Adaptive Systems

An Adaptive Mechanism for Moodle Based on Automatic Detection of Learning Styles

It was developped by Ioannis Karagiannis as part of his Ph.D (2018).

"An adaptive mechanism for Moodle based on learning styles supported by a data mining algorithm: Implementation of a Procedural Programming course", Department of Applied Informatics, University of Macedonia, January 2018. (Supervisor: Prof. Satratzemi Maya, Advisors: Prof. Dagdilelis Vasilios, Associate Prof. Kaskalis Theodor).

Available (in Greek) from: Digital Library and Institutional Repository, University of Macedonia: https://dspace.lib.uom.gr/handle/2159/21847

We propose an automatic approach that detects students’ learning styles in order to provide adaptive courses in Moodle. This approach is based on students’ responses to the ILS and the analysis of their interaction behavior within Moodle by applying a data mining technique. In conjunction to this, an adaptive mechanism that was implemented in Moodle is presented. This adaptive mechanism builds the student model based mainly on the proposed approach for automatic detection of learning styles in order to adapt the presentation and the proposed navigation to students’ different learning styles and educational objectives.

ProPer

ProPer is an integrated adaptive and adaptable learning environment that offers LMS administration facilities and conforms to SCORM specifications. It comprises a combination of an AEHS and an LMS, while it provides a variety of additional educational features both for the student and the teacher.

ProPer was develloped by Ioannis Kazanidis as part of his Ph.D. (2005-2010):

"Educational technology. Adaptive SCORM compliant web-based environment with the use of learning styles for distance education: application in the Object Oriented Programming Instruction", Department of Applied Informatics, University of Macedonia, May 2010. (Supervisor: Prof. Satratzemi Maya, Advisors: Prof. Evangelidis Georgios, Prof. Margaritis Konstantinos).

Available (in Greek) from: Digital Library and Institutional Repository, University of Macedonia: https://dspace.lib.uom.gr/handle/2159/13981

ProPer’s architecture is a combined architecture of a SCORM LMS and an AEHS. It adopts the structure of a typical SCORM Run Time Environment (RTE) with the addition of an adaptation module and the extension of the preexistent Domain and User Models. In brief, ProPer encompasses six main modules (Fig.1):

• The Domain Model (DM) represents the domain knowledge of the system.

• The User Model (UM) that represents the particular user’s knowledge of the domain as well as his/her individual characteristics. ProPer uses a multilayered overlay UM which stores three types of data: i) data about user knowledge, ii) data about user actions and goals, and iii) domain independent data.

• The Adaptation Module (AM) which interacts with the DM and UM in order to provide the system’s adaptive functionality. It incorporates a set of adaptation rules which define the way personalization is applied.

• The RTE Sequencer, which interacts with the DM and delivers the appropriate educational content to the learner.

• User Tracker (UT) which monitors the learner’s interaction with the system and stores all the essential data into the UM.

• Feedback Visualizer which initially calculates the feedback information and then visualizes the results and delivers them to the user .

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