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Cognitive load theory (CLT) has driven numerous empirical studies for over 30 years and is a major theme in many of the most cited articles published between 1988 and 2023. However, CLT articles have not been compared to other educational psychology research in terms of the research designs used and the extent to which recommendations for practice are justified. As Brady and colleagues found, a large percentage of the educational psychology articles reviewed were not experimental and yet frequently made specific recommendations from observational/correlational data. Therefore, in this review, CLT articles were examined with regard to the types of research methodology employed and whether recommendations for practice were justified. Across several educational psychology journals in 2020 and 2023, 16 articles were determined to directly test CLT. In contrast to other articles, which employed mostly observational methods, all but two of the CLT articles employed experimental or intervention designs. For the two CLT articles that were observational, recommendations for practice were not made. Reasons for the importance of experimental work are discussed.
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This content will become publicly available on October 3, 2026
Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
We prove a nonasymptotic central limit theorem (CLT) for vector-valued martingale differences using Stein’s method, and we use Poisson’s equation to extend the result to functions of Markov chains. We then show that these results can be applied to establish a nonasymptotic CLT for temporal difference learning with averaging. Funding: This work was supported by National Science Foundation [Grants CNS 23-12714, CCF 22-07547, and CNS 21-06801] and Air Force Office of Scientific Research [Grant FA9550-24-1-0002].
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- PAR ID:
- 10645139
- Publisher / Repository:
- INFORMS
- Date Published:
- Journal Name:
- Mathematics of Operations Research
- ISSN:
- 0364-765X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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