東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

A Cognitively Diagnostic Modeling Ap...

Elbulok, Musa.

FindBook

Google Book

Amazon

博客來

A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills./
作者:	Elbulok, Musa.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	100 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
標題:	Quantitative psychology. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28314016
ISBN:	9798569983926

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

A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 100 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798569983926Subjects--Topical Terms:

2144748
Quantitative psychology.
Subjects--Index Terms:

Cognitive Diagnosis Models

A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills.
LDR:02813nmm a2200361 4500 001 2396337
005 20240611104225.5
006 m o d
007 cr#unu||||||||
008 251215s2021 ||||||||||||||||| ||eng d
020 $a 9798569983926
035 $a (MiAaPQ)AAI28314016
035 $a AAI28314016
040 $a MiAaPQ $c MiAaPQ
100 1 $a Elbulok, Musa. $3 3766024
245 1 2 $a A Cognitively Diagnostic Modeling Approach to Diagnosing Misconceptions and Subskills.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 100 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
500 $a Advisor: Corter, James.
502 $a Thesis (Ph.D.)--Columbia University, 2021.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 0054.
650 4 $a Quantitative psychology. $3 2144748
650 4 $a Statistics. $3 517247
653 $a Cognitive Diagnosis Models
653 $a Multiple Choice Deterministic Inputs Noisy And Gate
653 $a Misconceptions
690 $a 0632
690 $a 0463
710 2 $a Columbia University. $b TC: Measurement and Evaluation. $3 3194606
773 0 $t Dissertations Abstracts International $g 82-08B.
790 $a 0054
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28314016