Using Electroencephalogram to Track Learner’s Reasoning in Serious Games

Ghali, Ramla; Frasson, Claude; Ouellet, Sébastien

doi:10.1007/978-3-319-39583-8_45

Cited by 2 publications

(3 citation statements)

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“…Many researchers have indicated that educational games can reach their full educational potential if they consider learners' individual needs and skills and accordingly offer real-time feedback, in-game interventions, task difficulty, and learning content adaptation (e.g., [27], [28], [29]). In this regard, Ghali et al [6] collected and processed learners' interaction with an educational game called LewiSpace for teaching drawing Lewis diagrams. In their research, the authors used personality traits and different physiological data (from different sources like Electroencephalography, eye tracker, and facial expression recognition) to predict learners' performance during gameplay.…”

Section: Related Researchmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Dongxiao Yu . achieve adaptive learning in educational games, for instance, through knowledge tracing that aims to model learners' knowledge over time and accordingly predict their learning performance (e.g., [6], [7], [8]). Prediction of learners' performance may help educators to provide learners with more appropriate learning materials, timely and personalized feedback, recommend tutorials and personalized learning paths, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Several research efforts have been carried out revolving around the prediction of learners' performance in educational games. For instance, Ghali et al [6] developed a VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

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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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