Academic performance prediction using chance discovery from online discussion forums

Ka Wai Gary WONG, Simon Y. K. LI

Research output: Chapter in Book/Report/Conference proceedingChapters

8 Citations (Scopus)

Abstract

In this paper, we present our preliminary results of identifying serendipitous findings from discussion forums of students by using a text-mining analytical tool to predict their academic performances. The analytical results were visualized by constructing KeyGraphs so that teachers can assess the effectiveness of teaching and innovation of learning respectively through the visualization of hidden patterns in the online learning environment. Our results show that the serendipitous findings have shown a traceable pattern, which is statistically significant to predict the academic performance of students. The research findings can lead to adaptive pedagogical designs for teaching and learning by finding hidden patterns and linkages among the students' serendipitous learning. The identified results are expected to support both teachers and students on how to improve teaching and learning with feedbacks from this new tool. Ultimately, this creates a new approach for transformative learning and teaching in education by using the advanced mining technology to assess the students' knowledge discovery process. Copyright © 2016 by The Institute of Electrical and electronics engineers, inc.
Original languageEnglish
Title of host publicationProceedings: 2016 IEEE 40th Annual Computer Software and Applications Conference
EditorsSorel REISMAN, Sheikh Iqbal AHAMED, Ling LIU, Dejan MILOJICIC, William CLAYCOMB, Mihhail MATSKIN, Hiroyuki SATO, Motonori NAKAMURA, Stevlio CIMATO, Chung Horng LUNG, Zhiyong ZHANG
Place of PublicationLos Alamitos
PublisherIEEE Computer Society
Pages706-711
Volume2
ISBN (Print)9781467388450
DOIs
Publication statusPublished - 2016

Citation

Wong, G. K. W., & Li, S. Y. K. (2016). Academic performance prediction using chance discovery from online discussion forums. In S. Reisman, S. I. Ahamed, L. Liu, D. Milojicic, W. Claycomb, M. Matskin, H. Sato, et al. (Eds.), Proceedings: 2016 IEEE 40th Annual Computer Software and Applications Conference (Vol. 2, pp. 706-711). Los Alamitos: IEEE Computer Society.

Keywords

  • Big data
  • Educational data mining
  • Topic detection
  • Serendipitous learning
  • Learning analytics
  • Chance discovery
  • KeyGraph

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