Abstract
Flow is a mental state where a person is fully focused on an activity, and is enjoying performing it. Mihaly Csikszentmihalyi, who coined the concept, defines flow in terms of the skill and challenge levels of the activity as perceived by the person performing such activity. In this chapter, we propose the use of neural networks to predict if a student, after completing a computer-programming problem, is in a state of flow or not. To do so, we performed an experiment where we apply a very basic computer-programming tutorial to 21 students. We registered in a database how much time it took the students to finish the test, how many keystrokes they needed to press before achieving the goals of each exercise, how much time it took the student to start trying to solve the problem, the time between each keystroke, and how many attempts the student needed before successfully completing each exercise. Using these variables, we built a neural network that was capable of predicting if a student was in flow or not after the completion of each problem in the tutorial.
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Hernandez, A., Garcia, M., Mancilla, A. (2014). Automatic Estimation of Flow in Intelligent Tutoring Systems Using Neural Networks. In: Castillo, O., Melin, P., Pedrycz, W., Kacprzyk, J. (eds) Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Studies in Computational Intelligence, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-319-05170-3_43
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DOI: https://doi.org/10.1007/978-3-319-05170-3_43
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