Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field

• Published in: IEEE transactions on learning technologies Vol. 4; no. 1; pp. 5 - 20
• Main Authors: Magnisalis, Ioannis, Demetriadis, Stavros, Karakostas, Anastasios
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2011; Institute of Electrical and Electronics Engineers, Inc; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation model; Adaptive collaborative learning support; adaptive hypermedia; Adaptive systems; Artificial Intelligence; Bibliographic Databases; Bibliographies; Classification; Collaboration; collaborative learning; Collaborative work; Computational modeling; Computer Software; Computer Software Evaluation; Computer System Design; Cooperative Learning; Design engineering; Educational Research; Educational Trends; Evidence; Hypermedia; Instructional Design; Instructional Effectiveness; intelligent support systems; Intelligent systems; Intelligent Tutoring Systems; Learning; Literature Reviews; Outcome Measures; Program Evaluation; Reporting; Research Needs; Research Reports; Searching; Student Motivation; Students; Studies; Web 2.0 Technologies
• ISSN: 1939-1382; 2372-0050
• DOI: 10.1109/TLT.2011.2

This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence on the impact of these systems on student learning, and 3) identify current trends and open research questions in the field. After systematically searching online bibliographic databases, 105 articles were included in the review with 70 of them reporting concrete evaluation data on the learning impact of AICLS systems. Systems design analysis led us to propose a classification scheme with five dimensions: pedagogical objective, target of adaptation, modeling, technology, and design space. The reviewed articles indicate that AICLS systems increasingly introduce Artificial Intelligence and Web 2.0 techniques to support pretask interventions, in-task peer interactions, and learning domain-specific activities. Findings also suggest that AICLS systems may improve both learners' domain knowledge and collaboration skills. However, these benefits are subject to the learning design and the capability of AICLS to adapt and intervene in an unobtrusive way. Finally, providing peer interaction support seems to motivate students and improve collaboration and learning.