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Example distribution of Learning Elements within a 4D-Space. Legend on the right shows 4th dimension
![Different LOs following Graf et al. [17, p. 32]](profile/Jim-Haug/publication/371693299/figure/tbl1/AS:11431281184926703@1693489897271/Different-LOs-following-Graf-et-al-17-p-32_Q320.jpg)
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Intelligent learning systems provide relevant learning materials to students based on their individual pedagogical needs and preferences. However, providing personalized learning objects based on learners’ preferences, such as learning styles which are particularly important for the recommendation of learning objects, re-mains a challenge. Recommen...
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... While the generated LPs were recommendations for the students, they were free to work on the material as they saw fit. Normann et al. [17] present a more detailed description of the algorithms used and their specific workings in the HASKI system, while this paper focuses more on the process of evaluating these ideas in practice. Given the intrinsic link between ALS and personalized learning, we believe it is imperative to reflect and integrate student's perspectives in the developmental phase of such a system. ...
The aim of this paper is to describe the evaluation process and findings of an AI-based Adaptive Learning System for the Computer Science discipline at two different German universities and discuss an array of methods in regard to assessing such a system. The primary objectives have been twofold: firstly, to examine the reception of selected learning elements, which were conceptually outlined based on relevant literature, among the student body; and secondly, to investigate the efficacy of individualized adaptive learning paths. These paths were generated by employing a variety of algorithms to analyze students learning style tendencies, with a particular emphasis on adaptive navigational techniques. The used algorithms encompassed a modified version of a literature based adaptive mechanism, an Ant-Colony-Algorithm and a Genetic Algorithm, alongside a lecturer-recommended learning path for a non-adaptive comparison. While the system suggested suitable learning paths based on student data, it never forced the individuals to give up their self-directed learning. The evaluation criteria revolved around the evolution of student motivation, interest levels, and knowledge acquisition during the time they spent working in the system. The evaluation sought to facilitate comparative analyses and assess algorithmic fitness for proficient learning path generation. The methods included both quantitative and qualitative approaches to gather data, seeking to strike a balance between being student-friendly and scientifically informative. They ranged from Likert Scale self-assessments to screen and video observations with retrospective interviews. Since the purpose of adaptive learning systems is intertwined with personalized learning it seems imperative to already take the preferences and opinions of students into account while the system is still in development. This complexity underscores the challenge of evaluating such systems, as significant constraints on student choice - though simplifying evaluation - directly oppose the ethos of individualized, self-directed learning. Initial findings suggest that the underlying theoretical considerations on sequencing and structuring of learning elements are confirmed, coupled with providing adequate flexibility to meet diverse learning needs. Cross-site evaluation of the literature-based learning elements indicated a high comprehensibility and positive student ratings. While significant positive trends were observed regarding knowledge acquisition, they cannot be definitively attributed to a specific method of learning path generation. Motivation and interest analyses show no significant differences among learning path types, albeit heavily limited by sample size. Similarly, emotion measurements, though limited, hint at positive impacts from HASKI system use. Despite limitations, early indications suggest student acceptance and potential effectiveness of learning paths, highlighting the need for larger sample sizes for validation and expansion. Ensuring alignment with student needs and user-friendly design are crucial considerations.
... Lernpfade werden dabei besonders im mathematisch-naturwissenschaftlichen Bereich diskutiert (Goldman, 2020;Hillmayr et al., 2017;Schmidt, 2009 Lernenden erforscht und diskutiert (Delaunay, 2022;Ezzaim et al., 2023;Jing et al., 2023Jing et al., , 2023Kabudi et al., 2021;Kurilovas et al., 2014;Normann et al., 2023;Pfaffmann & Roth, 2022;Rahayu et al., 2023;Raj & Renumol, 2022 (Kerres & Buntins, 2020;Reichow et al., 2022). ...
Wann immer Daten über die eigene Plattform hinaus ausgetauscht werden sollen, müssen die Rahmenbedingungen festgelegt werden, unter denen dieser Austausch erfolgreich zustande kommen kann. Beim Datenaustausch spielen Metadaten eine besondere Rolle. Um nützliche Bildungsservices anbieten zu können, sind gerade im Bildungsbereich didaktische Metadaten besonders relevant. Trotz der allgemein anerkannten Relevanz finden sich jedoch wenige bis gar keine Übersichten zu diesem Thema. Das vorliegende Kompendium möchte diese Lücke schließen, indem es einen Einblick in das Thema gewährt, weiterführende Informationen präsentiert und damit als Nachschlagewerk einen Ausgangspunkt für zukünftige Entwicklungen bietet. Es richtet sich daher an Entscheider*innen, Fachexpert*innen, Entwickler*innen und alle interessierten Personen, die einen thematischen Überblick über Metadaten- und E-Learning-Standards sowie didaktische Metadaten und Metadaten mit didaktischer Funktion gewinnen möchten.
