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Erschienen in:
Measuring Non-compositionality of Verb-Noun Collocations Using Lexical Functions and WordNet Hypernyms Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

A Comparative Framework to Evaluate Recommender Systems in Technology Enhanced Learning: a Case Study

verfasst von : Matteo Lombardi, Alessandro Marani

Erschienen in: Advances in Artificial Intelligence and Its Applications

Verlag: Springer International Publishing

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Abstract

When proposing a novel recommender system, one difficult part is its evaluation. Especially in Technology Enhanced Learning (TEL), this phase is critical because those systems influence students or educators in educational tasks. Our research aims to propose a framework for conducting comparative experiments of different recommender systems in a same educational context. The framework is expected to provide the accuracy of subject systems within a single experiment, depicting the benefits of a novel system against others. We also present an application of such framework for a comparative experiment of popular systems in TEL like Google, Slideshare, Youtube, MERLOT, Connexions and ARIADNE. Our results show that the proposed framework has been effective in comparing the accuracy of those systems, with a clear picture of their performance compared one another. Moreover, the results of the experiment can be used as a benchmark when evaluating novel recommender systems in TEL.

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Vorheriges Kapitel NLP Methodology as Guidance and Verification of the Data Mining of Survey ENSANUT 2012
Nächstes Kapitel An Affective and Cognitive Tutoring System for Learning Programming
Fußnoten
5
http://​cnx.​org/​ contents accessed on 12/05/2015.
 
7
IEEE 1484.12.1-2002, IEEE standard for learning object metadata.
 
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Metadaten
Titel
A Comparative Framework to Evaluate Recommender Systems in Technology Enhanced Learning: a Case Study
verfasst von
Matteo Lombardi
Alessandro Marani
Copyright-Jahr
2015
Buch
Advances in Artificial Intelligence and Its Applications

Print ISBN: 978-3-319-27100-2

Electronic ISBN: 978-3-319-27101-9

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-27101-9_11