Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/617
Title: | A hybrid recommender system integrated into LAMS for learning designers |
Authors: | Karga, Soultana Satratzemi, Maya |
Type: | Article |
Subjects: | FRASCATI::Social sciences FRASCATI::Engineering and technology |
Keywords: | Learning design recommender systems social tagging technology enhanced learning |
Issue Date: | 2018 |
Publisher: | Springer US |
Source: | Education and Information Technologies |
Volume: | 23 |
Issue: | 3 |
Last Page: | 1297 |
Volume Title: | 1329 |
Abstract: | In the constantly evolving field of e-learning, the Learning Design (LD) sector constitutes a critical success factor, as it has the potential to preserve and disseminate effective pedagogical approaches and enhance the quality of the educational process. Recognizing the LD process as demanding in terms of time and expertise this paper answers the research question of how to leverage Recommender Systems (RSs) and reuse pre-existing LD solutions in order to support teachers in the LD process. In particular, this paper presents the implementation and the first evaluation results of Mentor. Mentor is an RS that supports teachers in finding pre-existing LDs, which cater better for their needs and preferences, so as to re-design them. Mentor is integrated into LAMS, which is a well-known tool for designing, managing and delivering sequences of learning activities. The first user-centric evaluation experiment results are presented and confirm the underlying assumption that Mentor can facilitate teachers in the LD process. Further results concerning the user’s general perception and the perceived usefulness of Mentor are discussed. |
URI: | https://doi.org/10.1007/s10639-017-9668-0 https://ruomo.lib.uom.gr/handle/7000/617 |
ISSN: | 1360-2357 1573-7608 |
Other Identifiers: | 10.1007/s10639-017-9668-0 |
Appears in Collections: | Department of Applied Informatics |
Files in This Item:
File | Description | Size | Format | |
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Manuscript-Karga-SatratzemiEAIT-accepted.pdf | 936,48 kB | Adobe PDF | View/Open |
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