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In adaptive learning, comprehensive support through methodologies, adaptation mechanisms, and digital twins of the learning process is essential. This paper presents a study of evaluation systems for adaptive learning and testing, assessing relevance to the target criteria of knowledge and competencies of the learners. A systematic analysis was employed, along with methods of mathematical statistics and probabilistic modeling, and parameter identification of the adaptive learning process in accordance with the principles of the SMART paradigm. The paper demonstrates the adaptation of the online testing process within the SMART concept. A quantitative probabilistic assessment of the testing of achieved competencies and the identification of parameters of the learning process based on maximum likelihood estimation was conducted. An iterative process of hierarchical adaptation is shown. Directions for future evolution of research are indicated. The results are applicable in practical adaptive testing and knowledge assessment in practice. #COMESYSO1120.

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Statistical Evaluation and Testing of the Effectiveness of Adaptive E-Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Irina Yarygina,
  • Aleksey Losev,
  • Shokhida Irgasheva,
  • Natalia Bystrova,
  • Irina Nikolaeva

摘要

In adaptive learning, comprehensive support through methodologies, adaptation mechanisms, and digital twins of the learning process is essential. This paper presents a study of evaluation systems for adaptive learning and testing, assessing relevance to the target criteria of knowledge and competencies of the learners. A systematic analysis was employed, along with methods of mathematical statistics and probabilistic modeling, and parameter identification of the adaptive learning process in accordance with the principles of the SMART paradigm. The paper demonstrates the adaptation of the online testing process within the SMART concept. A quantitative probabilistic assessment of the testing of achieved competencies and the identification of parameters of the learning process based on maximum likelihood estimation was conducted. An iterative process of hierarchical adaptation is shown. Directions for future evolution of research are indicated. The results are applicable in practical adaptive testing and knowledge assessment in practice. #COMESYSO1120.