October 2007 Volume 10 Number 4 - Educational Technology ...

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Järvelä, S., Näykki, P., Laru, J., & Luokkanen., T. (2007). Structuring and Regulating Collaborative Learning in Higher Education with Wireless Networks and Mobile Tools. Educational Technology & Society, 10 (4), 71-79. Structuring and Regulating Collaborative Learning in Higher Education with Wireless Networks and Mobile Tools Sanna Järvelä, Piia Näykki, Jari Laru and Tiina Luokkanen University of Oulu, Finland // sanna.jarvela@oulu.fi // Fax +358 8 553 3744 ABSTRACT In our recent research we have explored possibilities to scaffold collaborative learning in higher education with wireless networks and mobile tools. The pedagogical ideas are grounded on concepts of collaborative learning, including the socially shared origin of cognition, as well as self-regulated learning theory. This paper presents our three design experiments on mobile, handheld supported collaborative learning. All experiments are aimed at investigating novel ways to structure and regulate individual and collaborative learning with smartphones. In the first study a Mobile Lecture Interaction tool (M.L.I.) was used to facilitate higher education students’ self-regulated learning in university lectures. In the second study smartphones were used as regulation tools to scaffold collaboration by supporting externalization of knowledge representations in individual and collaborative levels. The third study demonstrates how face to face and social software integrated collaborative learning supported with smartphones can be used for facilitating socially shared collaboration and community building. In conclusion, it is stressed that there is a need to place students in various situations in which they can engage in effortful interactions in order to build a shared understanding. Wireless networks and mobile tools will provide multiple opportunities for bridging different contents and contexts as well as virtual and face to face learning interactions in higher education. Keywords Collaborative learning, Higher education, Mobile tools, Self-regulated learning, Wireless networks Introduction Recent developments in mobile technologies have contributed to the potential to support learners studying a variety of subjects (Scanlon, Jones & Waycott, 2005; Sharples, 2000) in elementary education (Zurita & Nussbaum, 2007) as well as in higher education (Baggetun & Wasson, 2006; Näykki & Järvelä, 2007; Milrad & Jackson, in press). Furthermore, there have also been efforts to improve the performance of knowledge workers in work-place settings (Brodt & Verburg, 2007). The integration of social software (web2.0) (Kolbitsch & Maurer, 2006; Cress & Kimmerle, 2007) and new mobile technologies (Kurti, Milrad & Spikol, 2007) has created interesting new possibilities for organizing novel learning and working situations. In higher education much effort has been made to find new ways to support individual student learning, but also to find ways for effective collaboration. Previous studies have explored how mobile technology can be used for offering an additional channel for the lecture interaction. The Classtalk project focused on giving lecturers the ability to pose questions to students (Dufresne, Gerace, Leonard, Mestre, & Wenk, 1996) while the eClass project provided facilities for structured capture and access of classroom lecture activities (Abowd, 1999). Ratto, Shapiro, Truong and Grisworld (2003) developed the ActiveClass application for encouraging lecture participation by using personal wireless devices. Furthermore, in order to support collaborative learning, new possibilities of mobile technologies have been explored. Interactive logbook (Chan, Corlet, Sharples, Ting & Westmanncott, 2005) provided the technology for knowledge sharing and multimedia notetaking, while many campus-wide laptop initiatives have provided students with access to social computing tools, such as instant messaging or chat (Gay, Stefanone, Grace- Martin, Hembrooke, 2001). Overall, the general claim has been that when new technologies and software have been used in an educational setting, new learning opportunities have arisen. Thus far there have been plenty of case studies and design experiments where mobile technologies have been used for innovative pedagogical ideas and design studies. However, only a few studies give detailed arguments as to what are these new opportunities in terms of learning interaction and collaboration and what are the exact processes that mobile tools can scaffold. We claim that it is not only the learner being “mobile” that matters. A stronger argument for applying mobile tools for education is that of increasing students’ opportunities for interactions and sharing ideas and thus, increasing opportunities for an active mind in multiple contexts (Dillenbourg, Järvelä, & Fischer, in press). In this paper we describe our current research focusing on exploring possibilities to scaffold collaborative learning in higher education with wireless networks and mobile tools. The pedagogical ideas are grounded on collaborative learning, including the socially shared origin of ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 71
cognition as well as self-regulated learning theory. This is to say that special effort has been put on enhancing and scaffolding collaborative learning as cognitive, social, and motivated activity. Structuring and regulating collaborative learning Earlier research on collaborative learning has pointed out that shared understanding is not easy to achieve (Häkkinen & Järvelä, 2006; Leinonen, Järvelä & Häkkinen, 2005), and that students face difficulties engaging in learning and achieving their learning goals in a variety of learning contexts, including technology supported learning environments (Volet & Järvelä, 2001). In order to favour the emergence of productive interactions and to improve the quality of learning, different pedagogical models and technology-based regulation tools have been developed to support collaboration between participants. One way to enhance the process of collaboration, as well as to integrate individual and group-level perspectives of learning, is to structure learners’ actions with the aid of scaffolding or scripted cooperation (Fischer, Kollar, Mandl, & Haake, 2007). One of the ideas in this field is to design scripts that can be defined as “a set of instructions prescribing how students should perform in groups, how they should interact and collaborate and how they should solve the problem” (Dillenbourg, 2002, p. 63), that can be modified according to what kind of interaction, learning or outcomes are expected to be achieved. In scripted collaboration participants are supposed to follow prescriptions and engage in learning tasks. In addition to scripting as a mechanism to structure collaboration, we suggest that structuring can be enriched with technology-based regulation tools, which offer an individual and a group of learners opportunities to self-regulate their collaborative learning processes. Self-regulated learning theory concerns how learners develop learning skills and use learning skills effectively (Boekaerts, Pintrich & Zeidner, 2000). Self-regulated learners take charge of their own learning by choosing and setting goals, using individual strategies in order to monitor, regulate and control the different aspects influencing the learning process and evaluating his or her actions. Eventually, they become less dependent on others and on the contextual features in a learning situation. Although research into self-regulation has traditionally focussed on the individual perspective, there is increasing interest in considering the mental activities that are part of self-regulated learning at the social level, with reference to concepts such as social regulation, coregulation and shared regulation (McCaslin, 2004). Järvelä, Volet & Järvenoja (2005) characterize self-regulated learning from three perspectives. Self-regulated learning focuses on an individual as a regulator of a behavior and refers to the process of becoming a strategic learner by regulating their cognition, motivation and behavior to optimize learning (Schunk & Zimmerman, 1994). Conceptualizing self-regulated learning as a co-regulation has been influenced by socio-cultural theory and it emphasizes gradual appropriation of sharing common problems and tasks through interpersonal interaction (Hadwin, Wosney & Pontin, 2005; McCaslin & Hickey, 2001). The third perspective looks at how the regulation process can be framed to shared cognition and recent research on collaborative learning, which is in essence the co-construction of shared understanding (Roschelle & Teasley, 1995). This is collective regulation, where groups develop shared awareness of goals, progress, and tasks toward co-constructed regulatory processes, thereby sharing regulation processes as collective processes. Self-regulated learning theory has been used in our studies as a theoretical framework to develop those learning activities that give potential to individual and collaborative learning so that it stimulates active minds and interactions on individual and social levels. There are not yet studies which use mobile technology for supporting self-regulated learning, but recently there have been specific efforts made by people working on self-regulated learning theory to find ways to design technology to assist in helping students develop better learning strategies and regulate their learning process (e.g. Winne et al., 2006). Previous studies have explored how visualization as a form of regulation tool can be used for supporting individuals’ understanding (Larkin & Simon, 1987) or awareness of each others ideas (Fisher, Bruun, Gräsel, & Mandl, 2002; Leinonen & Järvelä, 2006). Visualizing individuals’ understanding can create for a group of learners a shared reference point, which supports focusing on central issues, for example shared or non-shared knowledge in a group’s interaction (Pea, 1994). Opportunities have been also searched from computer-based regulation tools, which aim to promote cognitive regulation processes. Learning tools are to promote motivated learning from the point of view of the individual learner as well as in opening new learning opportunities for social and interactive learning (Azevedo, 2005). This developmental work can be used for compensating weak study and collaboration skills in different 72
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Abstracting and Indexing Educationa
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The Relationship of Kolb Learning S
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a question about learning together
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affordances of networked learning s
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on at different times and work indi
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wanted to work. The same inquiry pr
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usability studies have a place in t
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Milrad, M., & Jackskon, M. (2007).
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information- and communication tech
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Resources 0.55 0.72 e17 In my schoo
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teachers’ instructional evolution
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Regression models indicating relati
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of beliefs about integrating comput
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Sinko, M., & Lehtinen, E. (1999). T
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concept of social support. Lastly,
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Procedure Content analysis procedur
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and mice are out of order. I have t
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feature of supportive online groups
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Anderson, J., & Lee, A. (1995). Lit
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Schwab, R. L., Jackson, S. E., & Sc
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Investigating the problem An extens
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meets the anywhere, anytime require
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minimise housekeeping tasks and fre
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engineering the Learning Shell so i
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organized in four groups, namely: T
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The correlations were not high enou
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Data illustrate some reliability an
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nature, some specific core objectiv
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Frederiksen, J.R., & Collins, A. (1
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Bottino, R. M., & Robotti, E. (2007
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The Text Editor allows the editing
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Mathematical content and structure
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Consolidation exercises Solution co
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handle more formal representations
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Lagrange, J. B., Artigue, M., Labor
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processed information was classifie
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Method Participants The participant
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peers and benefited most from discu
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Kayes, D.C. (2005). Internal validi
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This paper considers the potential
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Downes, S. (2003). Design and Reusa
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Appendix A. Good-enough approaches
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Rapid Prototyping and Design Based
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Initial Design Because the field-ba
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• reducing the number of required
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that they felt this hindrance; I fe
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goals” (Edelson, 2002, p. 114) wa
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more and more from the periphery of
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Edelson, M. (1988). The hermeneutic
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Wang, H.-Y., & Chen, S. M. (2007).
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we can see that S( X ) − S( Y ) S
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columns shown in Table 2, where 1
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(3) Find the maximum value among th
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Q.2 carries 25 marks, Q.3 carries 2
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the proposed methods can evaluate s
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Gogoulou, A., Gouli, E., Grigoriado
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enabling learner (subject) to work
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• Self-, Peer- and Collaborative-
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(iii) Regulates the communication:
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Figure 3. A screen shot of the SCAL
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2 nd study: Thirty-five students pa
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them characterized it as time and e
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Soller, A. (2001). Supporting Socia
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making reference to the others wher
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Solution 2.2: Deliberately select h
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just near a deadline, when it may b
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assessed on an individual basis. Wi
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- and even appreciated - by student
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Panitz, T., & Panitz, P. (1998). En
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Concept Mapping Concept mapping by
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Research Questions In order to dete
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with their teacher but rather than
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Tukey’s HSD post hoc test reveale
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collaboratively during study time?
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Jegede, O.J., Alaiyemola, F.F., & O
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esources. The use of synchronous te
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The use of digital communication mo
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contributed more information and en
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3. Use the second screen for the le
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• Use drawing to develop writing
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student and teacher on the synchron
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Vogel, J. J., Vogel, D. S., Cannon-
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Cases The first set of ten case stu
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Kılıçkaya, F. (2007). Website re
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