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    PublicationAccess status: Metadata only ,
    Context based learning: a survey of contextual indicators for personalized and adaptive learning recommendations – a pedagogical and technical perspective
    (2023)
    Abu-Rasheed, Hasanorcid-logo
    ;
    Weber, Christianorcid-logo
    ;
    Fathi, Madjidorcid-logo
    Learning personalization has proven its effectiveness in enhancing learner performance. Therefore, modern digital learning platforms have been increasingly depending on recommendation systems to offer learners personalized suggestions of learning materials. Learners can utilize those recommendations to acquire certain skills for the labor market or for their formal education. Personalization can be based on several factors, such as personal preference, social connections or learning context. In an educational environment, the learning context plays an important role in generating sound recommendations, which not only fulfill the preferences of the learner, but also correspond to the pedagogical goals of the learning process. This is because a learning context describes the actual situation of the learner at the moment of requesting a learning recommendation. It provides information about the learner’s current state of knowledge, goal orientation, motivation, needs, available time, and other factors that reflect their status and may influence how learning recommendations are perceived and utilized. Context-aware recommender systems have the potential to reflect the logic that a learning expert may follow in recommending materials to students with respect to their status and needs. During the last decade, several approaches have emerged in the literature to define the learning context and the factors that may capture it. Those approaches led to different definitions of contextualized learner-profiles. In this paper, we review the state-of-the-art approaches for defining a user’s learning-context. We provide an overview of the definitions available, as well as the different factors that are considered when defining a context. Moreover, we further investigate the links between those factors and their pedagogical foundations in learning theories. We aim to provide a comprehensive understanding of contextualized learning from both pedagogical and technical points of view. By combining those two viewpoints, we aim to bridge a gap between both domains, in terms of contextualizing learning recommendations.
    Source Type:Article
    DOI:10.3389/feduc.2023.1210968
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    PublicationAccess status: Open Access ,
    Integrating Learning Context and Explainability in Educational Recommender Systems Using Markov Decision Process over Knowledge Graphs
    (2025)
    Abu-Rasheed, Hasanorcid-logo
    Human learning is a complex and multi-dimensional process, governed by a wide range of factors that describe the individual differences between learners, and the contexts in which learning happens. Since these differences influence how learners respond to the learning content and activities, it is essential for an effective learning process to consider these factors when generating learning recommendations. Context-aware recommender systems (CARS) have offered a promising solution for tailoring learning experiences to the specific learning contexts. However, existing CARS often fall short in comprehensively integrating complex contextual data into their reasoning. Moreover, complex CARS face challenges in providing transparent and explainable recommendations to learners and educators, especially when they have no technical background, which hinders CARS’s effectiveness and acceptance among these educational stakeholders. This thesis addresses these challenges by developing a novel method for building Context-Aware Recommendations and Explainability through Knowledge Graphs (CARExKG). Structurally, the CARExKG method employs knowledge graphs to represent contextual learning factors and their interdependencies, capturing the dynamic interplay between various contextual variables in complex learning settings. Algorithmically, CARExKG employs a Markov decision process over knowledge graphs, featuring a context-sensitive reward function tailored to enable the RS to generate contextualized learning paths across various learning settings. In order to ensure human oversight, reduce stakeholders’ resistance to recommendations, and encourage collaborative human-AI decision-making, CARExKG further incorporates an explainability framework, utilizing large language models and expert-input from pedagogy specialists, to generate user-centric explanations, supporting learner’s understanding of the reasoning behind generating the recommended path, and enhancing their decision-making ability and ownership of their educational journey. To evaluate the proposed method, a set of experiments was designed to measure the effectiveness of the knowledge graphs, the recommendation algorithm, and the explainability framework. CARExKG is then evaluated as a complete system through a real-world user study with nursing staff in two elderly homes, where a complex learning scenario was constructed to mimic the multi-dimensional challenges they face in their profession. Evaluation results have demonstrated the effectiveness of the proposed approach for constructing the knowledge graph and the reasoning of the RS. Results also show the ability of the CARExKG method to improve learner satisfaction and outcomes in vocational education and training settings. Experiment findings underscore the importance of the interdisciplinary approach followed in designing CARExKG, which combines artificial intelligence, educational technology, and pedagogy to create adaptive, explainable, and learner-centered educational tools.
    Source Type:Doctoral Thesis
    DOI:10.25819/ubsi/10782
      4  4
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    PublicationAccess status: Open Access ,
    Recommendersysteme in der beruflichen Weiterbildung. Grundlagen, Herausforderungen und Handlungsempfehlungen. Ein Dossier im Rahmen des INVITE-Wettbewerbs
    (2022)
    Reichow, Insaorcid-logo
    ;
    Buntins, Katjaorcid-logo
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    Paaßen, Benjamin
    ;
    Abu-Rasheed, Hasanorcid-logo
    ;
    Weber, Christianorcid-logo
    Das vorliegende Dossier erläutert zunächst, was Recommendersysteme sind und wie sie technisch umgesetzt werden. Nachfolgend wird aufgezeigt, zu welchem Zweck Recommendersysteme beim technologiegestützten Lernen eingesetzt werden – sowohl im Bildungsbereich allgemein als auch speziell in der beruflichen Weiterbildung. Der größere Teil dieses Dossiers widmet sich anschließend spezifischen Herausforderungen der Entwicklung und Implementierung konkreter Recommendersysteme auf digitalen Weiterbildungsplattformen. Dabei werden basierend auf der bestehenden Literatur und Aussagen von Expert_innen Handlungsempfehlungen aufgeführt. Insgesamt soll das vorliegende Dossier damit den Einsatz von Recommendersystemen in der beruflichen Aus- und Weiterbildung sowohl aus technischer als auch didaktischer Perspektive beleuchten.
    Source Type:Other
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