Exposing students to new terminologies while collecting browsing search data (best technical paper)

Zammit, Omar, Smith, Serengul ORCID logoORCID: https://orcid.org/0000-0003-0777-5637, Windridge, David ORCID logoORCID: https://orcid.org/0000-0001-5507-8516 and De Raffaele, Clifford ORCID logoORCID: https://orcid.org/0000-0002-7081-702X (2020) Exposing students to new terminologies while collecting browsing search data (best technical paper). Proceedings Artificial Intelligence XXXVII, 40th SGAI International Conference on Artificial Intelligence (LNCS & LNAI, volume 12498). In: SGAI 2020, 15-17 Dec 2020, Cambridge, UK. ISBN 9783030637989, e-ISBN 9783030637996. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-030-63799-6_1)


Information overload is a well-known problem that generally occurs when searching for information online. To reduce this effect having prior knowledge on the domain and also a searching strategy is critical. Obtaining such qualities can be challenging for students since they are still learning about various domains and might not be familiar with the domain-specific keywords. In this paper, we are proposing a framework that aims to assist students to have a richer list of keyphrases that are pertinent to a domain under study and provide a mechanism for lectures to understand what search strategies their students are adopting. The proposed framework includes a Google Chrome Extension, a background and a remote server. The Google Chrome Extension is utilized to collect, process browsing data and generate reports containing keyphrases searched by students. The results of the user evaluation were compared with a similar framework (TextRank). The results indicate that our framework performed better in terms of accuracy of keyphrases and response time.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Part of the Lecture Notes in Computer Science book series (LNCS, volume 12498).
Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12498).
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 33256
Useful Links:
Depositing User: David Windridge
Date Deposited: 14 May 2021 08:47
Last Modified: 08 Jul 2021 22:44
URI: https://eprints.mdx.ac.uk/id/eprint/33256

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