On the usefulness of CDA-based score reporting: implications for self-regulated learning

In cognitive diagnostic modeling research, one area that has not had enough research interests is remedial learning or instruction based on the information provided by cognitive diagnostic assessment (CDA). The present study tries to address this research gap by looking into the usefulness of the fine-grained score reports based on CDA in two different ways, i.e., a post-test and a survey inquiring about the perceived effectiveness of the score report that provided the skill profile of individual students. Another significance of the current research is that it attempted to introduce cognitive diagnostic assessment into a regular school exam unlike most previous studies that retrofitted to the existing tests. College students in Korea participated in the study, who were encouraged to do self-regulated learning utilizing the detailed information in the CDA-based score report. The results of the post-test and the survey were positive overall, supporting the utility of CDA-generated performance reports. The article ends with some suggestions for future research based on the limitations of the study.

An Optimal Choice of Cognitive Diagnostic Model for Second Language Listening Comprehension Test

Cognitive diagnostic models (CDMs) show great promise in language assessment for providing rich diagnostic information. The lack of a full understanding of second language (L2) listening subskills made model selection difficult. In search of optimal CDM(s) that could provide a better understanding of L2 listening subskills and facilitate accurate classification, this study carried a two-layer model selection. At the test level, A-CDM, LLM, and R-RUM had an acceptable and comparable model fit, suggesting mixed inter-attribute relationships of L2 listening subskills. At the item level, Mixed-CDMs were selected and confirmed the existence of mixed relationships. Mixed-CDMs had better model and person fit than G-DNIA. In addition to statistical approaches, the content analysis provided theoretical evidence to confirm and amend the item-level CDMs. It was found that semantic completeness pertaining to the attributes and item features may influence the attribute relationships. Inexplicable attribute conflicts could be a signal of suboptimal model choice. Sample size and the number of multi-attribute items should be taken into account in L2 listening cognitive diagnostic modeling studies. This study provides useful insights into the model selection and the underlying cognitive process for L2 listening tests.

Opening the black box of selection

Medical school selection is currently in the paradoxical situation in which selection tools may predict study outcomes, but which constructs are actually doing the predicting is unknown (the ‘black box of selection’). Therefore, our research focused on those constructs, answering the question: do the internal structures of the tests in an outcome-based selection procedure reflect the content that was intended to be measured? Downing’s validity framework was applied to organize evidence for construct validity, focusing on evidence related to content and internal structure. The applied selection procedure was a multi-tool, CanMEDS-based procedure comprised of a video-based situational judgement test (focused on (inter)personal competencies), and a written aptitude test (reflecting a broader array of CanMEDS competencies). First, we examined content-related evidence pertaining to the creation and application of the competency-based selection blueprint and found that the set-up of the selection procedure was a robust, transparent and replicable process. Second, the internal structure of the selection tests was investigated by connecting applicants’ performance on the selection tests to the predetermined blueprint using cognitive diagnostic modeling. The data indicate 89% overlap between the expected and measured constructs. Our results support the notion that the focus placed on creating the right content and following a competency-blueprint was effective in terms of internal structure: most items measured what they were intended to measure. This way of linking a predetermined blueprint to the applicants’ results sheds light into the ‘black box of selection’ and can be used to support the construct validity of selection procedures.

Applying AdaBoost to Improve Diagnostic Accuracy

Cognitive diagnostic modeling has been adopted to support various diagnostic measuring processes. Specifically, this approach allows practitioners and/or researchers to investigate an individual’s status with regard to certain latent variables of interest. However, the diagnostic information provided by traditional estimation approaches often suffers from low accuracy, especially under small sample conditions. This paper adopts an AdaBoost technique, popular in the field of machine learning, to estimate latent variables. Further, the proposed approach involves the construction of a simple iterative algorithm that is based upon the AdaBoost technique – such that the area under the curve (AUC) is minimized. The algorithmic details are elaborated via pseudo codes with line-to-line verbal explanations. Simulation studies were conducted such that the improvement of latent variable estimates via the proposed approach can be examined.

Validation of a Questionnaire for Personality Profiling using Cognitive Diagnostic Modeling

This article describes the development of the ENCUIST (Extroversion, Neuroticism, Callous-Unemotional, Instability, Short-Test) questionnaire, which has been created to provide a personality profiling method based on a cognitive diagnostic modeling framework. The ENCUIST measures the attributes of extroversion, neuroticism, callous unemotionality and overt expressions of anger that are relevant in a forensic context. The scores provided by the ENCUIST are binary classifications of the individuals (high/low) in these attributes. The ENCUIST was developed using a sample of 516 subjects to study its validation through psychometric procedures, including factor analysis, cognitive diagnostic modeling and structural equation modeling. The results supported a four-factor structure. Linear regressions were used to evaluate the predictive validity of the scores provided by ENCUIST with respect to two external criteria that are relevant in the forensic context, namely behavioral activation and behavioral inhibition. The results showed that the extroversion dimension is positively related to behavioral activation, although the effect size is modest and the proportion of explained variance is only 11%. Moreover, the dimensions of neuroticism and anger expression are positively related to behavioral inhibition, with 7% of the variance explained. Together, these results suggest that cognitive diagnostic models are useful tools for the elaboration of personality profiles based on classifying subjects along binary attributes.

An Application of the Support Vector Machine for Attribute-By-Attribute Classification in Cognitive Diagnosis

Cognitive diagnostic modeling in educational measurement has attracted much attention from researchers in recent years. Its applications in real-world assessments, however, have been lagging behind its theoretical development. Reasons include but are not limited to requirement of large sample size, computational complexity, and lack of model fit. In this article, the authors propose to use the support vector machine (SVM), a popular supervised learning method to make classification decisions on each attribute (i.e., if the student masters the attribute or not), given a training dataset. By using the SVM, the problem of fitting and calibrating a cognitive diagnostic model (CDM) is converted into a quadratic optimization problem in hyperdimensional space. A classification boundary is obtained from the training dataset and applied to new test takers. The present simulation study considers the training sample size, the error rate in the training sample, the underlying CDM, as well as the structural parameters in the underlying CDM. Results indicate that by using the SVM, classification accuracy rates are comparable with those obtained from previous studies at both the attribute and pattern levels with much smaller sample sizes. The method is also computationally efficient. It therefore has great promise to increase the usability of cognitive diagnostic modeling in educational assessments, particularly small-scale testing programs.