Studying Learner’s Player Learning Style for Generating Adaptive Learning Game

Bennis, Lamyae; Kandali, Khalid; Bennis, Hamid

doi:10.1109/access.2022.3207191

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Equipping educational computer games (hereafter called educational games) with adaptivity has shown to be effective in improving learners' engagement, motivation, and learning gain in a wide range of fields (e.g., [1], [2], [3], [4], [5]). Such educational games provide customization of learning and/or gameplay to match the individual needs and interests of learners while playing.…”

Section: Introductionmentioning

confidence: 99%

Modeling Learners to Early Predict Their Performance in Educational Computer Games

et al. 2023

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Data mining approaches have proven to be successful in improving learners´interaction with educational computer games. Despite the potential of predictive modelling in providing timely adaptive learning and gameplay experience, there is a lack of research on the early prediction of learners' performance in educational games. In this research, we propose an early predictive modelling approach, called GameEPM, to estimate learners' final scores in an educational game for promoting computational thinking. Specifically, the GameEPM approach models the sequence of learners' actions and then uses a limited sequence of the actions to predict the final score of the game for each learner. The findings from our initial trials show that our approach can accurately and robustly estimate the learners' performance at the early stages of the game. Using less than 50% of learners' action sequences, the cross-validated deep learning model achieves a squared correlation higher than 0.8 with a relative error of less than 8%, outperforming a range of regression models like linear regression, random forest, neural networks, and support vector machines. An additional experiment showed that the validated deep learning model can also achieve high performance while tested on an independent game dataset, showing its applicability and robustness in real-world cases. Comparing the results with traditional machine learning methods revealed that, in the validation and application phases, up to 0.30 and 0.35 R 2 gain is achieved in favor of the deep learning model, respectively. Finally, we found that while the lengths of action sequences influence the predictive power of the traditional machine learning methods, this effect is not substantial in the deep learning model.

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Section: Introductionmentioning

confidence: 99%

Modeling Learners to Early Predict Their Performance in Educational Computer Games

et al. 2023

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Game-based Activity Design in Primary School Students’ Learning Style Detection

Fernando,

Premadasa

2024

Procedia Computer Science

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Student Learning Styles in Information Technology

Bayona-Oré

2023

Wseas Transactions on Computer Research

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Teachers employ various strategies to facilitate student learning. Each student possesses a unique way of gaining motivation and absorbing information. Achieving the best possible outcomes in the teaching-learning process involves considering the individual learning styles of each student. Among the several models available for determining learning styles, Kolb’s model stands out as widely utilized. This article aims to apply Kolb’s learning styles model to identify the preferred learning styles of 53 students enrolled in a course focused on information technologies. The sampling method used was non-probabilistic, and data collection relied on the Kolb learning style inventory. The obtained results indicate that the prevailing learning styles among the surveyed students are convergent (47.2%) and assimilative (34.0%). Familiarity with the diverse learning styles of the students can significantly assist teachers in enhancing the efficiency of the teaching-learning process.

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Studying Learner’s Player Learning Style for Generating Adaptive Learning Game

Cited by 3 publications

References 19 publications

Modeling Learners to Early Predict Their Performance in Educational Computer Games

Modeling Learners to Early Predict Their Performance in Educational Computer Games

Game-based Activity Design in Primary School Students’ Learning Style Detection

Student Learning Styles in Information Technology

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