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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/3298

Title: Dataset-driven research for improving recommender systems for learning
Authors: Verbert, Katrien
Drachsler, Hendrik
Manouselis, Nikos
Wolpers, Martin
Vuorikari, Riina
Duval, Erik
Keywords: dataTEL
STELLAR
AlterEgo
VOA3R
recommender systems
educational datasets
data science
Technology Enhanced Learning
experiment
collaborative filtering
TEL datasets
Issue Date: 11-Mar-2011
Abstract: In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.
Description: Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February, 27-March, 1, 2011, Banff, Alberta, Canada. http://dl.acm.org/citation.cfm?id=2090122&CFID=77368864&CFTOKEN=72282583
URI: http://hdl.handle.net/1820/3298
Appears in Collections:1. LN: Publications and Preprints

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