Transactions on Machine Intelligence 2821-1693 6 3 2023 10 01 A Bayesian Model and Bayesian Classification on the Data Obtained from Children's Educational Activity in the IoT Environment 126 136 181428 10.47176/TMI.2023.126 EN M. Moradi Information Technology and Computer Engineering, University of Qom, Qom, Iran 0000-0002-3124-3289 Journal Article 2022 04 17 Over the past decade, there has been a significant increase in interest and research concerning the integration of technology within educational environments. A notable outcome of incorporating such technology in early childhood education is the enhancement of children's motivation, self-confidence, and collaborative abilities. The importance of early childhood education can be examined from two perspectives: the adaptability of young minds in contemporary times and the lasting impact of education during this formative period. To assess the effectiveness of technology in education, a study utilized Bayesian modeling to analyze data collected through Internet of Things (IoT) technologies. Specifically, a classifier was developed to predict future educational outcomes for children based on their previous performance. The test data confirmed the model's high accuracy, underscoring the positive potential of technology in shaping the educational experiences of young learners. This approach aligns with broader research trends that employ Bayesian networks to predict student performance and learning behaviors. For instance, studies have demonstrated the application of Bayesian models in forecasting student outcomes and identifying at-risk students, thereby facilitating timely interventions and personalized learning strategies . Such methodologies highlight the transformative role of technology and advanced analytics in modern education, particularly in enhancing early childhood learning experiences. https://www.tmachineintelligence.ir/article_181428_13e849f87865123639269aaaae624474.pdf