Enhancing student clustering to generate adaptive metacognitive instructions in learning system for vocational high school. (2018)

• Record Type: Journal Article
• Authors: Hidayah, Indriana; Adji, Teguh Bharata; Setiawan, Noor Akhmad; Rahman, Norliza Abd
• Is Part Of: International journal of innovation and learning. Volume 24:Number 4(2018)
• Page Start: 419
• Page End: 436
• Publication Date: 2018
• Subjects: student clustering -- Likert scale analysis -- learning system -- adaptive metacognitive instructions -- vocational high school; Knowledge management -- Periodicals; Learning -- Periodicals; Diffusion of innovations -- Periodicals; 006.33
• Languages: English
• ISSNs: 1471-8197

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.