Probabilistic-Input, Noisy Conjunctive Models for Cognitive Diagnosis

Zhan, Peida; Wang, Wen Chung; Jiao, Hong; Bian, Yufang

doi:10.3389/fpsyg.2018.00997

Cited by 11 publications

(12 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within IRT this has further led to the test design idea (Embretson, 1985), cognitive diagnosis modeling (CDM) (Rupp et al, 2010) and explanatory item response models (De Boeck and Wilson, 2004). An important difference between CDM and the other approaches is that process inferences are discrete (often binary) and refer to mastery of skills that may be related to hypothesized processes; but see Zhan et al (2018c) for mastery in probabilistic terms. However, because response times are not involved in these approaches, we will not follow up on these developments here.…”

Section: Response Time Modelsmentioning

confidence: 99%

An Overview of Models for Response Times and Processes in Cognitive Tests

2019

View full text Add to dashboard Cite

Response times (RTs) are a natural kind of data to investigate cognitive processes underlying cognitive test performance. We give an overview of modeling approaches and of findings obtained with these approaches. Four types of models are discussed: response time models (RT as the sole dependent variable), joint models (RT together with other variables as dependent variable), local dependency models (with remaining dependencies between RT and accuracy), and response time as covariate models (RT as independent variable). The evidence from these approaches is often not very informative about the specific kind of processes (other than problem solving, information accumulation, and rapid guessing), but the findings do suggest dual processing: automated processing (e.g., knowledge retrieval) vs. controlled processing (e.g., sequential reasoning steps), and alternative explanations for the same results exist. While it seems well-possible to differentiate rapid guessing from normal problem solving (which can be based on automated or controlled processing), further decompositions of response times are rarely made, although possible based on some of model approaches.

show abstract

Section: Response Time Modelsmentioning

confidence: 99%

An Overview of Models for Response Times and Processes in Cognitive Tests

2019

View full text Add to dashboard Cite

show abstract

“…For example, Zhan et al (2018) proposed the probabilistic-input, noisy conjunctive (PINC) model, which defined attribute mastery status as probabilities and reported the probability of knowledge status for examinees from 0 to 1. According to Zhan et al (2018) , classifying an examinee’s KSs to 0 or 1 will cause a lot of information of examinees to be lost, so the PINC model can provide more precise and richer information to examinees’ KSs than the traditional CDMs. Therefore, researchers can try to use the probability of examinees’ KSs to develop a new difficulty index in the future.…”

Section: Limitations and Further Researchmentioning

confidence: 99%

Automated Test Assembly for Multistage Testing With Cognitive Diagnosis

Cai

Gao

et al. 2021

Front. Psychol.

View full text Add to dashboard Cite

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.

show abstract

“…Some small but existing growths are ignored, which in turn may lead students, especially those with low motivation to learn, to conclude that the current diagnostic feedback is ineffective or to abandon remedial action. Thus, further studies can attempt to extend the current models to handle polytomous attributes (Karelitz, 2004 ) and probabilistic attributes (Zhan et al, 2018b ) because they can describe the learning growth in a more refined way than binary attributes.…”

Section: Future Research Directionmentioning

confidence: 99%