Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching

Zainab R. Alhalalmeh (), Yasser M. Fouda, Muhammad A. Rushdi and Moawwad El-Mikkawy
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Zainab R. Alhalalmeh: Mathematics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
Yasser M. Fouda: Mathematics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
Muhammad A. Rushdi: Faculty of Engineering, Cairo University, Cairo 12613, Egypt
Moawwad El-Mikkawy: Mathematics Department, Faculty of Science, Mansoura University, Mansoura 35516, Egypt

Sustainability, 2023, vol. 15, issue 19, 1-22

Abstract: This research addressed the need to enhance template-matching performance in e-learning and automated assessments within Egypt’s evolving educational landscape, marked by the importance of e-learning during the COVID-19 pandemic. Despite the widespread adoption of e-learning, robust template-matching feedback mechanisms should still be developed for personalization, engagement, and learning outcomes. This study augmented the conventional best-buddies similarity (BBS) approach with four feature descriptors, Harris, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and maximally stable extremal regions (MSER), to enhance template-matching performance in e-learning. We systematically selected algorithms, integrated them into enhanced BBS schemes, and assessed their effectiveness against a baseline BBS approach using challenging data samples. A systematic algorithm selection process involving multiple reviewers was employed. Chosen algorithms were integrated into enhanced BBS schemes and rigorously evaluated. The results showed that the proposed schemes exhibited enhanced template-matching performance, suggesting potential improvements in personalization, engagement, and learning outcomes. Further, the study highlights the importance of robust template-matching feedback in e-learning, offering insights into improving educational quality. The findings enrich e-learning experiences, suggesting avenues for refining e-learning platforms and positively impacting the Egyptian education sector.

Keywords: machine learning; template matching; assessment automation; e-learning; feature extraction; computer vision; image processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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