DINA-BAG: A Bagging Algorithm for DINA Model Parameter Estimation in Small Samples

David Arthur and Hua-Hua Chang
Additional contact information
David Arthur: Purdue University
Hua-Hua Chang: Purdue University

Journal of Educational and Behavioral Statistics, 2024, vol. 49, issue 3, 342-367

Abstract: Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining accurate estimates of skill mastery at the population level. We introduce a novel algorithm (bagging algorithm for deterministic inputs noisy “and†gate) that is inspired by ensemble learning methods in the machine learning literature and produces more stable and accurate estimates of the population skill mastery profile distribution for small sample sizes. Using both simulated data and real data from the Examination for the Certificate of Proficiency in English, we demonstrate that the proposed method outperforms other methods on several metrics in a wide variety of scenarios.

Keywords: cognitive diagnosis; small sample; ensemble learning; DINA (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/10769986231188442 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:49:y:2024:i:3:p:342-367

DOI: 10.3102/10769986231188442

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().