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  5. Module Advisor: Guiding Students with Recommendations
 
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Module Advisor: Guiding Students with Recommendations

Author(s)
Hagemann, Nina  
O'Mahony, Michael P.  
Smyth, Barry  
Uri
http://hdl.handle.net/10197/10164
Date Issued
2018-05-17
Date Available
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
Lecture Notes in Computer Science
Copyright (Published Version)
2018 Springer
Subjects

Recommender systems

Content-based filteri...

Diversity

Collaborative filteri...

Module recommendation...

Elective modules

DOI
10.1007/978-3-319-91464-0_34
Web versions
http://its2018.its-conferences.com/
Language
English
Status of Item
Peer reviewed
Journal
Lecture Notes in Computer Science (LNCS, volume 10858)
Conference Details
The 14th International Conference (ITS 2018), Montreal, Canada, 11-15 June 2018
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

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454.3 KB

Format

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Checksum (MD5)

dae95bdb50d253a58719e8f6d533de2c

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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