🤖 AI Summary
This study addresses the pedagogical narrowing, technical limitations, and compliance challenges associated with fully or partially digitized summative assessments in large-scale higher education examinations. To reconcile the instructional value of open-ended, problem-oriented questions with the need for scalable grading, the authors propose a hybrid e-assessment approach that retains paper-based handwritten responses within a structured answer format. This method leverages a two-stage verification pipeline: first, visual large language models and handwriting recognition technologies automatically extract handwritten content from standardized response tables; second, the extracted answers undergo semantic comparison against reference answer keys. The proposed framework preserves the educational benefits of open-response items while substantially reducing recognition errors, thereby enhancing the accuracy, fairness, and scalability of summative assessment in higher education.
📝 Abstract
This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education. The analysis focuses on the didactic narrowing caused by closed question formats and on organizational, technical, and legal constraints that become particularly relevant in large student cohorts. As an alternative, the paper proposes a hybrid e-assessment approach that retains paper-based, problem-oriented examination tasks while enabling semi-automated grading. Assessment-relevant intermediate results are encoded in a structured answer format, entered by students by hand, and subsequently captured from table fields. The central technical bottleneck is reliable recognition of handwritten characters under realistic examination conditions. Recent vision-capable large language models, combined with a two-pass validation principle and comparison against a solution key, can reduce misclassifications and thereby improve the validity, fairness, and scalability of summative assessment.