Student Cognitive Profile Classification and Instructional Strategy Design Based on Drift Diffusion Model
Journal: Region - Educational Research and Reviews DOI: 10.32629/rerr.v7i4.3853
Abstract
: In the current field of education, achieving personalized education has become a key pursuit. The core difficulty
lies in accurately quantifying students' cognitive differences and translating these differences into practical intervention
programs. Traditional psychological indicators often only stay at the level of describing the surface behavior of students
and are difficult to deeply explore the internal mechanisms of students' cognitive processes. The Drift Diffusion Model
(DDM), as the core mathematical model in the field of cognitive decision-making, brings new ideas for solving this
problem with its unique advantages. DDM depicts the dynamic process of evidence accumulation through a Stochastic
Differential Equation. Its parameters—drift rate ( μ ), noise intensity ( σ ), and decision threshold ( Α )—quantify
individual differences in cognitive abilities. This study extends DDM to educational contexts, establishing an end-to-end
theoretical framework: "behavioral data → DDM parameters → cognitive classification → instructional strategies." This
framework provides an actionable mathematical foundation for educational personalization. Future research may integrate
dynamic parameter estimation methods to enable real-time optimization of instructional strategies. Keywords: Drift Diffusion Model, stochastic differential equation, cognitive profile classification
Keywords
Drift Diffusion Model, stochastic differential equation, cognitive profile classification, instructional strategy design
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[1] Gluth, S., Rieskamp, J., & Buchel, C. (2012). "Deciding when to decide: Time-variant sequential sampling models
explain the emergence of value-based decisions in the human brain". Journal of Neuroscience, 32(31), 10686–10698.
[2] Myers, C. E., Interian, A., & Moustafa, A. A. (2022). "A practical introduction to using the drift diffusion model of
decision - making in cognitive psychology, neuroscience, and health sciences". Frontiers in Psychology, 13 :1039172.
[3] Ratcliff, R. (1978). "A theory of memory retrieval". Psychological Review, 85(2), 59–108.
[4] Ratcliff, R., & Tuerlinckx, F. (2002). "Estimating parameters of the diffusion model: Approaches to dealing with
contaminant reaction times and parameter variability". Psychonomic Bulletin & Review, 9(3), 438–481.
[5] Vandekerckhove, J., & Tuerlinckx, F. (2007). "Fitting the Ratcliff diffusion model to experimental data". Psychonomic Bulletin & Review, 14(6), 1011–1026.
explain the emergence of value-based decisions in the human brain". Journal of Neuroscience, 32(31), 10686–10698.
[2] Myers, C. E., Interian, A., & Moustafa, A. A. (2022). "A practical introduction to using the drift diffusion model of
decision - making in cognitive psychology, neuroscience, and health sciences". Frontiers in Psychology, 13 :1039172.
[3] Ratcliff, R. (1978). "A theory of memory retrieval". Psychological Review, 85(2), 59–108.
[4] Ratcliff, R., & Tuerlinckx, F. (2002). "Estimating parameters of the diffusion model: Approaches to dealing with
contaminant reaction times and parameter variability". Psychonomic Bulletin & Review, 9(3), 438–481.
[5] Vandekerckhove, J., & Tuerlinckx, F. (2007). "Fitting the Ratcliff diffusion model to experimental data". Psychonomic Bulletin & Review, 14(6), 1011–1026.
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