Uml-alf agent based adaptive learning framework : a case study on uml

Kocabaş, Efe Cem
As the amount of accessible and shareable knowledge increases, it is figured out that learning platforms offering the same context and learning path to all users can not meet the demands of learners. This issue brings out the necessity of designing and developing adaptive hypermedia systems. This study describes an agent-based adaptive learning framework whose goal is to implement effective tutoring system with the help of Artificial Intelligence (AI) techniques and cognitive didactic methods into Adaptive Educational Hypermedia Systems (AEHS) in the domain of Unified Modeling Language (UML). There are three main goals of this study. First goal is to explore how supportive agents affect student’s learning achievement in distance learning. Second goal is to examine the interaction between supportive agents and learners with the help of experiments in Human Computer Interaction laboratories and system analysis. The effects of the methodology that agents give misleading hints which are common mistakes of other learners are also investigated. Last goal is to deliver effective feedback to students both from IAs and tutors. In order to assess that UML-ALF has accomplished its objectives, we followed an experimental procedure. Experimental groups have taken the advantage of adaptive and intelligent techniques of the UML-ALF and control groups have used the traditional learning techniques. The results show that there is a positive correlation between variables practice score and number of agent suggestion which means, as the participants benefit from supportive agents, they get higher scores.


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Citation Formats
E. C. Kocabaş, “Uml-alf agent based adaptive learning framework : a case study on uml,” M.S. - Master of Science, Middle East Technical University, 2010.