Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs

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

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely intervention according to the results of student behavior analysis. However, when researchers applied machine learning to train a risk identifying model, the reason which affected the performance of the model was overlooked. This study collected seven datasets within three universities located in Taiwan and Japan and listed performance metrics of risk identification model after fed data into eight classification methods. U1, U2, and U3 were used to denote the three universities, which have three, two, and two cases of datasets (learning logs), respectively. According to the results of this study, the factors influencing the predictive performance of classification methods are the number of significant features, the number of categories of significant features, and Spearman correlation coefficient values. In U1 dataset case 1.3 and U2 dataset case 2.2, the numbers of significant features, numbers of categories of significant features, and Spearman correlation coefficient values for significant features were all relatively high, which is the main reason why these datasets were able to perform classification with high predictive ability.

Original languageEnglish
Pages (from-to)206-230
Number of pages25
JournalInteractive Learning Environments
Volume28
Issue number2
DOIs
StatePublished - 17 Feb 2020

Keywords

  • academic performance
  • classification methods
  • Educational big data
  • learning analytics
  • learning logs

Fingerprint

Dive into the research topics of 'Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs'. Together they form a unique fingerprint.

Cite this