Tahani model of fuzzy database for an adaptive metacognitive scaffolding in Hypermedia Learning Environment (Case: Algorithm and structure data course)

Studies show that an adaptive learning environment is needed by learners. Adaptive e-learning systems have been developed with personalized instructions based on student's knowledge level. Since each student has a unique cognitive and metacognitive level, we currently develop an adaptive Hypermedia Learning Environment (HLE) for Algorithm and Data Structure Course based on a metacognitive awareness level. HLE provides adaptive scaffolding as instructional interventions to facilitate students' aptitude in programming. Scaffolding can be determined by knowing student's metacognitive awareness level. In this research, we propose the use of Tahani Model of Fuzzy Database in web-based HLE to support the adaptive decision-making. Tahani is one of some feasible models in a fuzzy database that can categorize students' metacognitive awareness level. The produced fuzzy rules have been validated by metacognitive experts of educational psychology. A hundred students have been categorized into three groups (low, medium, high) and provided with appropriate scaffolding. Evaluation results verified the suitability of the proposed method and the student's scaffolding.

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