Scaffolding collaborative learning with cognitive tools based on mobile computers

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Scaffolding collaborative learning with cognitive tools based on mobile computers

  1. 1. Scaffolding collaborative learning with cognitive tools based on mobile computers Jari Laru University of Oulu Department of Educational Sciences and Teacher Education Research Unit for Learning & Educational Technology (LET) KTK235, Snellmania Po.Box 2000, 90014 University of Oulu
  2. 2. Methodological insights:Case study research & Design Based Research
  3. 3. Kurti, Arianit, Exploring the multiple dimensions of context: Implications for the design and development ofinnovative technology-enhanced learning environments. - Växjö : Växjö University Press, 2009. - (ActaWexionensia ; 180/2009)Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage Publications
  4. 4. Peterson, R. & Herrington, J. (2005). The State of the Art of Design-Based Research. In G. Richards (Ed.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2005 (pp. 2302-2307). Chesapeake, VA: AACE. Retrieved fromhttp://www.editlib.org/p/21540.-- Additional readings ---Sandoval, W. A., & Bell, P. L. (2004). Design-Based Research Methods For Studying Learning In Context: Introduction. Educational Psychologist,39(4), 199-201.http://www.designbasedresearch.org/publications.html
  5. 5. My research:Scaffolding collaborative learning with cognitivetools based on mobile computers
  6. 6. AIMS from past to todayThis thesis work focuses on developing and analyzinginnovative ways of supporting applying the framework ofdistributed scaffolding for learning activities in authenticreal world contexts.In this study theoretical ideas of cognitive tools,collaborative learning and scaffolding are applied fordesigning light-weight mobile software and pedagogicalmodels for learning in authentic real world contexts.This is done in order to generate new knowledge andsolutions that advance collaborative learning in mobilecomputer supported collaborative learning
  7. 7. Introduction Earli SIGMobile computers Everyday contexts Scaffolding collaborative Master’s programme, University, Professional learning with cognitive Community, K-12 students, tools based on Higher Education students, Nature school mobile computers Case I Case II Case III workplace (n=10) Nature (N=22) University (N=22) EMI ILE INTHIG
  8. 8. Social patterns in mobile technology mediated collaboration among members of the professional distanceeducation communityThe aim of this study was to identify social patterns in mobile technology mediated collaboration among distributed members of theprofessional distance education community. Ten participants worked for twelve weeks designing a master’s programme in InformationSciences. The participants’ mobile technology usage activity and interview data were first analyzed to get an overview of the densityand distribution of collaboration at individual and community levels. Secondly, the results of the social network analyses wereinterpreted to explore how different social network patterns of relationships affect online and offline interactions. Thirdly, qualitativedescriptions of participant teamwork were analysed to provide practical examples and explanations. Overall, the analyses revealednonparticipative behaviour within the online community. The social network analysis revealed structural holes and sparse collaborationamong participants in the offline community. It was found that due to their separated practices in the offline community, they didn’thave a need for mobile collaboration tools in their practices.Laru, J. & Järvelä, S. (2008). Social patterns in mobile technology mediated collaboration among members of the professional distance educationcommunity. Educational Media International Journal, 45(1),17-3. Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners This study explores how collaborative inquiry learning can be supported with multiple scaffolding agents in a real-life field trip context. In practice, a mobile peer-to-peer messaging tool provided meta-cognitive and procedural support, while tutors and a nature guide provided more dynamic scaffolding in order to support argumentative discussions between groups of students during the cocreation of knowledge claims. The aim of the analysis was to identify and compare top- and low-performing dyads/triads in order to reveal the differences regarding their co-construction of arguments while creating knowledge claims. Although the results revealed several shortcomings in the types of argumentation, it could be established that differences between the top performers and low performers were statistically significant in terms of social modes of argumentation, the use of warrants in the mobile tool and in overall participation. In general, the use of the mobile tool likely promoted important interaction during inquiry learning, but led to superficial epistemological quality in the knowledge claim messages. Laru, J., Järvelä, S. & Clariana, R. (2010). Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners. Interactive Learning Environments, Online first, 1-15. doi:10.1080/10494821003771350 Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context 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. The participants (n=22) worked in groups of four to five students for 12 weeks. Groups were required to complete a wiki project by the end of the semester. In order to complete the wiki project, students needed to participate in recurrent solo and collective phases mediated by the use of social software tools and face-to-face meetings in their respective sessions. The data for multivariate Bayesian analysis was composed of video recordings, social software usage activity and pre- and post-tests of students’ conceptual understanding. In our case, we found that using social software tools together to perform multiple tasks likely increased individual knowledge acquisition during the course. Bayesian classification analysis revealed that the best predictors of good learning outcomes were wiki-related activities. In addition, according to the Bayesian dependency model, students who monitored their peers’ work via syndication services and who were active by adding, modifying or deleting text in their group’s wiki obtained higher scores. The model also shows that many other learning activities were indirectly related to learning outcome. Laru, J., Näykki, P. & Järvelä, S. (2011). Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context. Special issue on Web 2.0 on Higher Education. Journal of Internet and Higher Education.
  9. 9. Social patterns in mobile technology mediated collaboration among members of the professional distance education community 1. What is the density and the distribution of the collaboration at individual and community levels in the online and offline communities?Questions 2. How do different social network patterns of relationships affect online and offline interactions? 3. How do participants describe teamwork and the technologies used to support it? Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners 1. What were the differences between top and low performers in regards to collaborative inquiry learning during the field trip? groups? 2. What was the difference between top and low performers in regards to the structural quality of knowledge claim messages? 3. How much did the top and low performers learn about biology during the field trip? Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context 1. How much did students learn during the course? 2. Which social software and face-to-face variables were the best predictors for identifying differences between high- and low-performing groups of students? 3. What was the impact of social software and face-to-face sessions on individual students learning gain?
  10. 10. Social patterns in mobile technology mediated collaboration among members of the professional distance education community • 1st generation: mobile versions of desktop tools:Tools FLE3mobile • wlan Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners • 2nd generation: context- aware peer-to-peer mobile tools: flyers • mobile encounter network (bluetooth) Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context • 3nd generation: mobile social media: mobile clients + flickr + wordpress + wikispaces + google reader • 3G connectivity
  11. 11. Social patterns in mobile technology mediated collaboration among members of the professional distance education community • No groups designed (participants worked in three teams though)Design ”Let’s try it” .. • No clear task, work related activities (no formal learning) • Knowledge building • Metacognitive scaffolding Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners • Dyads/Triads • Ill-structured task • Argumentative collaboration • Procedural scaffolding & metacognitive scaffolding Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context • 4-5 students per group • Ill-structured tasks • Small groups of learners were supported by multiple social software tools and face-to-face activities • Recurrent individual and collaborative phases • Multiple scaffolds
  12. 12. Social patterns in mobile technology mediated collaboration among members of the professional distance education community • Quantitative analysis of FLE3mobile’s log-filesMethods SNA Social network analysis • (log file analyzer) Qualitative-Quantitative Interview analysis (SNA analysis) Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners Mann-whitney • Quantititative Mindmap analysis (pre-post-test) • Qualitative analysis of recorded argumentative U-test • discussions (Mann-whitney U-test) Qualitative analysis of the flyers (Mann-whitney U-test) Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context • Quantitative analysis of conceptual knowledge Bayes Classification analysis • test (normalized gain, t-test) Qualitative+Quantitative analysis of social software activities (Bayesian classification Dependency modeling analysis + Bayesian dependency modeling)
  13. 13. Social patterns in mobile technology mediated collaboration among members of the professional distance education community • Overall, the analyses revealed nonparticipative behaviour within the online community. The social network analysis revealed structural holes and sparse collaboration among participants in the offline community.Results • It was found that due to their separated practices in the offline community, they did not have a need for mobile collaboration tools in their practices. Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners • Although the results revealed several shortcomings in the types of argumentation, it could be established that differences between the top performers and low performers were statistically significant in terms of social modes of argumentation, the use of warrants in the mobile tool and in overall participation. • In general, the use of the mobile tool likely promoted important interaction during inquiry learning, but led to superficial epistemological quality in the knowledge claim messages. Supporting small-group learning using multiple Web 2.0 tools: A case study in the higher education context • Explorative Bayesian classification analysis revealed that the best predictors of good learning outcomes were wiki-related activities. • In general, the results indicated that interaction between individual and collective actions likely increased individual knowledge acquisition during the course.
  14. 14. Similar • Cognitive tools; Generic cognitive tools • Mobile computer supported collaborative learning • Can be considered as example: development of ”mobile learning” (from past to today) • Design can be considered as example: learning from => learning withDifferent • Study 1 is socio-cultural (COP) while others are socio-cognitive • Methodological designs are quite different • No explicit design cycles from study 1 to study 3, instead studies are independent cases. Development cycles are in design etc.
  15. 15. Theory TheoryLearning design Learning designTechno trends Techno trendsMethodology MethodologyContext ContextTool Tool

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