Benchmarking and tuning regression algorithms on predicting students’academic performance

Owen H.T. Lu, Anna Y.Q. Huang, Stephen J.H. Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

With the adaption of online learning environment, students’ learning behavior can be recorded as digital data. In order to implement the conceptual framework of learning analytics, many researchers applied machine learning methodologies and used data which collected from digital learning environment to predict students’ academic performance for targeting at-risk population. However, along with the characteristic of machine learning methodologies, it presents diversity prediction performance due to the statistical property of educational data and these caused the difficulty to applied machine learning technology to classroom. In this study, we collected the state-of-the-art on regression algorithms and used an E-book-based learning dataset within 53 students for benchmarking the suitable algorithm for targeting at-risk students. In addition, we address the issues from learning environment, including over-concentration score, dropout students and data instance insufficiently, for improving prediction performance. The results revealed that the proposed performance tuning process could obtain optimal performance metrics and avoid over-fitting problem.

Original languageEnglish
Title of host publicationICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings
EditorsLung-Hsiang Wong, Michelle Banawan, Niwat Srisawasdi, Jie Chi Yang, Ma. Mercedes T. Rodrigo, Maiga Chang, Ying-Tien Wu
PublisherAsia-Pacific Society for Computers in Education
Pages477-486
Number of pages10
ISBN (Electronic)9789869721424
StatePublished - 24 Nov 2018
Event26th International Conference on Computers in Education, ICCE 2018 - Metro Manila, Philippines
Duration: 26 Nov 201830 Nov 2018

Publication series

NameICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings

Conference

Conference26th International Conference on Computers in Education, ICCE 2018
Country/TerritoryPhilippines
CityMetro Manila
Period26/11/1830/11/18

Keywords

  • At-risk Student Identification
  • Learning Analytics
  • Regression

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