G-DINA Model Research Articles

Exploring Interrelationships Among L2 Writing Subskills: Insights from Cognitive Diagnostic Models

ABSTRACT The purpose of this study was to explore the nature of interactions among second/foreign language (L2) writing subskills. Two types of relationships were investigated: subskill–item and subskill–subskill relationships. To achieve the first purpose, using writing data obtained from the writing essays of 500 English as a foreign language (EFL) students, the fit of the G-DINA model as general cognitive diagnostic model (CDM) was compared against a Wald-selected model (i.e., a model in which different CDMs such as DINA, DINO, A-CDM, LLM, and RRUM were selected for each item). To study cross-subskill impacts, two-way interactions between the subskills were estimated, and lens and scatter plots were checked. Subskill–item relationships showed that the subskills of L2 writing contribute additively and independently to the probability of successful performance. Furthermore, the loglinear analysis revealed that some of the subskills draw information from the other subskills. The plots showed that neglecting inter-subskill relationships would lead to misclassification of students.

An exploratory Q-matrix estimation method based on sparse non-negative matrix factorization.

Cognitive diagnostic assessment (CDA) is widely used because it can provide refined diagnostic information. The Q-matrix is the basis of CDA, and can be specified by domain experts or by data-driven estimation methods based on observed response data. The data-driven Q-matrix estimation methods have become a research hotspot because of their objectivity, accuracy, and low calibration cost. However, most of the existing data-driven methods require known prior knowledge, such as initial Q-matrix, partial q-vector, or the number of attributes. Under the G-DINA model, we propose to estimate the number of attributes and Q-matrix elements simultaneously without any prior knowledge by the sparse non-negative matrix factorization (SNMF) method, which has the advantage of high scalability and universality. Simulation studies are carried out to investigate the performance of the SNMF. The results under a wide variety of simulation conditions indicate that the SNMF has good performance in the accuracy of attribute number and Q-matrix elements estimation. In addition, a set of real data is taken as an example to illustrate its application. Finally, we discuss the limitations of the current study and directions for future research.

Determining the number of attributes in the GDINA model.

Exploratory cognitive diagnosis models have been widely used in psychology, education and other fields. This paper focuses on determining the number of attributes in a widely used cognitive diagnosis model, the GDINA model. Under some conditions of cognitive diagnosis models, we prove that there exists a special structure for the covariance matrix of observed data. Due to the special structure of the covariance matrix, an estimator based on eigen-decomposition is proposed for the number of attributes for the GDINA model. The performance of the proposed estimator is verified by simulation studies. Finally, the proposed estimator is applied to two real data sets Examination for the Certificate of Proficiency in English (ECPE) and Big Five Personality (BFP).

Selecting an Appropriate Cognitive Diagnostic Model for Eighth Grade Math Questions: TIMSS 2019 Study

Background and Objective: Traditional assessment methods have been criticized for neglecting the cognitive processes required by test-takers to provide correct responses. To address these issues, cognitive diagnostic models have been introduced by researchers. This study aimed to explore and apply cognitive diagnostic models to estimate the psychometric properties of published eighth-grade math questions from TIMSS 2019. Methods and Materials: The research method employed in this study is mixed-methods, conducted with a retrofitting approach to existing tests. The statistical population of this study consisted of all Iranian eighth-grade students who participated in the TIMSS 2019 study. The population size included 1,095,026 students from 23,895 schools. The sample consisted of 5,980 Iranian eighth-grade students from 220 schools, selected through a two-stage cluster sampling method, with an average age reported as 14.1 years. Findings: Using a qualitative method, 16 skills across four content domains—knowledge, application, and reasoning—were identified and the Q-matrix was developed. Then, in the quantitative section, the fit of the DINA model as a compensatory model, the DINO model as a non-compensatory model, and the G-DINA model as a general model were examined with data from students' responses to math questions in blocks 1, 2, 3, and 5. The findings showed that in terms of model fit, the G-DINA model had the weakest fit, while the DINA model had the best fit. Absolute fit indices of the models also indicated that the DINA model was better, and the DINO model did not fit. Relative fit indices of the models showed no significant difference between the G-DINA model and the other two models, and overall, the non-compensatory DINA model demonstrated the best fit. Conclusion: The results indicated that the DINA model had the best fit with the data and can be considered the optimal model for analyzing the eighth-grade mathematics questions in TIMSS 2019.

Development of Online Calibration Method based on SCAD penalty and EM perspective in CD-CAT: G-DINA model

Development of Online Calibration Method based on SCAD penalty and EM perspective in CD-CAT: G-DINA model

The Investigation of The Differential Item Functions of The 2012 High School Entrance Exam Mathematics Test Based On The G-DINA Model

Purpose: This study aims to determine the item parameters and estimations of student attribute profiles related to the 2012 8th Grade HSEE Mathematics subtest item responses using the G-DINA model and examine whether the items show DIF according to the gender variable.
 Design/Methodology/Approach: The study was conducted on the data of 1.063.570 students who took the 2012 HSEE Mathematics subtest. The analysis was carried out on the target population data to avoid sampling error. This study is a descriptive, survey type study.
 Findings: When the model fit indices and comparison results were examined, it was concluded that the model that best explained the behavior of students responding to the items in the 20-item form of the 2012 HSEE Mathematics subtest was G- DINA. When the G-DINA model parameters were examined, it was found that the 𝛼! = [0000] attribute profile (with 61%) in which none of the four defined attributes were found in the student, and the 𝛼!" = [1111] attribute profile (with 17%) in which all four attributes were present in the student (17%) were the most common attribute profiles. As a result of DIF analysis within the scope of CDM, and it was identified that item 4 showed a significant uniform DIF in favor of female students while item 19 showed a uniform DIF in favor of male students at moderate level.
 Highlights: It is thought that DIF analyses within the framework of cognitive diagnostic models can provide a statistical basis for item bias decisions.

A General Mixture Model for Cognitive Diagnosis

Although the generalized deterministic inputs, noisy “and” gate model (G-DINA; de la Torre, 2011) is a general cognitive diagnosis model (CDM), it does not account for the heterogeneity that is rooted from the existing latent groups in the population of examinees. To address this, this study proposes the mixture G-DINA model, a CDM that incorporates the G-DINA model within the finite mixture modeling framework. An expectation–maximization algorithm is developed to estimate the mixture G-DINA model. To determine the viability of the proposed model, an extensive simulation study is conducted to examine the parameter recovery performance, model fit, and correct classification rates. Responses to a reading comprehension assessment were analyzed to further demonstrate the capability of the proposed model.

Research on the innovation of teaching model of college board tennis based on GDINA model

Abstract With the slogan of national fitness and lifelong sports, more and more people are involved in sports activities, able to have one or two sports interests, stick to them for a long time, and develop lifelong exercise habits. With the emergence of the new sport of collegiate cricket, it has attracted more people to participate in it with its unique sporting appeal. High school sports, as the leader of national fitness, can play a good role as a catalyst for the development of board tennis in China because of the specificity of its group. In order to promote the reform of college physical education public curriculum, so that board tennis can also be a good enrichment of the sound college physical education public sports program. Based on the database and the G-DINA model, this paper uses bibliometric methods to analyze the current state of board tennis research in several dimensions. The cognitive diagnostic G-DINA model was applied to analyze tests, quantify students’ cognitive structure, and apply it to assist teachers in remedial instruction. The final results show that the research on collegiate netball, as an international emerging program, is currently in its initial development phase, and the research is “extensive” but not focused. It is also recommended to improve the quantity and quality of literature research, focus on the construction of scientific research teams; broaden the research perspective, and construct a horizontal and vertical theoretical research methodological system from 3 levels: microscopic, medium and macro.

Tourism architecture design strategy in tourism area planning based on G-DINA model

Abstract Tourism architecture is an important material guarantee for carrying out tourism activities in scenic spots and is an important part of the tourism system. This paper analyzes the theory and structure of the G-DINA model in detail. To conceptualize the geospatial tourism architecture, form a conceptual cognition, and establish a basic geospatial tourism architecture cognitive model. Then the G-DINA model is brought into the basic cognitive model, and cognitive judgments are made using observation of tourism building features to form a G-DINA-based geospatial cognitive model of tourism buildings for planning and design of scenic tourism buildings. Experimental proof: By comparing the number of tourists in a tourist area for two months, it is obvious that the number of tourists in the second month has increased by 592 compared with the number of tourists in the first month, which is 160% of the number of tourists in the first month, and the comprehensive index of tourism economy has increased from 0.08 to 0.5, with an annual growth of 8.3%, showing a gradual upward trend in general. Accordingly, it can be considered that the attractiveness and competitiveness of tourist areas are significantly improved through the rational planning and design of tourist buildings. This verifies that reasonable design of tourism architecture and scientific planning of tourism areas can strengthen the harmony between tourism architecture and local style and surrounding environment, highlight regional characteristics and let them be inherited, and promote tourism development.

Optimization of college physical education curriculum based on G-DINA

Abstract Physical education curriculum in colleges and universities has been paid more and more attention and turned one of the important contents of education curriculum. In order to make up for the deficiency that traditional tests can only get a general score and further optimize the physical education curriculum, this study selected G-DINA model according to Wald statistic, analyzed the physical education curriculum textbooks, and clarified the cognitive attributes and hierarchical relations of the curriculum. Then, pursuant to the attributes and hierarchical relations obtained, this study constructed typical assessment model matrix and developed the curriculum cognitive diagnosis test paper based on Q matrix. Through comparing results of the two physical education curriculum diagnosis test papers based on G-DINA model, it can be seen that the ratio of students who master the attributes A4 and A5 is climbed up to 67.2% and 59%, respectively, which indicates that students’ overall mastery of A4 and A5 knowledge blocks has been significantly improved after teachers’ intensive and in-depth optimized teaching. Therefore, it can be concluded from the above that the optimized teaching of physical education courses in colleges and universities plays a significant role, and at the same time, for teachers, G-DINA model is conductive to fully controlling the teaching feedback effect, timely adjusting the teaching key and difficult points, and predicting the depth and breadth of teaching trend. Even further, the model is helpful for college physical education institutes to deepen the physical education curriculum optimization in colleges and universities.

Using country-specific Q-matrices for cognitive diagnostic assessments with international large-scale data

In cognitive diagnosis assessment (CDA), the impact of misspecified item-attribute relations (or “Q-matrix”) designed by subject-matter experts has been a great challenge to real-world applications. This study examined parameter estimation of the CDA with the expert-designed Q-matrix and two refined Q-matrices for international large-scale data. Specifically, the G-DINA model was used to analyze TIMSS data for Grade 8 for five selected countries separately; and the need of a refined Q-matrix specific to the country was investigated. The results suggested that the two refined Q-matrices fitted the data better than the expert-designed Q-matrix, and the stepwise validation method performed better than the nonparametric classification method, resulting in a substantively different classification of students in attribute mastery patterns and different item parameter estimates. The results confirmed that the use of country-specific Q-matrices based on the G-DINA model led to a better fit compared to a universal expert-designed Q-matrix.

Development and Validation of Reading Comprehension Assessments by Using GDINA Model

This research is about developing and validating reading comprehension assessments using the GDINA model. Reading comprehension assessment has stayed relatively unaltered over the years, despite changes recommended by legislators or trends in the education industry. As a result, assessments are still used to evaluate students' performance, whether norm- or criterion-referenced; to inform instruction; to find out if students may acquire access to the right programmes; and even to evaluate the programme, which is still employed. The current reading comprehension assessment in Malaysia does not provide instructors with adequate information to make educated decisions. There was not enough data or information provided by the reading comprehension examinations to support the students individually, as a class, or even as a whole school, despite the teachers' sincere attempts to assist their students in performing and succeeding and becoming strong readers. We need better assessments to help instructors who are desperate for effective tools to help them fulfill individual needs. Reading comprehension is a mental process that occurs when a reader engages with the material. It's a problem for people who have trouble reading. Thus, the discussion is about developing and validating reading comprehension, which can be improvised using the GDINA model.

Validating a Reading Assessment Within the Cognitive Diagnostic Assessment Framework: Q-Matrix Construction and Model Comparisons for Different Primary Grades.

Cognitive diagnostic assessment (CDA) has been developed rapidly to provide fine-grained diagnostic feedback on students’ subskills and to provide insights on remedial instructions in specific domains. To date, most cognitive diagnostic studies on reading tests have focused on retrofitting a single booklet from a large-scale assessment (e.g., PISA and PIRLS). Critical issues in CDA involve the scarcity of research to develop diagnostic tests and the lack of reliability and validity evidence. This study explored the development and validation of the Diagnostic Chinese Reading Comprehension Assessment (DCRCA) for primary students under the CDA framework. Reading attributes were synthesized based on a literature review, the national curriculum criteria, the results of expert panel judgments, and student think-aloud protocols. Then, the tentative attributes were used to construct three booklets of reading comprehension items for 2–6 graders at three key stages. The assessment was administered to a large population of students (N = 21,466) in grades 2–6 from 20 schools in a district of Changchun City, China. Q-matrices were compared and refined using the model-data fit and an empirical validation procedure, and five representative cognitive diagnostic models (CDMs) were compared for optimal performance. The fit indices suggested that a six-attribute structure and the G-DINA model were best fitted for the reading comprehension assessment. In addition, diagnostic reliability, construct, internal and external validity results were provided, supporting CDM classifications as reliable, accurate, and useful. Such diagnostic information could be utilized by students, teachers, and administrators of reading programs and instructions.

Cognitively Diagnostic Analysis Using the G-DINA Model in R

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed.

Application of Bi-factor MIRT and Higher-order CDM Models to an In-house EFL Listening Test for Diagnostic Purposes

ABSTRACT The present study examined the performance of the bi-factor multidimensional item response theory (MIRT) model and higher-order (HO) cognitive diagnostic models (CDM) in providing diagnostic information and general ability estimation simultaneously in a listening test. The data used were 1,611 examinees’ item-level responses to an in-house EFL listening test in China and five content experts’ item-attribute coding results of the test form. The bi-factor MIRT model was compared with five CDMs with and without a higher-order structure in terms of model fit, attribute classification and general ability estimation. The results showed that the bi-factor MIRT model provided the best model-data fit, followed by the HO-G-DINA model, the saturated G-DINA model, and other reduced CDMs. The HO-G-DINA model produced attribute classification results more similar to the G-DINA model, whereas the bi-factor MIRT model offered better results in discriminating examinees’ general listening ability. The findings of this study highlighted the feasibility of using the bi-factor MIRT model as an attractive alternative for diagnostic assessment, especially in language assessment where attributes are assumed to be continuous.

An Empirical Q-Matrix Validation Method for the Polytomous G-DINA Model.

A number of empirically based Q-matrix validation methods are available in the literature, all of which were developed for cognitive diagnosis models (CDMs) involving dichotomous attributes. However, in many applications, it is more instructionally relevant to classify students into more than two categories (e.g., no mastery, basic mastery, and advanced mastery). To extend the practical utility of CDMs, methods for validating the Q-matrix for CDMs that measure polytomous attributes are needed. This study focuses on validating the Q-matrix of the generalized deterministic input, noisy, "and" gate model for polytomous attributes (pG-DINA). The pGDI, an extension of the G-DINA model discrimination index, is proposed for polytomous attributes. The pGDI serves as the basis of a validation method that can be used not only to identify potential misspecified q-entries, but also to suggest more appropriate attribute-level specifications. The theoretical properties of the pGDI are underpinned by several mathematical proofs, whereas its practical viability is examined using simulation studies covering various conditions. The results show that the method can accurately identify misspecified q-entries and suggest the correct attribute-level specifications, particularly when high-quality items are involved. The pGDI is applied to a proportional reasoning test that measures several polytomous attributes.

An Improved Parameter-Estimating Method in Bayesian Networks Applied for Cognitive Diagnosis Assessment.

Bayesian networks (BNs) can be employed to cognitive diagnostic assessment (CDA). Most of the existing researches on the BNs for CDA utilized the MCMC algorithm to estimate parameters of BNs. When EM algorithm and gradient descending (GD) learning method are adopted to estimate the parameters of BNs, some challenges may emerge in educational assessment due to the monotonic constraints (greater skill should lead to better item performance) cannot be satisfied in the above two methods. This paper proposed to train the BN first based on the ideal response pattern data contained in every CDA and continue to estimate the parameters of BN based on the EM or the GD algorithm regarding the parameters based on the IRP training method as informative priors. Both the simulation study and realistic data analysis demonstrated the validity and feasibility of the new method. The BN based on the new parameter estimating method exhibits promising statistical classification performance and even outperforms the G-DINA model in some conditions.

Automated Test Assembly for Multistage Testing With Cognitive Diagnosis.

Computer multistage adaptive test (MST) combines the advantages of paper and pencil-based test (P&P) and computer-adaptive test (CAT). As CAT, MST is adaptive based on modules; as P&P, MST can meet the need of test developers to manage test forms and keep test forms parallel. Cognitive diagnosis (CD) can accurately measure students’ knowledge states (KSs) and provide diagnostic information, which is conducive to student’s self-learning and teacher’s targeted teaching. Although MST and CD have a lot of advantages, many factors prevent MST from applying to CD. In this study, we first attempt to employ automated test assembly (ATA) to achieve the objectives of MST in the application of CD (called CD-MST) via heuristic algorithms. The mean correct response probability of all KSs for each item is used to describe the item difficulty of CD. The attribute reliability in CD is defined as the test quantitative target. A simulation study with the G-DINA model (generalized deterministic input noisy “and” gate model) was carried out to investigate the proposed CD-MST, and the results showed that the assembled panels of CD-MST satisfied the statistical and the non-statistical constraints.

International comparison analysis of mathematics achievement for 4th grades using Sequential G-DINA model

본 연구는 다분 문항 반응 자료를 위한 순차적 G-DINA 모형의 적용 가능성을 확인하기 위하여 실제 자료인 TIMSS 2015 초등학교 4학년 수학 자료를 활용하여 국가 간 수학 성취도를 비교하는 것이다. 이를 위하여 TIMSS 2015 4학년 수학에 대한 단계형 Q행렬을 개발하여 내용 및 측정평가 전문가들의 검토와 순차적 G-DINA 모형에 적합한 통계적 타당화 절차를 거쳐 최종 Q행렬을 완성하였다. 연구 대상으로는 한국과 TIMSS 성취도 상위국에 해당하는 4개의 비교 국가가 포함되었다. 연구 결과, 첫째, 통계적 타당화 절차, 내용 및 측정평가 전문가들의 검토를 통해 자료에 보다 적합한 Q행렬을 완성하였고, 둘째, 다분 문항 반응 자료가 지니는 부분점수에 의한 정보를 소실하지 않고 각 단계별 문항 모수를 산출하여 보다 상세한 정보를 확보하였다. 셋째, SG-DINA 모형에 의한 국가별 비교 분석을 통해 국가별로 학생의 수학 성취 향상을 위해 필요한 인지요소를 파악하였다. 이러한 결과를 바탕으로 다분 인지진단모형의 적용 활성화, 향수 수학 교육과정 개선의 필요성을 시사점으로 도출하였다.This study compares mathematics achievements between countries using TIMSS 2015 elementary school 4th grade mathematics data, which are actual data, to confirm the applicability of the sequential G-DINA model for multi-item response data. To this end, a step-by-step Q matrix was developed for TIMSS 2015 4th grade mathematics, and the final Q matrix was completed through review of content and measurement evaluation experts, and a statistical justification procedure suitable for the sequential G-DINA model. The subjects of this study included Korea and four comparative countries, which are the top countries in TIMSS achievement. As a result of the study, first, through the review of statistical validation procedures, content, and measurement evaluation experts, a more suitable Q matrix could be completed, and second, the information by the partial score of the multi-item response data was not lost and each step More detailed information could be provided by calculating the item parameters. Third, through the comparative analysis of each country using the SG-DINA model, it was possible to identify the cognitive factors necessary for improving the student s mathematics achievement by country.

Developing a Learning Progression for Probability Based on the GDINA Model in China.

This research focuses on developing a learning progression of probability for middle school students, and it applies the GDINA model in cognitive diagnosis models to data analysis. GDINA model analysis firstly extracted nine cognitive attributes and constructed their attribute hierarchy and the hypothesized learning progression according to previous studies, curriculum standards, and textbooks. Then the cognitive diagnostic test was developed based on Q-matrix theory. Finally, we used the GDINA model to analyze a sample of 1624 Chinese middle school students’ item response patterns to identify their attribute master patterns, verify and modify the hypothesized learning progression. The results show that, first of all, the psychometric quality of the measurement instrument is good. Secondly, the hypothesized learning progression is basically reasonable and modified according to the attribute mastery probability. The results also show that the level of probabilistic thinking of middle school students is improving steadily. However, the students in grade 8 are slightly regressive. These results demonstrate the feasibility and superiority of using cognitive diagnosis models to develop a learning progression.