Exploring Intelligent Instructional Strategies Tailored to Individual Differences

DOI

10.2991/978-94-6239-733-0_33How to use a DOI?

Keywords

Introductory Courses; Intelligent Education; Reinforcement Learning

Abstract

In current teaching practice for introductory computer science courses, the traditional lecture model, characterized by a “uniform pace and standardized output”, often struggles to accommodate individual differences arising from learners’ varied prior knowledge and cognitive rhythms. Consequently this approach frequently leads to gaps in the development of computational thinking and a decline in learning motivation. To address this teaching dilemma, this paper introduces the Markov Decision Process as a quantitative analytical framework to construct an adaptive guidance model tailored to the foundational cultivation of computational thinking. By formally modeling learners’ cognitive states, deriving instructional intervention strategies, and establishing incentive-based feedback mechanisms, this model achieves the dynamic optimization of learning pathways. This research not only provides a methodological reference for adaptive learning path planning in large-scale educational environments, but also paves the way for the in-depth reform of interdisciplinary teaching models and the development of corresponding software systems in the future.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jing Bai
PY  - 2026
DA  - 2026/07/17
TI  - Exploring Intelligent Instructional Strategies Tailored to Individual Differences
BT  - Proceedings of the 6th International Conference on Internet, Education and Information Technology (IEIT 2026)
PB  - Atlantis Press
SP  - 322
EP  - 327
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-733-0_33
DO  - 10.2991/978-94-6239-733-0_33
ID  - Bai2026
ER  -