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How Do Multiple-Choice Assessments Reflect Student Performance? An Educational Data Mining Analysis Using Machine Learning Classifiers
Research Article  ·  Published: 23 October 2026
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ICCK Transactions on Educational Data Mining
Volume 2, Issue 2, 2026: 85-101
Research Article Free to Read

How Do Multiple-Choice Assessments Reflect Student Performance? An Educational Data Mining Analysis Using Machine Learning Classifiers

by
Niusha Shafiabady Niusha Shafiabady 1,2 * ORCID
Darius Pfitzner Darius Pfitzner 3 ORCID
Talha Nawaz Talha Nawaz 4 ORCID
Seb Dianati Seb Dianati 5 ORCID
Mark Brady Mark Brady 6 ORCID
1 Women in AI for Social Good Lab, Discipline of Information Technology, Australian Catholic University, North Sydney, NSW 2060, Australia
2 Faculty of Science and Technology, Charles Darwin University, Sydney, NSW 2000, Australia
3 Australian Institute of Business, Adelaide, SA 5000, Australia
4 Department of Electrical Engineering-Electronics-Information Technology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91054, Germany
5 Learning and Teaching Innovation, University of Southern Queensland, Springfield, QLD 4300, Australia
6 Law, Faculty of Arts and Society, Charles Darwin University, Darwin, NT 0909, Australia
* Corresponding Author: Niusha Shafiabady, [email protected]
Volume 2, Issue 2
Volume 2, Issue 2 2026
Received: 30 June 2026 Revised: 11 August 2026 Accepted: 26 August 2026 Published: 23 October 2026 CrossMark
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DOI: 10.62762/TEDM.2026.107680
ARK: ark:/57805/tedm.2026.107680
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Article Information

Published in ICCK Transactions on Educational Data Mining
Volume/Issue Volume 2, Issue 2, 2026
Pages 85-101

Abstract

Multiple-choice questions (MCQs) are widely used in higher education because they efficiently assess knowledge and a broad range of cognitive skills across disciplines. This study examines how MCQ-based assessments contribute to predicting final student grades, using data from 2,390 students (comprising 26,698 non-missing attribute values) enrolled in sixteen offerings of three undergraduate and postgraduate economics units at Charles Darwin University (CDU) between 2016 and 2019. Six machine learning classifiers were trained on assessment marks, assessment weightings, final examination performance, and the proportion of MCQs in the examination. Feature importance analysis was then used to quantify the contribution of each assessment component. The study compares MCQ and task-based examination questions, online MCQ and task-based assessments, and online and written MCQ components. Support Vector Machines achieved the highest accuracy (96.07%), followed by XGBoost (93.76%). Across the higher-performing classifiers, final examination performance and the task-based assessment were the strongest predictors of final grades. Online and written MCQ components contributed less strongly, although MCQs remain useful indicators of foundational knowledge. These findings support balanced assessment designs that combine MCQs with task-based activities, and they demonstrate how explainable AI can inform assessment analytics within Educational Data Mining.

Graphical Abstract

How Do Multiple-Choice Assessments Reflect Student Performance? An Educational Data Mining Analysis Using Machine Learning Classifiers

Keywords

Educational data mining learning analytics multiple-choice questions student performance prediction assessment analytics artificial intelligence machine learning explainable AI

Data Availability Statement

The de-identified data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to institutional ethics approval and privacy requirements.

Funding

This work was supported without any funding.

Conflicts of Interest

Darius Pfitzner is affiliated with the Australian Institute of Business, Adelaide, SA 5000, Australia. The authors declare that this affiliation had no influence on the study design, data collection, analysis, interpretation, or the decision to publish, and that no other competing interests exist.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

The Charles Darwin University Human Research Ethics Committee determined that the secondary analysis of the de-identified records was exempt from further ethical review in accordance with institutional guidelines for research involving de-identified data.

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Cite This Article

APA Style
Shafiabady, N., Pfitzner, D., Nawaz, T., Dianati, S., & Brady, M. (2026). How Do Multiple-Choice Assessments Reflect Student Performance? An Educational Data Mining Analysis Using Machine Learning Classifiers. ICCK Transactions on Educational Data Mining, 2(2), 85-101. https://doi.org/10.62762/TEDM.2026.107680
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TY  - JOUR
AU  - Shafiabady, Niusha
AU  - Pfitzner, Darius
AU  - Nawaz, Talha
AU  - Dianati, Seb
AU  - Brady, Mark
PY  - 2026
DA  - 2026/10/23
TI  - How Do Multiple-Choice Assessments Reflect Student Performance? An Educational Data Mining Analysis Using Machine Learning Classifiers
JO  - ICCK Transactions on Educational Data Mining
T2  - ICCK Transactions on Educational Data Mining
JF  - ICCK Transactions on Educational Data Mining
VL  - 2
IS  - 2
SP  - 85
EP  - 101
DO  - 10.62762/TEDM.2026.107680
UR  - https://www.icck.org/article/abs/TEDM.2026.107680
KW  - Educational data mining
KW  - learning analytics
KW  - multiple-choice questions
KW  - student performance prediction
KW  - assessment analytics
KW  - artificial intelligence
KW  - machine learning
KW  - explainable AI
AB  - Multiple-choice questions (MCQs) are widely used in higher education because they efficiently assess knowledge and a broad range of cognitive skills across disciplines. This study examines how MCQ-based assessments contribute to predicting final student grades, using data from 2,390 students (comprising 26,698 non-missing attribute values) enrolled in sixteen offerings of three undergraduate and postgraduate economics units at Charles Darwin University (CDU) between 2016 and 2019. Six machine learning classifiers were trained on assessment marks, assessment weightings, final examination performance, and the proportion of MCQs in the examination. Feature importance analysis was then used to quantify the contribution of each assessment component. The study compares MCQ and task-based examination questions, online MCQ and task-based assessments, and online and written MCQ components. Support Vector Machines achieved the highest accuracy (96.07%), followed by XGBoost (93.76%). Across the higher-performing classifiers, final examination performance and the task-based assessment were the strongest predictors of final grades. Online and written MCQ components contributed less strongly, although MCQs remain useful indicators of foundational knowledge. These findings support balanced assessment designs that combine MCQs with task-based activities, and they demonstrate how explainable AI can inform assessment analytics within Educational Data Mining.
SN  - 3070-5843
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Shafiabady2026How,
  author = {Niusha Shafiabady and Darius Pfitzner and Talha Nawaz and Seb Dianati and Mark Brady},
  title = {How Do Multiple-Choice Assessments Reflect Student Performance? An Educational Data Mining Analysis Using Machine Learning Classifiers},
  journal = {ICCK Transactions on Educational Data Mining},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {85-101},
  doi = {10.62762/TEDM.2026.107680},
  url = {https://www.icck.org/article/abs/TEDM.2026.107680},
  abstract = {Multiple-choice questions (MCQs) are widely used in higher education because they efficiently assess knowledge and a broad range of cognitive skills across disciplines. This study examines how MCQ-based assessments contribute to predicting final student grades, using data from 2,390 students (comprising 26,698 non-missing attribute values) enrolled in sixteen offerings of three undergraduate and postgraduate economics units at Charles Darwin University (CDU) between 2016 and 2019. Six machine learning classifiers were trained on assessment marks, assessment weightings, final examination performance, and the proportion of MCQs in the examination. Feature importance analysis was then used to quantify the contribution of each assessment component. The study compares MCQ and task-based examination questions, online MCQ and task-based assessments, and online and written MCQ components. Support Vector Machines achieved the highest accuracy (96.07\%), followed by XGBoost (93.76\%). Across the higher-performing classifiers, final examination performance and the task-based assessment were the strongest predictors of final grades. Online and written MCQ components contributed less strongly, although MCQs remain useful indicators of foundational knowledge. These findings support balanced assessment designs that combine MCQs with task-based activities, and they demonstrate how explainable AI can inform assessment analytics within Educational Data Mining.},
  keywords = {Educational data mining, learning analytics, multiple-choice questions, student performance prediction, assessment analytics, artificial intelligence, machine learning, explainable AI},
  issn = {3070-5843},
  publisher = {Institute of Central Computation and Knowledge}
}

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