Module Advisor: Guiding Students with Recommendations

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Title: Module Advisor: Guiding Students with Recommendations
Authors: Hagemann, Nina
O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/10164
Date: 17-May-2018
Online since: 2019-04-25T11:46:18Z
Abstract: Personalised recommendations feature prominently in many aspects of our lives, from the movies we watch, to the news we read, and even the people we date. However, one area that is still relatively underdeveloped is the educational sector where recommender systems have the potential to help students to make informed choices about their learning pathways. We aim to improve the way students discover elective modules by using a hybrid recommender system that is specifically designed to help students to better explore available options. By combining notions of content-based similarity and diversity, based on structural information about the space of modules, we can improve the discoverability of long-tail options that may uniquely suit students preferences and aspirations.
Type of material: Conference Publication
Publisher: Springer
Start page: 319
End page: 325
Series/Report no.: Lecture Notes in Computer Science
Copyright (published version): 2018 Springer
Keywords: Recommender systemsContent-based filteringDiversityCollaborative filteringModule recommendationsElective modules
DOI: 10.1007/978-3-319-91464-0_34
Other versions: http://its2018.its-conferences.com/
Language: en
Status of Item: Peer reviewed
Is part of: Lecture Notes in Computer Science (LNCS, volume 10858)
Conference Details: The 14th International Conference (ITS 2018), Montreal, Canada, 11-15 June 2018
Appears in Collections:Insight Research Collection

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