Timing the Adaptive Learning Process with Events Ontology

Homola, Martin; Kluka, Jan; Kubincová, Zuzana; Marmanová, Patrícia; Cifra, Milan

doi:10.1007/978-3-030-35758-0_1

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…To address these challenges, the concept of intelligent e-learning systems was introduced, leveraging computational intelligence in web-based education [5,6]. To meet learners' needs, intelligent e-learning systems must fulfill various requirements, including the selection of suitable learning objects (LOs) to structure courses according to each learner's style [7][8][9][10]. Indeed, the selection of LOs is a crucial step in course design and sequencing.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

Azzi,

Radouane,

Laaouina

et al. 2024

Informatics

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In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

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