Predicting grades

Yannick Meier, Jie Xu, Onur Atan, Mihaela Van Der Schaar

Research output: Contribution to journalArticle

58 Scopus citations

Abstract

To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on maximizing the accuracy of the prediction while overseeing the importance of issuing timely and personalized predictions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past seven years. We demonstrate that for 85% of the students we can predict with 76% accuracy whether they are going do well or poorly in the class after the fourth course week. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.

Original languageEnglish (US)
Article number7313031
Pages (from-to)959-972
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume64
Issue number4
DOIs
StatePublished - Feb 15 2016
Externally publishedYes

Keywords

  • Forecasting algorithms
  • data mining
  • digital signal processing education
  • grade prediction
  • online learning

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Meier, Y., Xu, J., Atan, O., & Van Der Schaar, M. (2016). Predicting grades. IEEE Transactions on Signal Processing, 64(4), 959-972. [7313031]. https://doi.org/10.1109/TSP.2015.2496278