Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Intelligent Diagnosis and Dynamic Differentiated Instruction for High-School Mathematics Concepts Based on APOS Theory - Model Development, Implementation Pathway, And Validation Plan

Xi Yang, Wenzhuo Zhang

Abstract

In high-school mathematics classrooms, conceptual understanding can usually be inferred only indirectly from students' answers, explanations, and transfer performance. A total score or a single correct-or-incorrect response rarely shows whether a student's difficulty lies in an externally guided action that has not yet been interiorized, an inability to view a process as a whole, or weak connections among concepts. To address this diagnostic problem, the study adopts a theoretical-construction and design-research orientation and proposes an intelligent diagnosis and dynamic differentiated-instruction framework grounded in APOS genetic decomposition. The framework first specifies observable evidence of Action, Process, Object, and Schema constructions for a particular concept. A Q-matrix then organizes answers, solution steps, representational transitions, verbal explanations, revision traces, and transfer tasks. Expert rubrics, cognitive diagnosis, and sequential updating are combined to produce profiles with explicit confidence estimates. Instructional groups are formed around students' current constructional difficulties and reconsidered after short task cycles, so that one assessment does not harden into a general ability label. Using the high-school function concept as an example, the paper presents a task blueprint, scoring anchors, and a classroom implementation process, together with validation requirements for construct validity, classification accuracy, calibration, fairness, and teacher usability. The framework is not intended to replace teacher judgment. Its value lies in turning APOS mental constructions into discussable classroom evidence while defining boundaries for low- confidence review and responsible data use.

Keywords

APOS theory; high-school mathematics; concept instruction; intelligent diagnosis; dynamic differentiated instruction; formative assessment IJASMT E- ISSN 2489-009X1 Introduction

References

[1] Ministry of Education of the People's Republic of China. General High School Mathematics Curriculum Standards (2017 Edition, Revised 2020)[S]. Beijing: People's Education Press, 2020. [in Chinese] [2] Qiao L Q. APOS: A constructivist theory of mathematics learning[J]. Global Education Outlook, 2001(3):16-18. [in Chinese] [3] Pu A S, Shi N Z. High-school students' understanding of the function concept from the perspective of APOS theory[J]. Journal of Mathematics Education, 2007, 16(2):48-50. [in Chinese] [4] Dubinsky E, McDonald M A. APOS: A constructivist theory of learning in undergraduate mathematics education research[C]//Holton D. The Teaching and Learning of Mathematics at University Level. Dordrecht: Kluwer Academic Publishers, 2001:275-282. [5] Arnon I, Cottrill J, Dubinsky E, et al. APOS Theory: A Framework for Research and Curriculum Development in Mathematics Education[M]. New York: Springer, 2014. [6] Breidenbach D, Dubinsky E, Hawks J, et al. Development of the process conception of function[J]. Educational Studies in Mathematics, 1992, 23(3):247-285. [7] Jiang C L, Hu L. An experimental study of teaching translations of quadratic-function graphs based on APOS theory and the RMI principle[J]. Journal of Mathematics Education, 2020, 29(6):32-39. [in Chinese] [8] VanLehn K. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems[J]. Educational Psychologist, 2011, 46(4):197-221. [9] de la Torre J. The generalized DINA model framework[J]. Psychometrika, 2011, 76(2):179-199. [10] Wang F, Liu Q, Chen E, et al. Neural cognitive diagnosis for intelligent education systems[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(4):6153-6161. [11] Piech C, Bassen J, Huang J, et al. Deep knowledge tracing[C]//Advances in Neural Information Processing Systems 28. 2015. [12] Bloom B S. Learning for mastery[J]. Evaluation Comment, 1968, 1(2):1-12. [13] Tomlinson C A. The Differentiated Classroom: Responding to the Needs of All Learners[M]. 2nd ed. Alexandria, VA: ASCD, 2014. [14] Black P, Wiliam D. Assessment and classroom learning[J]. Assessment in Education: Principles, Policy & Practice, 1998, 5(1):7-74. [15] Miao F, Holmes W. Guidance for Generative AI in Education and Research[R/OL]. Paris: UNESCO, 2023[2026-08-24]. [16] Ministry of Education of the People's Republic of China. Release of Artificial Intelligence Education Ethics: A Reference Framework[EB/OL]. (2026-05-13)[2026-08- 24]. [in Chinese] [17] Corbett A T, Anderson J R. Knowledge tracing: Modeling the acquisition of procedural knowledge[J]. User Modeling and User-Adapted Interaction, 1995, 4(4):253-278. DOI:10.1007/BF01099821. IJASMT E- ISSN 2489-009X ,

More Articles from INTERNATIONAL JOURNAL OF APPLIED SCIENCES AND MATHEMATICAL THEORY