Related Experiment Videos
Bayesian cognitive diagnosis optimizes personalized learning paths via mediation of cognitive load and Hidden Markov
1School of Art and Design, Guangzhou College of Commerce, Guangzhou, Guangdong, China.
Abstract:
Personalized learning path optimization based on cognitive diagnosis models faces two key gaps: the sparsity of large-scale educational data makes it difficult for traditional DINA models to converge, and the psychological mechanism underlying the effectiveness of personalized paths lacks empirical testing. To address these gaps, this study proposes and validates a personalized learning path optimization framework that integrates Bayesian cognitive diagnosis, knowledge space theory, and cognitive load theory. The study employed a mixed-data design: in the first phase, a Bayesian DINA model was trained on the EdNet public dataset (N = 5,000) and a shortest remediation path algorithm was developed; in the second phase, a dataset with 120 students was used to validate the algorithm's effectiveness, using the Bootstrap method to test the mediating effect of cognitive load and introducing a Hidden Markov Model to analyze the dynamic evolution of knowledge states. The results showed that the Bayesian DINA model converged successfully on data with 91.3% sparsity (R-hat < 1.01), with attribute mastery probability estimates ranging from 0.280 to 0.368; the personalized path improved efficiency by 23.6% compared to the fixed-order path (Cohen's d = 0.98), which was cross-validated as a 22.0% time saving in a separate experiment involving probability learning; cognitive load was identified as the primary mediator between personalized paths and learning outcomes [indirect effect = 0.28, 95% CI (0.19, 0.37)], with learning motivation [indirect effect = 0.18, 95% CI (0.11, 0.25)] and self-efficacy [indirect effect = 0.12, 95% CI (0.06, 0.18)] serving as complementary mediators in a multiple mediation model. The three pathways together explained 86.6% of the total effect; the HMM identified A5 (Analytical Thinking) as a learning bottleneck, with a forward transition probability of only 0.31. This study provides a feasible methodological solution for cognitive diagnosis with sparse data, theoretically identifies the mediating pathway of cognitive load, and offers precise targets for bottleneck attribute intervention in intelligent tutoring systems in practice.
Related Concept Videos
01:21Cognitive Learning
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
01:17Cognitivism
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process information is...
01:20Introduction to Cognitive Psychology
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
01:12Observational Learning
01:27Associative Learning
Classical conditioning, also known...
01:14Avoidance Learning and Learned Helplessness
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...