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2021 | OriginalPaper | Chapter

Condensed Discriminative Question Set for Reliable Exam Score Prediction

Authors : Jung Hoon Kim, Jineon Baek, Chanyou Hwang, Chan Bae, Juneyoung Park

Published in: Artificial Intelligence in Education

Publisher: Springer International Publishing

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Abstract

The inevitable shift towards online learning due to the emergence of the COVID-19 pandemic triggered a strong need to assess students using shorter exams whilst ensuring reliability. This study explores a data-centric approach that utilizes feature importance to select a discriminative subset of questions from the original exam. Furthermore, the discriminative question subset’s ability to approximate the students exam scores is evaluated by measuring the prediction accuracy and by quantifying the error interval of the prediction. The approach was evaluated using two real-world exam datasets of the Scholastic Aptitude Test (SAT) and Exame Nacional do Ensino Médio (ENEM) exams, which consist of student response data and the corresponding the exam scores. The evaluation was conducted against randomized question subsets of sizes 10, 20, 30 and 50. The results show that our method estimates the full scores more accurately than a baseline model in most question sizes while maintaining a reasonable error interval. The encouraging evidence found in this paper provides support for the strong potential of the on-going study to provide a data-centric approach for exam size reduction.

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Metadata
Title
Condensed Discriminative Question Set for Reliable Exam Score Prediction
Authors
Jung Hoon Kim
Jineon Baek
Chanyou Hwang
Chan Bae
Juneyoung Park
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-78270-2_79

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