Measuring Domain Knowledge for Early Prediction of Student Performance: A Semantic Approach

Anupam Khan, Sourav Ghosh, Soumya K. Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The growing popularity of data mining catalyses the researchers to explore various exciting aspects of education. Early prediction of student performance is an emerging area among them. The researchers have used various predictors in performance modelling studies. Although prior cognition can affect student performance, establishing their relationship is still an open research challenge. Quantifying the knowledge from readily available data is the major challenge here. We have proposed a semantic approach for this purpose. Association mining on nearly 0.35 million observations establishes that prior cognition impacts the student performance. The proposed approach of measuring domain knowledge can help the early performance modelling studies to use it as a predictor.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering
EditorsHiroyuki Mitsuhara, Yoshiko Goda, Yutato Ohashi, Maria Mercedes T. Romero, Jun Shen, Neelakantam Venkatarayalu, Gary Wong, Masanori Yamada, Chi-Un Lei
PublisherIEEE
Pages444-451
Number of pages8
ISBN (Electronic)978-1-7281-6942-2
ISBN (Print)978-1-7281-6943-9
DOIs
Publication statusPublished - 8 Mar 2021
EventInternational Conference on Teaching, Assessment, and Learning for Engineering 2020 - Online
Duration: 8 Dec 202011 Dec 2020
http://tale2020.org/

Publication series

NameIEEE International Conference on Teaching, Assessment and Learning for Engineering
PublisherIEEE
ISSN (Print)2374-0191
ISSN (Electronic)2470-6698

Conference

ConferenceInternational Conference on Teaching, Assessment, and Learning for Engineering 2020
Abbreviated titleTALE 2020
Period8/12/2011/12/20
Internet address

Keywords / Materials (for Non-textual outputs)

  • Domain knowledge
  • student performance
  • early prediction
  • educational data mining
  • classroom learning

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