Collaborative recommendations in virtual learning environments.
Recommender systems are software applications that aim to suggest meaningful and useful items to users chen interacting with large volumes of data such as online multimedia content, news, products, among others. These systems generate personalized recommendations of items based on the explicit or im...
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| Main Author: | |
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| Format: | masterThesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/20.500.12008/22855 |
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| Summary: | Recommender systems are software applications that aim to suggest meaningful and useful items to users chen interacting with large volumes of data such as online multimedia content, news, products, among others. These systems generate personalized recommendations of items based on the explicit or implicit preferences expressed by users, as well as information about their collaborations and relationships. there are numerous fields of application for these systems such as e-commerce, social networking, online streaming, among others, playing a key role in virtual learning environments. Recommender systems assist students in finding appropriate educational items, enhancing their learning experience and academia results. Two previous research projects (QHIR LACCIR and UTU) with limited scope, provided experience in different aspects of the development of recommendation algorithms which process information about socially-connected users and their collaborations. Based on those positive results obtained, this thesis proposes mechanisms to generate personalized recommendations to students through the development of a complete recommender system, using information about their collaborations in virtual learning environments. The aim is to describe and specify in detail wow different approaches, techniques, and recommendation algorithms are used to produce meaningful recommendations in real life educational scenarios. More specifically, it focuses on personalized recommendations for medical doctors and other health specialists enrolled in online Continuing Medical Education (CME) courses Iliroiigli a virtual learning environment end RedEMC. A general conclusion based on user feedback given by students, is that collaborative recommendations enhance each students’ learning process and user’s experience, stimulating further collaboration. It is worth to highlight that explicit feedback given by RedEMC’s users stated that more than 90% of the recommendations of resources/activities and comments were rated positively. |
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