferencesArnold, N., & Paulus, T. (2010).Using a social networking site for experiential learning: Appropriating, lurking, modeling and community building. The Internet and Higher Education, 13(4), 188-196. doi:10.1016/j.iheduc.2010.04.002Birdsall, W. F. (2007). Web 2.0 as a social movement.Webology, 4(2). Retrieved from http://www.webology.ir/2007/v4n2/a40.htmlBransford, J., Brown, A. L., & Cocking, R. R. (1999).How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press. Retrieved from http://www.nap.edu/catalog.php?record_id=9853Cohen, A., &Scardamalia, M. (1998). Discourse about ideas: Monitoring and regulation in face- to-face and computer-mediated environments. Interactive Learning Environments, 6(1- 2), 93-113. doi:10.1076/ilee.6.1.93.3610Collins, A., & Halverson, R. (2010).The second educational revolution: Rethinking education in the age of technology. Journal of Computer Assisted Learning, 26(1), 18-27. doi:10.1111/j.1365-2729.2009.00339.xCongdon, P. (2003). Applied Bayesian modelling.Chichester: Wiley.Cress, U., &Kimmerle, J. (2008).A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3(2), 105-122. doi:10.1007/s11412-007-9035-zCrook, C. (2008). Web 2.0 technologies for learning: The current landscape - opportunities, challenges and tensions (British Educational Communications and Technology Agency (BECTA) Report: Web 2.0 technologies for learning at Key Stages 3 and 4). Retrieved from http://dera.ioe.ac.uk/1474/
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  • 29. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationJärvelä, S., Veermans, M., & Leinonen, P. (2008). Investigating student engagement in computer-supported inquiry: A process-oriented analysis. Social Psychology in Education, 11(3), 299-322. doi:10.1007/s11218-007-9047-6Jensen, F. V. (2001). Bayesian networks and decision graphs.New York: Springer.Jermann, P., &Dillenbourg, P. (2008). Group mirrors to support interaction regulation in collaborative problem solving. Computers & Education, 51(1), 279-296. doi:10.1016/j.compedu.2007.05.012Jonassen, D. H., Tessmer, M., & Hannum, W. H. (1999). Task analysis methods for instructional design.Mahwah, NJ: L. Erlbaum Associates.Kesim, E., &Agaoglu, E. (2007).A paradigm shift in distance education: Web 2.0 and social software. Turkish Online Journal of Distance Education, 8(3), 66-75. Retrieved from http://tojde.anadolu.edu.tr/tojde27/Lee, M. J. W., Miller, C., & Newnham, L. (2008). RSS and content syndication in higher education: Subscribing to a new model of teaching and learning. Educational Media International, 45(4), 311-322. doi:10.1080/09523980802573255Meyer, K. A. (2010). Web 2.0 research: Introduction to the special issue. The Internet and Higher Education, 13(4), 177-178. doi:10.1016/j.iheduc.2010.07.004Myllymäki, P., Silander, T., Tirri, H., &Uronen, P. (2002).B-course: A web-based tool for Bayesian and causal data analysis. International Journal on Artificial Intelligence Tools, 11(3), 369-387. doi:10.1142/S0218213002000940Pea, R. D. (1993).Practises of distributed intelligence and designs for education.In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 47-87). New York, NY: Cambridge University Press.
  • 30. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationRavenscroft, A. (2009). Social software, web 2.0 and learning: Status and implications of an evolving paradigm. Journal of Computer Assisted Learning, 25(1), 1-5. doi:10.1111/j.1365-2729.2008.00308.xSchroeder, A., Minocha, S., & Schneider, C. (2010).The strengths, weaknesses, opportunities and threats of using social software in higher and further education teaching and learning. Journal of Computer Assisted Learning, 26(3), 159-174. doi:10.1111/j.1365- 2729.2010.00347.xSharma, P., & Fiedler, S. (2007). Supporting self-organized learning with personal web publishing technologies and practices.Journal of Computing in Higher Education, 18(2), 3-24.doi:10.1007/BF03033411Silander, T., &Tirri, H. (1999).Bayesian classification.In P. Ruohotie, H. Tirri, P. Nokelainen,& T. Silander (Eds.), Modern modeling of professional growth (pp. 61-84).Hämeenlinna: RCVE.Säljö, R. (2003). Representational tools and the transformation of learning. In B. Wasson, U. Hoppe,& S. Ludvigsen (Eds.), Designing for change in networked learning environments (pp. 1-2). Dordrecht, The Netherlands: Kluwer Academic Publishers.Teasley, S. D. (1997). Talking about reasoning: How important is the peer in peer collaborations? In L. B. Resnick, R. Saljo, C. Pontecorvo,& B. Burge (Eds.), Discourse, tools, and reasoning: Situated cognition and technologically supported environments (pp. 361-384). Heidelberg, Germany: Springer-Verlag.Uzunboylu, H., Bicen, H., & Cavus, N. (2011). The efficient virtual learning environment: A case study of web 2.0 tools and windows live spaces. Computers & Education, 56(3), 720- 726. doi:10.1016/j.compedu.2010.10.014
  • 31. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationWebb, M. N. (1989). Peer interaction and learning in small groups. International Journal of Educational Research, 13, 21-40. doi:10.1016/0883-0355(89)90014-1Wheeler, S. (2009). Learning space mashups: Combining web 2.0 tools to create collaborative and reflective learning spaces. Future Internet, 1(1), 3-13. doi:10.3390/fi1010003Wyman, B. G., &Randel, J. M. (1998).The relation of knowledge organization to performance of a complex cognitive task.Applied Cognitive Psychology, 12(3), 251- 264.doi:10.1002/(SICI)1099-0720(199806)12:3<251::AID-ACP510>3.0.CO;2-FXie, Y., Ke, F., & Sharma, P. (2008). The effect of peer feedback for blogging on college students’ reflective learning processes. The Internet and Higher Education, 11(1), 18- 25. doi:10.1016/j.iheduc.2007.11.001Yin, R. K. (2003).Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage Publications.
  • 32. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationTable 1.Pre-test and post-test raw scores and normalized gain scores Pretest Posttest Normalized score score gainCondition M SD M SD M SDAll students 3.95 2.66 7.95 2.92 0.29 0.16High performers 4.27 2.87 10.00 1.95 0.42 0.08Low performers 3.64 2.54 5.91 2.21 0.16 0.08Note. Normalized learning gain was calculated by using Hakes (1998) approach. Next, theaverage normalized gain scores were used to identify high-performing and low-performingstudents for following Bayesian classification analysis.Table 2.Importance ranking of the social software usage and learning activity variables by thelevel of normalized gain score Class variable: The level of normalized gain score Drop low-performers < high-performers > a 0.29 0.29Predictor variablesb % M SD M SD 2074.1 3810.1 F.wiki.wc.activity 90.91 2151.09 2 3427.73 0 F.wiki.wc.ne t 90.91 855.45 507.49 1173.91 444.70 F.wiki.edits.activity 90.91 34.55 21.16 68.64 77.90Note. In the classification modelling process (Silander&Tirri, 1999), the automatic searchlooked 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 is81.82%.
  • 33. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationTable 3 ProbabilityDependency ratioD.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 dependencybetween the two variables and the importance of the dependency for the model.
  • 34. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the highereducation 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 collaborationAppendixTable 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 MinB. Reflect [discussion] B.discussion utterances 74.27 28.17 118 23 78.18 23.48 107 49 70.36 32.89 118 23C. Conceptualize [photo-taking] C.photos photos 3.86 1.25 6 2 3.73 1.10 5 2 4.00 1.41 6 2D. 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/post words/post 88.09 37.76 153 9 101.27 40.11 153 30 74.91 31.67 128 9E. Review and evaluate [discussion] E.discussion utterances 219.86 80.44 390 74 202.64 69.47 327 81 237.09 90.06 390 74F. 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 4 F.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 words 2789.41 3064.02 12830 320 3427.73 3810.10 12830 355 2151.09 2074.12 6654 320 F.wiki.wc.net words 1014.68 493.33 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 2 F.wiki.wc.comments words 277.08 235.46 841 0 252.46 220.18 701 0 301.70 258.10 841 0G. 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 0Normalized learning gain Gain pre-post 0.29 0.16 0.60 0.00 0.42 0.08 0.6 0.31 0.16 0.08 0.27 0 gainNote. Mean, standard deviation and max-min values for all students (both high-and low-performing students) were calculated in order help interpret the results ofBayesian classification modelling and to providean overview of the students’ activities during the course.
  • 35. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web 2.0 tools: A case study in the highereducation 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
  • 36. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaboration
  • 37. ACCEPTED MANUSCRIPTJariLaru, PiiaNäykki, SannaJärvelä, Supporting small-group learning using multiple Web2.0 tools: A case study in the higher education context, The Internet and HigherEducation, 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; Highereducation; Small-group collaborationFigure 3. Graphical visualization of Ba yesian network (Jensen, 2001) contains three components: 1) collecteddata as ellipses; 2) dependencies visualised as lines between nodes, and 3) strength of each dependency ascolor in the network. The dark er the line, the stronger is the statistical dependency between the two v ariables,and the more important (higher r atio value) the dependency is for the model. R emoving the dependencybetween B.discussion and Gain; C.photos and Gain; D .blog.words/post and F.wiki.wc.net; D.blog.words/postand F.wiki.wc.activity; D.blog.words/post and F.wiki.edits.net; D.blog.words/post and F.wiki.edits.activity; andE.discussion and F.wiki.edits.comments would not change the probabilit y of the final model.

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