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Recommending Personalized Video Lecture Augmentations with Tagged Community Question Answers.

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Bibliographic Details
Title: Recommending Personalized Video Lecture Augmentations with Tagged Community Question Answers.
Authors: Ghosh, Krishnendu
Source: International Journal of Artificial Intelligence in Education (Springer Science & Business Media B.V.); Dec2025, Vol. 35 Issue 4, p1999-2046, 48p
Subject Terms: DIGITAL learning, EDUCATIONAL technology, EDUCATIONAL films, EDUCATIONAL resources, USER interfaces, WEB personalization, EFFECTIVE teaching, AUTODIDACTICISM
Abstract: Regardless of their domains, level, or expertise, students consider video lectures one of the most popular learning media while engaged in self-study sessions on any e-learning platform. In the absence of experts/teachers in a self-study session, students often need to browse the Internet to avail themselves of additional information on the relevant topics. Hence, it would be helpful for such motivated students if we augment the video lectures with such supplementary references. In this article, we present a video lecture augmentation system leveraging question-answer (QA) pairs offering supplementary references on the course-relevant concepts. We also designed a user interface to present these augmented video lectures categorically so that the students can readily opt for the augmentations of their choice. While we qualitatively surveyed the personalization of the augmentations and usability aspects of the user interface, we quantitatively evaluated our proposed video lecture augmentation system in terms of the performances of two primary underlying modules: augmentation retrieval and tag recommendation. We quantified the pedagogical effectiveness of the augmentations following an equivalent pretest-posttest setup. All these experiments indicate that the proposed augmentations are relevant and pedagogically effective, the categorical representation helps the students choose the necessary resources readily, and the designed interface is easy to use. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Regardless of their domains, level, or expertise, students consider video lectures one of the most popular learning media while engaged in self-study sessions on any e-learning platform. In the absence of experts/teachers in a self-study session, students often need to browse the Internet to avail themselves of additional information on the relevant topics. Hence, it would be helpful for such motivated students if we augment the video lectures with such supplementary references. In this article, we present a video lecture augmentation system leveraging question-answer (QA) pairs offering supplementary references on the course-relevant concepts. We also designed a user interface to present these augmented video lectures categorically so that the students can readily opt for the augmentations of their choice. While we qualitatively surveyed the personalization of the augmentations and usability aspects of the user interface, we quantitatively evaluated our proposed video lecture augmentation system in terms of the performances of two primary underlying modules: augmentation retrieval and tag recommendation. We quantified the pedagogical effectiveness of the augmentations following an equivalent pretest-posttest setup. All these experiments indicate that the proposed augmentations are relevant and pedagogically effective, the categorical representation helps the students choose the necessary resources readily, and the designed interface is easy to use. [ABSTRACT FROM AUTHOR]
ISSN:15604292
DOI:10.1007/s40593-025-00459-9