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Mining Educational Data to Predict Students' Performance through Procrastination Behavior.

Danial Hooshyar1, Margus Pedaste1, Yeongwook Yang1

  • 1Institute of Education, University of Tartu, Tartu 50103, Estonia.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a new algorithm to predict student performance in online learning by analyzing assignment submission behavior. The algorithm accurately identifies procrastination patterns, aiding educators in supporting students with learning difficulties.

Keywords:
educational data mininghigher educationonline learningpredication of students’ performanceprocrastination behavior

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Area of Science:

  • * Educational Technology
  • * Learning Analytics
  • * Artificial Intelligence in Education

Background:

  • * Student procrastination significantly impacts online learning performance.
  • * Early identification of procrastination is crucial for academic success.
  • * Existing methods often overlook pre-submission behavioral patterns.

Purpose of the Study:

  • * To propose a novel algorithm (PPP) for predicting student performance based on procrastination behavior.
  • * To analyze students' assignment submission patterns beyond simple late or non-submissions.
  • * To identify students with learning difficulties through their procrastination tendencies.

Main Methods:

  • * Development of a novel algorithm (PPP) using student assignment submission behavior.
  • * Feature vector construction representing submission patterns before assignment due dates.
  • * Application of clustering for student categorization (procrastinator, candidate, non-procrastinator).
  • * Comparison of various classification methods (Linear SVM, Neural Network) for performance prediction.

Main Results:

  • * The PPP algorithm achieved 96% accuracy in predicting student performance.
  • * Linear SVM excelled with continuous features, while Neural Networks performed better with categorical features.
  • * Categorical features generally outperformed continuous features in prediction.
  • * Increased cluster numbers negatively impacted the predictive power of classification methods.

Conclusions:

  • * The PPP algorithm effectively predicts student performance by analyzing procrastination behaviors.
  • * Understanding pre-submission assignment patterns offers valuable insights into student engagement and potential difficulties.
  • * The findings support the integration of learning analytics tools for personalized educational support.