Enhancing student clustering to generate adaptive metacognitive instructions in learning system for vocational high school
by Indriana Hidayah; Teguh Bharata Adji; Noor Akhmad Setiawan; Norliza Abd Rahman
International Journal of Innovation and Learning (IJIL), Vol. 24, No. 4, 2018

Abstract: Adaptivity in learning systems depends on accuracy of learner modelling. Specifically, for generating cluster-based instructions, quality of student clustering is critical. Studies on student clustering are abundant; however, a system for clustering metacognitive that considers proper analysis technique of Likert-scaled dataset is unavailable. This article proposes a student clustering method which uses a new Likert scale analysis. It is performed on a dataset collected from 81 students of a vocational high school. The performance was compared to previous methods; enhancement is shown by higher silhouette-index and strong correlation with work readiness score. To evaluate the clustering effectiveness, it is implemented on an e-learning system to generate adaptive instructions. The e-learning system is a supplement for fundamental programming course in the school. The t-test result shows that learning gain of experiment group is significantly higher that of the control group. Therefore, the proposed method is effective in improving students' learning quality.

Online publication date: Wed, 03-Oct-2018

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