Incremental Knowledge-based System for Recommending Content Adaptation in Dynamic Learning Environment

Abstract

Dynamic learning environment (DLE) provides an opportunity for students with a remarkable learning experience in a limited time and specific situations. In this condition, adaptation and personalization have been key issues to accommodate differences between students. Both paradigms emphasize tailoring learning activities to students’ understanding and interest through learning objectives, instructional approaches, and learning pathways. In addition, the students will learn optimized instructional activities at their own pace. This paper presents an incremental knowledge-based system to facilitate learning content adaptation in DLE. To be specific, the knowledge base contains a set of rules incrementally constructed using Ripple Down Rules (RDR) after evaluating a series of test cases. The test cases are generated automatically by analyzing the attributes that reflect the learning situation. Since it is impossible to perform thorough testing involving all input parameters, the selection criteria using pairwise testing are applied to minimize the refinement. Therefore, the evaluation of the theoretical concept is then carried out on a real case. The selected case study for the analysis is the subject of Computer Networking for an undergraduate course. Several adaptive scenarios are presented based on some criteria. An education expert is involved in recommending suitable content for adaptation during the evaluation phase. However, the knowledge base development is automatically constructed from the incremental knowledge acquisition process. As the evaluation progresses, the knowledge base is validated for its accuracy in predicting learning content recommendations.