The world of distance education is constantly expanding, enriching itself with tools and services to increase the ability to provide training content. Due to the new technologies, the training paths take on new appealing features; however, it remains complex to suggest the appropriate training path to the right student. In this scenario, the use of Recommender Systems (RSs) could be helpful. RSs could allow recommending personalized learning paths to students in order to improve their abilities and their knowledge. In particular, among Recommender Systems, some of them consider contextual information. This paper aims to describe a new approach that suggests learning paths to users taking advantage of recommendation techniques and introducing them through multimedia content. Moreover, the proposed approach aims to provide recommendations when ratings are unknown through the knowledge of profiles of users and items. The proposed approach has been tested through students of two courses with diverse characteristics.

An Adaptive Learning Path Builder based on a Context Aware Recommender System

Colace F.;Lombardi M.;Marongiu F.;Santaniello D.;Valentino C.
2021

Abstract

The world of distance education is constantly expanding, enriching itself with tools and services to increase the ability to provide training content. Due to the new technologies, the training paths take on new appealing features; however, it remains complex to suggest the appropriate training path to the right student. In this scenario, the use of Recommender Systems (RSs) could be helpful. RSs could allow recommending personalized learning paths to students in order to improve their abilities and their knowledge. In particular, among Recommender Systems, some of them consider contextual information. This paper aims to describe a new approach that suggests learning paths to users taking advantage of recommendation techniques and introducing them through multimedia content. Moreover, the proposed approach aims to provide recommendations when ratings are unknown through the knowledge of profiles of users and items. The proposed approach has been tested through students of two courses with diverse characteristics.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4777584
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