Curriculum Optimization by Correlation Analysis and Its Validation
The paper introduces a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. The technology is a ”lazy” Data Mining technology, which models students’ learning characteristics by considering real data instead of deriving (”guessing”) their characteristics explicitly. It aims at mining course characteristics similarities of former students’ study traces and utilizing them to optimize curricula of current students based to their performance traits revealed by their educational history. This (compared to a former publication) refined technology generates suggestions of personalized curricula. The technology is supplemented by an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.). Finally, the paper derives some refinement ideas for the evaluation method.
Keywordsadaptive learning technologies personalized curriculum mining educational data mining
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