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A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills./
Author:	Elbulok, Musa.
Description:	1 online resource (100 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
Subject:	Quantitative psychology. -
Online resource:	click for full text (PQDT)
ISBN:	9798569983926

A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills.
Elbulok, Musa.

A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills. - 1 online resource (100 pages)

Source: Dissertations Abstracts International, Volume: 82-08, Section: B.

Thesis (Ph.D.)--Columbia University, 2021.

Includes bibliographical references

The objective of the present project was to propose a new methodology for measuring misconceptions and subskills simultaneously using diagnostic information available from incorrect alternatives in multiple-choice tests designed for that purpose. Misconceptions are systematic and persistent errors that represent a learned intentional incorrect response (Brown & VanLehn, 1980; Ozkan & Ozkan, 2012). In prior research, Lee and Corter (2011) found that classification accuracy for their Bayesian Network misconception diagnosis models improved when latent higher-order subskills and specific wrong answers were included. Here, these contributions are adapted to a cognitively diagnostic measurement approach using the multiple-choice Deterministic Inputs Noisy "And" Gate (MC-DINA) model, first developed by de la Torre (2009b), by specifying dependencies between attributes to measure latent misconceptions and subskills simultaneously. A simulation study was conducted employing the proposed methodology (referred to as MC-DINA-H) across sample sizes (500, 1000, 2,000, and 5,000 examinees) and test lengths (15, 30, and 60 items) conditions. Eight attributes (4 misconceptions and 4 subskills) were included in the main simulation study. Attribute classification accuracy of the MC-DINA-H was compared to four less complex models and was found to more accurately classify attributes only when the attributes were relatively frequently required by multiple-choice options in the diagnostic assessment. The findings suggest that each attribute should be required by at least 15-20 percent of options in the diagnostic assessment.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798569983926Subjects--Topical Terms:

1182802
Quantitative psychology.
Subjects--Index Terms:

Cognitive Diagnosis ModelsIndex Terms--Genre/Form:

554714
Electronic books.

A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills.
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520 $a The objective of the present project was to propose a new methodology for measuring misconceptions and subskills simultaneously using diagnostic information available from incorrect alternatives in multiple-choice tests designed for that purpose. Misconceptions are systematic and persistent errors that represent a learned intentional incorrect response (Brown & VanLehn, 1980; Ozkan & Ozkan, 2012). In prior research, Lee and Corter (2011) found that classification accuracy for their Bayesian Network misconception diagnosis models improved when latent higher-order subskills and specific wrong answers were included. Here, these contributions are adapted to a cognitively diagnostic measurement approach using the multiple-choice Deterministic Inputs Noisy "And" Gate (MC-DINA) model, first developed by de la Torre (2009b), by specifying dependencies between attributes to measure latent misconceptions and subskills simultaneously. A simulation study was conducted employing the proposed methodology (referred to as MC-DINA-H) across sample sizes (500, 1000, 2,000, and 5,000 examinees) and test lengths (15, 30, and 60 items) conditions. Eight attributes (4 misconceptions and 4 subskills) were included in the main simulation study. Attribute classification accuracy of the MC-DINA-H was compared to four less complex models and was found to more accurately classify attributes only when the attributes were relatively frequently required by multiple-choice options in the diagnostic assessment. The findings suggest that each attribute should be required by at least 15-20 percent of options in the diagnostic assessment.
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653 $a Misconceptions
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