Data Mining for Discovering Effective Time-Series Transition of Learning Strategies on Mutual Viewing-Based Learning

Access this Article

Search this Article

Author(s)

Abstract

<p>We aim to develop a real-time feedback system of learning strategies during lesson time to improve academic achievement. It has been known that mutual viewing-based learning is an effective educational method. However, even though mutual viewing is an effective lesson style, there are effective or ineffective learning strategies in the learners' individual activities. In general, the method of evaluating learning strategies is a questionnaire survey. However, the questionnaire cannot measure the learning strategies in real time. Thus, it is difficult to detect the students who use ineffective learning strategies during lesson time in real time. Recently, a system that can measure the learning strategies in real time has been developed. Using this system, it is possible to detect students who use ineffective learning strategies during lesson time on the mutual viewing-based learning. From this point of view, we aim to develop a recommendation system for real-time learning strategies for teachers and students to achieve a highly educational effect. For this purpose, we must know the features of effective or ineffective learning strategies via a system that can measure learning strategies. In this paper, we report the discovery of features of effective or ineffective learning strategies based on the data-mining approach using the <i>k</i>-means method, transition diagram, and random forest. We classified the time-series learning strategies over 40 min into 216 strategies and surveyed the improvement probability of academic achievement via a random-forest-based classification model. By embedding our results into the system, we may be able to automatically detect students who use ineffective learning strategies and recommend effective learning strategies.</p>

Journal

  • Journal of Advanced Computational Intelligence and Intelligent Informatics

    Journal of Advanced Computational Intelligence and Intelligent Informatics 22(7), 1046-1055, 2018

    Fuji Technology Press Ltd.

Codes

  • NII Article ID (NAID)
    130007520236
  • NII NACSIS-CAT ID (NCID)
    AA12042502
  • Text Lang
    ENG
  • ISSN
    1343-0130
  • NDL Article ID
    029339768
  • NDL Call No.
    Z78-A599
  • Data Source
    NDL  J-STAGE 
Page Top