Beyond Scores : A Novel Method for Predicting Student Performance Based on Rank and Positional Embedding

Han WEI, Zongxi LI*, Haoran XIE, Kevin HUNG, Minhong WANG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

Abstract

Predicting student performance based on continuous assessment scores is an important task in educational data mining (EDM). The task aims to provide personalized feedback and intervention for students at risk of failing the course or dropping out. In this paper, we examine the limitations of using raw scores as input features for predicting student outcomes, as raw scores can be biased and sensitive to the variation of assignment difficulty. We propose a novel approach that uses rank instead of score as the input feature for predicting student performance. Rank is a relative measure that reflects the performance of a student compared to other students in the same course or cohort, which can reduce bias caused by different score distributions and assignment difficulties, and capture the relative position and improvement of each student. In this paper, we use the idea of positional embedding to encode rank into a dense representation for prediction models. Positional encoding assigns a vector representation to each rank based on its ordinal position in a sequence. We apply positional encoding to various recurrent neural network (RNN) models and other machine learning models, such as logistic regression and support vector machines. We compare their performance on public datasets of student assessment scores. Our results show that using rank with positional encoding can significantly improve the prediction accuracy and reliability of RNN models and their variants.

Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE International Conference on Behavioural and Social Computing, BESC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350395884
DOIs
Publication statusE-pub ahead of print - 17 Jan 2024
Event10th IEEE International Conference on Behavioural and Social Computing, BESC 2023 - Larnaca, Cyprus
Duration: 30 Oct 20231 Nov 2023

Publication series

NameProceedings of the 2023 IEEE International Conference on Behavioural and Social Computing, BESC 2023

Conference

Conference10th IEEE International Conference on Behavioural and Social Computing, BESC 2023
Country/TerritoryCyprus
CityLarnaca
Period30/10/231/11/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • educational data mining
  • machine learning
  • positional encoding
  • rank
  • recurrent neural network
  • student performance prediction

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