Aggregation and Mapping of Social Media Attribute Names Extracted from Chat Conversation for Personalized E-Learning

Al-Abri, Amal; Jamoussi, Yassine; Al-Khanjari, Zuhoor; Kraïem, Naoufel

doi:10.1109/icbdsc.2019.8645567

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Cited by 2 publications

References 28 publications

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Dialogue-based Continuous Update of User Portraits

Liu

et al. 2021

2021 IEEE International Conference on Services Computing (SCC)

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Dialogue-based Continuous Update of User Portraits

Liu

et al. 2021

2021 IEEE International Conference on Services Computing (SCC)

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

Alhalalmeh,

Fouda,

Rushdi

et al. 2023

Sustainability

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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.

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Aggregation and Mapping of Social Media Attribute Names Extracted from Chat Conversation for Personalized E-Learning

Cited by 2 publications

References 28 publications

Dialogue-based Continuous Update of User Portraits

Dialogue-based Continuous Update of User Portraits

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

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