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Technology-enhanced systems and tools for collaborative learning scaffolding
Daradoumis T., Caballé S., Juan A., Xhafa F., Springer Publishing Company, Incorporated, Berlin, Germany, 2011. 328 pp. Type: Book (978-3-642198-13-7)
Date Reviewed: Sep 12 2013

Although several trends in technology-supported education are getting headlines (such as massive online open courses), perhaps the most interesting, pedagogically and technically, is computer-supported collaborative learning (CSCL), roughly two decades old but now developing more rapidly.

This volume offers 13 papers on CSCL developments. The book has four focuses: (1) frameworks and infrastructures that foster collaborative learning; (2) methodologies and tools for analyzing and managing students’ interactions; (3) models that involve collaborative ontologies, games, and adaptive collaboration support; and (4) techniques to create more powerful e-collaboration settings.

A more detailed discussion of some of the chapters follows.

In “Facebook as a Collaborative Platform in Higher Education: The Case Study of the Universitat Oberta de Catalunya,” by Fernàndez and Gil-Rodríguez, Facebook is proposed as an open learning tool in higher education, with arguments as to why social media networks have enormous potential for learning. The authors ran an experimental course, Travel 2.0, at the Universitat Oberta de Catalunya (UOC). They used an “open horizontal approach” to learning where traditional course hierarchy is minimized, and where students hold the power and direction of learning. Students tended to revert to a more traditional learning mode in which a teacher provides the course structure and facilitates participation. While interesting, the results do not directly inform the design of future courses.

“CLFP Intrinsic Constraints-Based Group Management of Blended Learning Situations,” by Perez-Sanagustín et al., discusses collaborative learning flow patterns (CLFPs), specific methods that teachers can use to increase student learning. For example, the Jigsaw pattern puts students into subgroups to study different aspects of a complex problem, and then regroups them to join subsolutions. The authors present a tool to notify teachers of violated constraints while adjusting to disruptions such as students dropping the class.

A small user study compared using the tool to manual group adjustment. The study’s design is suspect, since teachers were asked to first perform the task manually, and then use the tool afterward. This permits carryover effects, where teachers could potentially be faster with the tool simply because they practiced the task once manually beforehand.

In “A Pedagogical Approach for Collaborative Ontologies Building,” by Mangione et al., the potential of collaborative tools for the creation of ontologies is explored, and the lack of ontology authoring tools that specifically address a pedagogical context is discussed.

Top-down collaborative ontology construction scenarios are classified into three categories: parallel mode, which allows experts to edit the entire ontology simultaneously; sequential mode; and mutual mode, in which users contribute in a wiki format. In contrast, bottom-up strategies emphasize the students’ contributions to the development of the ontology. These tools were validated at the University of Bologna.

In “A Framework to Foster Collaboration Between Students Through a Computer Supported Collaborative Learning Environment,” by Bayon et al., a model called CLF for an adaptive CSCL course is presented that helps to predict student needs and to promote collaboration even in students who are initially reluctant. Perhaps most interesting in this chapter are the “collaborative indicators” computed by the system. A prototype implementation and an initial user study were done. Student groups of four wrote stories collaboratively, in three phases: individual work, sharing, and merging. Questionnaire answers by students and researchers showed that students generally thought the system encouraged collaboration, but usually disagreed with the “negative” collaboration indicators. Researchers’ manually assigned collaborative indicators often disagreed substantially with the system’s.

Kordaki and Siempos, in “A Collaborative and Adaptive Design Pattern of the Jigsaw Method Within Learning Design-Based E-learning Systems,” demonstrate how the open-source learning activity management system (LAMS) can be used to design and implement the Jigsaw collaborative learning pattern in an e-learning environment. They also show how online activities built into LAMS can be used for all six phases in the Jigsaw method in the context of a programming languages class.

Adaptation patterns are formal specifications for customizing a learning script to improve the experience of individual learners. Learning scripts represent learning activities in a theatrical play form, specified in IMS-LD (a modeling/scripting language). “Modeling Adaptation Patterns in the Context of Collaborative Learning: Case Studies of IMS-LD Based Implementation,” by Magnisalis and Demetriadis, provides three case studies in which an adaptation pattern is applied to an activity. Then, the authors discuss the problems with representing an adaptation pattern in IMS-LD, and future research. However, this paper’s main idea still needs to be tested in an actual classroom.

Overall, the formatting and copyediting of the volume are uneven. In addition, the publisher’s price for the book makes each paper about $14, which is high compared to the CSCL proceedings ($0 for more than 100 papers). Although the CSCL field is too large to be completely represented in 13 papers, the editors of this book have organized their small sample of CSCL literature nicely.

Reviewers:  S. L. TanimotoZachary AlstadCaitlin BonnarJeffrey BoothYun-En LiuTrevor PerrierRob Thompson Review #: CR141547 (1311-0978)
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Computer-Supported Cooperative Work (H.5.3 ... )
 
 
Collaborative Learning (K.3.1 ... )
 
 
Education (J.1 ... )
 
 
Learning (I.2.6 )
 
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