scaffolding collaborative learning with cognitive tools based on mobile computers

Scaffolding collaborative learning with cognitive tools based on

mobile computersJari Laru

University of OuluDepartment of Educational Sciences and Teacher EducationResearch Unit for Learning & Educational Technology (LET)

KTK235, SnellmaniaPo.Box 2000, 90014 University of Oulu

Methodological insights:Case study research & Design Based Research

Kurti, Arianit, Exploring the multiple dimensions of context: Implications for the design and development of innovative technology-enhanced learning environments. - Växjö : Växjö University Press, 2009. - (Acta Wexionensia ; 180/2009)

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage Publications

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 from http://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

http://www.editlib.org/p/21540

My research:Scaffolding collaborative learning with cognitive tools based on mobile computers

AIMS from past to todayThis thesis work focuses on developing and analyzing innovative ways of supporting applying the framework of distributed scaffolding for learning activities in authentic real world contexts.

In this study theoretical ideas of cognitive tools, collaborative learning and scaffolding are applied for designing light-weight mobile software and pedagogical models for learning in authentic real world contexts.

This is done in order to generate new knowledge and solutions that advance collaborative learning in mobile computer supported collaborative learning

Quick and dirty solutions

EMI ILE INTHIG

Case Iworkplace (n=10)

Case IIIUniversity (N=22)

Introduction

Earli SIG

Case IINature (N=22)

Mobile computers Everyday contexts

Scaffolding collaborative

learning with cognitive tools based on

mobile computers

Master’s programme, University, Professional Community, K-12 students, Higher Education students, Nature school

Laru, J. & Järvelä, S. (2008). Social patterns in mobile technology mediated collaboration among members of the professional distance education community. Educational Media International Journal, 45(1),17-3.

The aim of this study was to identify social patterns in mobile technology mediated collaboration among distributed members of the professional distance education community. Ten participants worked for twelve weeks designing a master’s programme in Information Sciences. The participants’ mobile technology usage activity and interview data were first analyzed to get an overview of the density and distribution of collaboration at individual and community levels. Secondly, the results of the social network analyses were interpreted to explore how different social network patterns of relationships affect online and offline interactions. Thirdly, qualitative descriptions of participant teamwork were analysed to provide practical examples and explanations. 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. It was found that due to their separated practices in the offline community, they didn’t have a need for mobile collaboration tools in their practices.

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.

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 cocreationof 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. Ingeneral, 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

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.

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

Supporting collaborative inquiry during a biology field trip with mobile peer-to-peer tools for learning: a case study with K-12 learners

Social patterns in mobile technology mediated collaboration among members of the professional distance education community




Questions1. What is the density and the distribution of the collaboration at individual and community levels in the online and offline communities?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?

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?

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?




• 1st generation: mobile versions of desktop tools: FLE3mobile

• wlan

• 2nd generation: context-aware peer-to-peer mobile tools: flyers

• mobile encounter network (bluetooth)

• 3nd generation: mobile social media: mobile clients + flickr + wordpress + wikispaces + google reader

• 3G connectivity

Tools




• Dyads/Triads• Ill-structured task• Argumentative collaboration• Procedural scaffolding & metacognitive

scaffolding

Design ”Let’s try it” ..

• No groups designed (participants worked in three teams though)

• No clear task, work related activities (no formal learning)

• Knowledge building• Metacognitive scaffolding

• 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




• Quantititative Mindmap analysis (pre-post-test)• Qualitative analysis of recorded argumentative

discussions (Mann-whitney U-test)• Qualitative analysis of the flyers (Mann-whitney

U-test)

Methods• Quantitative analysis of FLE3mobile’s log-files

(log file analyzer)• Qualitative-Quantitative Interview analysis (SNA

analysis)

• Quantitative analysis of conceptual knowledge test (normalized gain, t-test)

• Qualitative+Quantitative analysis of social software activities (Bayesian classification analysis + Bayesian dependency modeling)

SNA

U-test

Bayes

Mann-whitney

Classification analysisDependency modeling

Social network analysis




Results

• 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.

• 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.

• 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.

• 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.

Similar

Different

• 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 with

• 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.

Theory Theory

Techno trendsLearning designLearning design

Techno trends

Context Context

Tool Tool

Methodology Methodology