Explainable AI for assessment design optimization in undergraduate education
DOI:
https://doi.org/10.61286/e-rms.v4i.381Keywords:
explainable artificial intelligence, machine learning, education, assessment design, undergraduate education, learning analytics, curriculum optimization, educational data mining.Abstract
Assessment design plays a pivotal role in shaping undergraduate learning outcomes, but often the choice of assessment structure and weighting is made following the pedagogical intuition rather than research data. Although machine learning has gained significant use in the higher education sector to forecast the performance of students, insufficient effort has gone into showing how the elements of assessment per se affect academic performance. This paper proposes an assessment-centric explainable artificial intelligence (XAI) framework for analyzing and optimizing assessment design in undergraduate education. Real course assessment data in the form of assessment weights, frequency and diversity machine learning models are trained to predict course outcomes and SHapley Additive exPlanations (SHAP) are used to measure the contribution of individual assessment components. Unlike existing student-centric explainability approaches, the proposed framework focuses on assessment structures, enabling transparent analysis of how design choices affect success and failure risk. According to the outcomes of the experiment, high stakes final exams are strongly related with the probability of failures, whereas the diversified strategies of continuous evaluation decrease the probability of failures. The outcomes provide a feasible information to the curriculum developers and academic decision-makers that could help them to redesign assessment based on evidence.
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