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This paper explores the development of diagnostic models within intelligent educational systems, with a focus on mathematical disciplines. A comparative analysis of discrete and continuous knowledge tracing approaches is presented, highlighting their strengths in adapting to students’ cognitive profiles and uncovering latent skill dynamics. In parallel, the paper introduces a novel sense-economic model that aims to enhance student motivation by recognizing meaningful contributions to learning. Educational progress is measured not solely through correctness but through deeper indicators such as analytical reasoning, conceptual understanding, creative problem-solving, and peer support. A central component of this approach is the implementation of a unified blockchain-based token (SenseCoin), which operates within a decentralized infrastructure of validated educational values—termed “senses.” These tokens are awarded based on transparent, configurable criteria and can be exchanged for intrinsically valuable educational opportunities, such as access to premium learning resources, project-based modules, or mentorship-driven activities. The model supports a dynamic ontology of sense types, allowing flexible expansion across domains and disciplines. The paper argues that the combination of diagnostic precision and value-sensitive tokenization offers a promising direction for the future of educational systems—one grounded in personal meaning, autonomy, and integrity rather than coercion or formal compliance.

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Diagnostic Models and a Crypto-Based Sense-Economic Approach to Enhancing Motivation in Intelligent Mathematics Learning Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Andriy Chukhray,
  • Oleksandr Yevdokymov,
  • Liudmila Mandrikova,
  • Tetiana Dehtiarova,
  • Olena Havrylenko

摘要

This paper explores the development of diagnostic models within intelligent educational systems, with a focus on mathematical disciplines. A comparative analysis of discrete and continuous knowledge tracing approaches is presented, highlighting their strengths in adapting to students’ cognitive profiles and uncovering latent skill dynamics. In parallel, the paper introduces a novel sense-economic model that aims to enhance student motivation by recognizing meaningful contributions to learning. Educational progress is measured not solely through correctness but through deeper indicators such as analytical reasoning, conceptual understanding, creative problem-solving, and peer support. A central component of this approach is the implementation of a unified blockchain-based token (SenseCoin), which operates within a decentralized infrastructure of validated educational values—termed “senses.” These tokens are awarded based on transparent, configurable criteria and can be exchanged for intrinsically valuable educational opportunities, such as access to premium learning resources, project-based modules, or mentorship-driven activities. The model supports a dynamic ontology of sense types, allowing flexible expansion across domains and disciplines. The paper argues that the combination of diagnostic precision and value-sensitive tokenization offers a promising direction for the future of educational systems—one grounded in personal meaning, autonomy, and integrity rather than coercion or formal compliance.