Inferring the Number of Attributes for the Exploratory DINA Model

Chen, Yinghan; Liu, Ying; Culpepper, Steven Andrew; Chen, Yuguo

doi:10.1007/s11336-021-09750-9

Cited by 21 publications

(10 citation statements)

References 34 publications

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“…Therefore, the interval has only one number, and the number of attributes can be uniquely determined. The small interval is a significant result, which provides useful information for further finding the true K (see, e.g., Chen et al, 2021).…”

Section: Appendix Amentioning

confidence: 99%

“…Thus, there is an urgent need for an automatic and intelligent means to construct the Q-matrix. During the past decade, construction of a mapping relationship between items and latent attributes based on examinees' response data became a field undergoing intense study (e.g., Barnes, 2010;Chen, 2017;Chen et al, 2015Chen et al, , 2018Chen et al, , 2021Chiu, 2013;Chung, 2014;Close, 2012;de la Torre, 2008;Desmarais, 2012;Desmarais et al, 2012;Desmarais & Naceur, 2013;Kang et al, 2019;Lim & Drasgow, 2017;Liu, 2016;Liu et al, 2012Liu et al, , 2013Sun et al, 2014Sun et al, , 2015Wang et al, 2020;Xiang, 2013;Xu & Shang, 2018;Yu & Cheng, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Another kind of research, which does not require a pre-specified Q-matrix, is called the unsupervised learning schema in the literature. In this schema, the Q-matrix can be obtained solely from the examinees' response data (see, e.g., Chen et al, 2015Chen et al, , 2018Chen et al, , 2021Desmarais et al, 2012;Sun et al, 2014Sun et al, , 2015.…”

Section: Introductionmentioning

confidence: 99%

“…It is assumed that an examinee must possess all required attributes to answer an item correctly. Many existing Q-matrix estimation methods have been based on the DINA model (see, e.g., Chen et al, 2015Chen et al, , 2018Chen et al, , 2021Gu & Xu, 2019;Liu et al, 2013). DINA is one of the most parsimonious models in that each item has only two parameters: slipping and guessing, denoted by s j and g j , j = 1,2,. .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data‐driven Q‐matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment

Xiong

Luo

et al. 2022

Brit J Math & Statis

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Attributes and the Q‐matrix are the central components for cognitive diagnostic assessment, and are usually defined by domain experts. However, it is challenging and time consuming for experts to specify the attributes and Q‐matrix manually. Thus, there is an urgent need for an automatic and intelligent means to address this concern. This paper presents a new data‐driven approach for learning the Q‐matrix from response data. By constructing a statistical index and a heuristic algorithm based on Boolean matrix factorization, the response matrix is decomposed into the Boolean product of the Q‐matrix and the attribute mastery patterns. The feasibility of the proposed approach is evaluated using simulated data generated under various conditions. A real data example is also presented to demonstrate the usefulness of the proposed approach.

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Section: Appendix Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data‐driven Q‐matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment

Xiong

Luo

et al. 2022

Brit J Math & Statis

View full text Add to dashboard Cite

show abstract

“…To obtain the student's learning status (student personality status), on the basis of data preprocessing and data cleaning, the DINA model [11] is used to diagnose the degree of mastery of students' knowledge points.…”

Section: Diagnosis Of Students' Knowledge Of Masterymentioning

confidence: 99%

Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining

Zhou

Wang

2021

Scientific Programming

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Aiming at the problems of traditional method of exercise recommendation precision, recall rate, long recommendation time, and poor recommendation comprehensiveness, this study proposes a personalized exercise recommendation method for English learning based on data mining. Firstly, a personalized recommendation model is designed, based on the model to preprocess the data in the Web access log, and cleaning the noise data to avoid its impact on the accuracy of the recommendation results is focused; secondly, the DINA model to diagnose the degree of mastery of students’ knowledge points is used and the students’ browsing patterns through fuzzy similar relationships are clustered; and finally, according to the clustering results, the similarity between students and the similarity between exercises are measured, and the collaborative filtering recommendation of personalized exercises for English learning is realized. The experimental results show that the exercise recommendation precision and recall rate of this method are higher, the recommendation time is shorter, and the recommendation results are comprehensive.

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A Sparse Latent Class Model for Polytomous Attributes in Cognitive Diagnostic Assessments

Culpepper

Douglas

2022

Methodology of Educational Measurement and Assessment

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Inferring the Number of Attributes for the Exploratory DINA Model

Cited by 21 publications

References 34 publications

Data‐driven Q‐matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment

Data‐driven Q‐matrix learning based on Boolean matrix factorization in cognitive diagnostic assessment

Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining

A Sparse Latent Class Model for Polytomous Attributes in Cognitive Diagnostic Assessments

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