Exposing students to new terminologies while collecting browsing search data (best technical paper)
Zammit, Omar, Smith, Serengul ORCID: https://orcid.org/0000-0003-0777-5637, Windridge, David
ORCID: https://orcid.org/0000-0001-5507-8516 and De Raffaele, Clifford
ORCID: 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)
Abstract
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) |
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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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