A taxonomy for user models in adaptive systems: special considerations for learning environments

Medina-Medina, Nuria; García-Cabrera, Lina

doi:10.1017/s0269888916000035

Cited by 8 publications

(1 citation statement)

References 47 publications

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“…In order to provide each learner with the most accurate learning path and material, an AR system was developed (namely PARSAT), which determines their knowledge level and their learning needs. This is accomplished through the student model, which, nowadays, is present in the majority of most adaptive educational software [49].…”

Section: Fuzzy Weightsmentioning

confidence: 99%

Personalization of the Learning Path within an Augmented Reality Spatial Ability Training Application Based on Fuzzy Weights

Papakostas

Troussas

Krouska

et al. 2022

Sensors

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Adaptive systems and Augmented Reality are among the most promising technologies in teaching and learning processes, as they can be an effective tool for training engineering students’ spatial skills. Prior work has investigated the integration of AR technology in engineering education, and more specifically, in spatial ability training. However, the modeling of user knowledge in order to personalize the training has been neither sufficiently explored nor exploited in this task. There is a lot of space for research in this area. In this work, we introduce a novel personalization of the learning path within an AR spatial ability training application. The aim of the research is the integration of Augmented Reality, specifically in engineering evaluation and fuzzy logic technology. During one academic semester, three engineering undergraduate courses related to the domain of spatial skills were supported by a developed adaptive training system named PARSAT. Using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of the Structure of the Observed Learning Outcomes for the design of the learning material, PARSAT offers adaptive learning activities for the students’ cognitive skills. Students’ data were gathered at the end of the academic semester, and a thorough analysis was delivered. The findings demonstrated that the proposed training method outperformed the traditional method that lacked adaptability, in terms of domain expertise and learning theories, considerably enhancing student learning outcomes.

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Section: Fuzzy Weightsmentioning

confidence: 99%