Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2024

Abstract

Learning from concise educational materials, such as lecture notes and presentation slides, often prompts students to seek additional resources. Newcomers to a subject may struggle to find the best keywords or lack confidence in the credibility of the supplementary materials they discover. To address these problems, we introduce Slide++, an automated tool that identifies keywords from lecture slides, and uses them to search for relevant links, videos, and Q&As. This interactive website integrates the original slides with recommended resources, and further allows instructors to 'pin' the most important ones. To evaluate the effectiveness of the tool, we trialled the system in four undergraduate computing courses, and invited students to share their experiences via a survey and focus groups at the end of the term. Students shared that they found the generated links to be credible, relevant, and sufficient, and that they became more confident in their understanding of the courses. We reflect on these insights, our experience of using Slide++, and explore how Large Language Models might mitigate some augmentation challenges.

Keywords

slide augmentation, resource curation, query suggestions

Discipline

Databases and Information Systems | Instructional Media Design

Research Areas

Software and Cyber-Physical Systems

Areas of Excellence

Digital transformation

Publication

Artificial Intelligence in Education: 25th International Conference, AIED 2024, Recife, Brazil, July 8-12

Volume

2151

First Page

200

Last Page

208

ISBN

9783031643125

Identifier

10.1007/978-3-031-64312-5_24

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-031-64312-5_24

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