Abstract
The early years of a child’s life greatly affects education potential and furthermore potential of educational achievements in adulthood. The brain develops faster during early age, and missed cognitive opportunities in that period are difficult to make up for. In this research, we have developed a machine learning model based on the data points obtained from the educational game, with aim to predict how many attempts are necessary for an individual child to complete the task or assessment as part of educational game. In-game assessments are based on the skills that the child already possess and those developed while playing the game. Training of the machine learning model is based on collected and processed data points (features), while model interconnections are related to the factors of the child’s cognitive growing up process. Model performance benchmarks are elaborated in results and conclusion section of the paper as quality measures of the forecast indicators.
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Tolic, A., Mrsic, L., Jerkovic, H. (2021). Learning Success Prediction Model for Early Age Children Using Educational Games and Advanced Data Analytics. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_61
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DOI: https://doi.org/10.1007/978-3-030-68154-8_61
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