Panorama of Recommender Systems to Support Learning

Hendrik Drachsler; Katrien Verbert; Olga C. Santos; Nikos Manouselis

doi:10.1007/978-1-4899-7637-6_12

Panorama of Recommender Systems to Support Learning

Hendrik Drachsler, Katrien Verbert, Olga C. Santos, Nikos Manouselis

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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Abstract

This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.

Original language	English
Title of host publication	Recommender Systems Handbook
Editors	Francesco Ricci, Lior Rokach, Bracha Shapira
Place of Publication	Boston, MA
Publisher	Springer
Pages	421-451
Number of pages	31
Edition	2
ISBN (Electronic)	978-1-4899-7637-6
ISBN (Print)	978-1--4899-7636-9
DOIs	https://doi.org/10.1007/978-1-4899-7637-6_12
Publication status	Published - 14 Dec 2015

Keywords

recommender systems
learning
Technology enhanced learning
Classification framework
State-of-the-art
review
Educational datasets
Learning Analytics
Educational data mining
Trend analysis
Personalisation
Future challenges

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10.1007/978-1-4899-7637-6_12

Book chapterAccepted author manuscript, 651 KBLicence: CC BY-NC-ND

Cite this

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AB - This chapter presents an analysis of recommender systems in TechnologyEnhanced Learning along their 15 years existence (2000-2014). All recommender systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework. The reviewed systems have been classified into 7 clusters according to their characteristics and analysed for their contribution to the evolution of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.

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KW - learning

KW - Technology enhanced learning

KW - Classification framework

KW - State-of-the-art

KW - review

KW - Educational datasets

KW - Learning Analytics

KW - Educational data mining

KW - Trend analysis

KW - Personalisation

KW - Future challenges

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A2 - Rokach, Lior

A2 - Shapira, Bracha

PB - Springer

CY - Boston, MA

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Panorama of Recommender Systems to Support Learning

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