Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Restructure Recommendation Framework for Online Learning Content using Student Feedback Analysis

온라인 학습을 위한 학생 피드백 분석 기반 콘텐츠 재구성 추천 프레임워크

  • Choi, Ja-Ryoung (Research Institute of ICT Convergence, Sookmyung Women's University) ;
  • Kim, Suin (School of Computing, KAIST) ;
  • Lim, Soon-Bum (Dept. of Information Technology Engineering, Sookmyung Women's University)
  • Received : 2018.08.16
  • Accepted : 2018.10.22
  • Published : 2018.11.30
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Abstract

With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.

Keywords

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Fig. 1. E-learning systems using student feedback.

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Fig. 2. Overview of Recommendation Framework.

Table 1. Topics extracted from quiz-related questions

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Table 2. Effect of proposed automatic updates

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Table 3. Comparison chart of system

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