A software and technology solution for taking into account students' cognitive abilities in academic performance prediction systems

Oleg S. Zhigalov

Abstract


Educational information systems at universities include modules for collecting academic performance and attendance data, enabling their use in forecasting using machine learning methods. Results from psychological testing and cognitive ability analysis can be an important addition to this data set, as stress tolerance, motivation, and working memory significantly influence final academic performance. A design for an educational system with integrated psychological testing tools has been developed, designed for use in predicting academic performance. The data collection system is implemented as a battery of tests, each of which is presented as a web interface. A key feature of the psychological tests is the need to measure both the results themselves and reaction times. This can be implemented as web pages, with page refreshes after each response and local storage of results. A technology stack implementing verified online testing tools has been selected. The proposed solution is based on an approach that preloads materials and sends results upon completion of the entire psychological test. The result of this work is a software and technology solution designed to incorporate cognitive ability testing results to expand the feature space when constructing machine learning models for predicting student academic performance. This work has practical implications for developing the capabilities of university educational information systems.

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References


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