Impact of teachers’ grading policy on the identification of at-risk students in learning analytics

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

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The purpose of learning analytics is to promote student success in the classroom. To implement the framework of learning analytics, researchers have adopted machine learning methodologies to identify at-risk students at an early stage. In theory, machine learning is a mathematical algorithm that improves automation through experience. The experience is the data collected from online learning platforms, and in general, the data contain various features such as the number of times that a student accesses the learning material each week. Relevant studies have demonstrated extremely high accuracy in identifying at-risk students using identification models trained by machine learning. However, numerous details and data challenges have been overlooked in prior studies, calling into question the accuracy of past contributions. In this study, we focused on one type of data challenge: data imbalance. The data imbalance problems in education are usually the result of teachers’ grading policy. To highlight the seriousness of this issue, we collected data from 12 blended learning courses and summarized 3 types of grading policies: discrimination, stringency, and leniency. We then provided evidence that the leniency strategy causes the illusion of high accuracy of at-risk student identification. Finally, we verified a robust method to address the effectiveness of the leniency strategy, and using these results, we summarized the characteristics of students who tend to be misidentified by machine learning methodology.

Original languageEnglish
Article number104109
JournalComputers and Education
Volume163
DOIs
StatePublished - Apr 2021

Keywords

  • At-risk students
  • Data imbalance
  • Grading policy
  • Learning analytics

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