Abstract
The paper presents an exploration of the role of mobile technology for the realization of personalized learning and for improvement the students’ learning performance within an intelligent educational environment. The two of the created predictive models show the patterns and anomalies in students’ learning behavior and their learning performance. The utilized supervised machine learning algorithms: Random Forest, ID3, Naïve Bayes, Deep learning, k-NN are evaluated, and the results points out that the most suitable algorithms for solving these classification tasks are decision tree-based Random Forest and ID3. A multi-layer perceptron is used for predicting the students’ group learning performance at whole.
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Acknowledgments
This research is supported by the Bulgarian FNI fund through the project “Modeling and Research of Intelligent Educational Systems and Sensor Networks (ISOSeM)”, contract КП-06-H47/4 from 26.11.2020.
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Ivanova, M., Ivanova, T., Terzieva, V., Todorova, K. (2022). Modeling Students’ Learning Performance and Their Attitudes to Mobile Learning. In: Auer, M.E., Tsiatsos, T. (eds) New Realities, Mobile Systems and Applications. IMCL 2021. Lecture Notes in Networks and Systems, vol 411. Springer, Cham. https://doi.org/10.1007/978-3-030-96296-8_58
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