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Over several years, the evaluation of polytomous attributes in small‐sample settings has posed a challenge to the application of cognitive diagnosis models. To enhance classification precision, the support vector machine (SVM) was introduced for estimating polytomous attribution, given its proven feasibility for dichotomous cases. Two simulation studies and an empirical study assessed the impact of various factors on SVM classification performance, including training sample size, attribute structures, guessing/slipping levels, number of attributes, number of attribute levels, and number of items. The results indicated that SVM outperformed the pG‐DINA model in classification accuracy under dependent attribute structures and small sample sizes. SVM performance improved with an increased number of items but declined with higher guessing/slipping levels, more attributes, and more attribute levels. Empirical data further validated the application and advantages of SVMs. [ABSTRACT FROM AUTHOR]

Computerized adaptive testing for cognitive diagnosis (CD‐CAT) achieves remarkable estimation efficiency and accuracy by adaptively selecting and then administering items tailored to each examinee. The process of item selection stands as a pivotal component of a CD‐CAT algorithm, with various methods having been developed for binary responses. However, multiple‐choice (MC) items, an important item type that allows for the extraction of richer diagnostic information from incorrect answers, have been underemphasized. Currently, the Jensen–Shannon divergence (JSD) index introduced by Yigit et al. (Applied Psychological Measurement, 2019, 43, 388) is the only item selection method exclusively designed for MC items. However, the JSD index requires a large sample to calibrate item parameters, which may be infeasible when there is only a small or no calibration sample. To bridge this gap, the study first proposes a nonparametric item selection method for MC items (MC‐NPS) by implementing novel discrimination power that measures an item's ability to effectively distinguish among different attribute profiles. A Q‐optimal procedure for MC items is also developed to improve the classification during the initial phase of a CD‐CAT algorithm. The effectiveness and efficiency of the two proposed algorithms were confirmed by simulation studies. [ABSTRACT FROM AUTHOR]

One of the central components of cognitive diagnostic assessment is the Q-matrix, which is an essential loading indicator matrix and is typically constructed by subject matter experts. Nonetheless, to a large extent, the construction of Q-matrix remains a subjective process and might lead to misspecifications. Many researchers have recognized the importance of estimating or validating the Q-matrix, but most of them focus on the conditions of relatively large sample sizes. This article aims to explore Q-matrix validation possibilities under small sample conditions and uses regularized methods to validate the Q-matrix based on the compensatory reparametrized unified model and generalized deterministic inputs, noisy "and" gate models. Simulation studies were conducted to evaluate the viability of the modified least absolute shrinkage and selection operator (Lasso) and modified smoothly clipped absolute deviation (SCAD) methods, comparing them with existing methods. Results show that the modified Lasso and the modified SCAD methods outperform the stepwise, Hull, and MLR-B methods in general, especially under the conditions of small sample sizes. While good recovery in all small sample size conditions is not guaranteed, the modified methods demonstrate advantages across various item quality conditions. Also, a real data set is analyzed to illustrate the application of the modified methods. [ABSTRACT FROM AUTHOR]

In the realm of online learning, where resources are abundant, it is essential to customize recommendations and plans to meet individual learning needs. This involves not only identifying and addressing areas of weakness but also aligning the learning journey with each learner's cognitive preferences. However, existing methods for suggesting and structuring learning paths have notable limitations. To address these challenges, this paper introduces a knowledge graph-based personalized learning path planning method (KG-PLPPM). By leveraging a knowledge graph and refining cognitive diagnosis models, the proposed method tailors learning paths to individual needs. It evaluates knowledge concept similarity and learner mastery, and employs an algorithm for path planning. In the experiments, two metrics—the concept sequence degree and learning efficiency—are used to assess our work. Experimental results demonstrate that the method presented enhances the coherence and relevance of recommended learning paths, and achieves a higher concept sequence degree, indicating that knowledge concepts are arranged in a manner consistent with the learning sequence, which aligns more closely with learners' cognitive preferences. Moreover, across various learning progresses and path lengths, it effectively addresses weak knowledge areas, significantly enhancing learning efficiency. [ABSTRACT FROM AUTHOR]

The Q-matrix is one of the core components of cognitive diagnostic assessment, which is a matrix describing the relationship between items and the attributes being assessed. Numerous studies have shown that inaccuracies in defining the Q-matrix can degrade parameter estimation and model fitting results. Currently, Q-matrix validation often involves exhaustive search algorithms (ESA), which traverse through all possible q -vectors and determine the optimal q -vector for items based on indicators or criteria corresponding to different validation methods. However, ESA methods are time-consuming, especially when the number of attributes is large, as the search complexity grows exponentially. This study proposes a more efficient search algorithm, the priority attribute algorithm (PAA), which conducts searches one by one according to the priority of attributes, greatly simplifying the search process. Simulation studies indicate that PAA can significantly enhance search efficiency while maintaining the same or even higher accuracy than ESA, particularly when dealing with a large number of attributes. Moreover, the Q-matrix validation method employing PAA demonstrates better applicability to small samples. A real-data analysis indicates that applying the PAA-based Q-matrix validation method may yield suggested Q-matrices with higher model–data fit and greater practical utility. [ABSTRACT FROM AUTHOR]

With the increasing demand for precise test feedback, cognitive diagnosis models (CDMs) have attracted more and more attention for fine classification of students with regard to their ability to master given skills. The aim of this paper is to use a highly effective Gibbs algorithm based on auxiliary variables (GAAV) to estimate the deterministic input noisy "and" gate (DINA) model that is widely used for cognitive diagnosis. The applicability of the algorithm to other CDMs is also discussed. Unlike the Metropolis–Hastings algorithm, this new algorithm does not require repeated adjustment of the turning parameters to achieve an appropriate acceptance probability, and it also overcomes the dependence of the traditional Gibbs sampling algorithm on the conjugate prior distribution. Four simulation studies are conducted, and a detailed analysis of fraction subtraction test data is carried out to further illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Based on theories on vocabulary knowledge, grammar knowledge, and reading comprehension subcomponents, ten attributes/subskills were defined for 50 items from relevant subtests of TEM4 (Band Four of Test for English Majors in China). Cognitive diagnosis was conducted on the TEM4 data of the randomly sampled 2285 examinees (roughly at the B2 level) through the saturated generalized deterministic inputs, noisy "and" gate (G-DINA) model. The person parameters obtained from cognitive diagnosis served as the basis for simple multiple regression and path analyses for detecting relationship patterns. The study discovered that the relationship pattern at both construct and attribute/subskill levels can be better described as a mediation pattern in which vocabulary knowledge and its attributes are more suitable to serve as the starting point for reading comprehension. The study also discussed the patterns of the impact of vocabulary and grammar attributes on reading subskills as well as the internal subskill relationships within the construct of reading comprehension. [ABSTRACT FROM AUTHOR]

Cognitive Diagnosis Models in educational measurement are restricted latent class models that describe ability in a knowledge domain as a composite of latent skills an examinee may have mastered or failed. Different combinations of skills define distinct latent proficiency classes to which examinees are assigned based on test performance. Items of cognitively diagnostic assessments are characterized by skill profiles specifying which skills are required for a correct item response. The item-skill profiles of a test form its Q-matrix. The validity of cognitive diagnosis depends crucially on the correct specification of the Q-matrix. Typically, Q-matrices are determined by curricular experts. However, expert judgment is fallible. Data-driven estimation methods have been developed with the promise of greater accuracy in identifying the Q-matrix of a test. Yet, many of the extant methods encounter computational feasibility issues either in the form of excessive amounts of CPU times or inadmissible estimates. In this article, a two-step algorithm for estimating the Q-matrix is proposed that can be used with any cognitive diagnosis model. Simulations showed that the new method outperformed extant estimation algorithms and was computationally more efficient. It was also applied to Tatsuoka's famous fraction-subtraction data. The paper concludes with a discussion of theoretical and practical implications of the findings. [ABSTRACT FROM AUTHOR]

The Q‐matrix is a crucial component of cognitive diagnostic theory and an important basis for the research and practical application of cognitive diagnosis. In practice, the Q‐matrix is typically developed by domain experts and may contain some misspecifications, so it needs to be refined using Q‐matrix validation methods. Based on signal detection theory, this paper puts forward a new Q‐matrix validation method (i.e., β$$ \beta $$ method) and then conducts a simulation study to compare the new method with existing methods. The results show that when the model is DINA (deterministic inputs, noisy ‘and’ gate), the β$$ \beta $$ method outperforms the existing methods under all conditions; under the generalized DINA (G‐DINA) model, the method still has the highest validation rate when the sample size is small, and the item quality is high or the rate of Q‐matrix misspecification is ≥.4. Finally, a sub‐dataset of the PISA 2000 reading assessment is analysed to evaluate the reliability of the β$$ \beta $$ method. [ABSTRACT FROM AUTHOR]

Feedback is a powerful instructional tool for motivating learning. But effective feedback, requires that instructors have accurate information about their students' current knowledge status and their learning progress. In modern educational measurement, two major theoretical perspectives on student ability and proficiency can be distinguished. Latent trait models identify ability as a continuous uni- or multi-dimensional construct, with unidimensional item response theoretic (IRT) models presumably the most popular type of latent trait models. They report a single ability score that allows for locating examinees relative to their peers on the latent ability dimension targeted by the test. Latent trait models have been criticized for lacking diagnostic information on students' specific skills, their strengths and weaknesses in a knowledge domain. Cognitive diagnosis (CD) models, in contrast, describe ability as a combination of discrete skills (called "attributes") that constitute (partially) ordered latent classes of proficiency. The focus of CD is on collecting information about the learning progress for immediate feedback to students in terms of skills they have mastered and those needing study. CD has been underused in education; performance assessment still mostly relies on latent-trait-based methods. The motivation for the study reported here arose from the desire to conduct a side-by-side evaluation of the two seemingly disparate psychometric frameworks, CD and IRT. Data from a biochemistry end-of-term exam were used for illustration. They were fitted with multiple CD and IRT models, among them also HO-GDINA models that permit for a close approximation to several unidimensional IRT models. [ABSTRACT FROM AUTHOR]

Concept mapping is widely used as a tool for assessing students' understanding of science. To fully realize the diagnostic potential of concept mapping, a scoring method that not only provides an objective and accurate assessment of students' drawn concept maps but also provides a detailed understanding of students' proficiency and deficiencies in knowledge is necessary. However, few of the existing scoring methods focus on the latent constructs (e.g., knowledge, skills, and cognitive processes) that guide the creation of concept maps. Instead, they focus on the completeness of the concept map by assigning a composite score, which makes it difficult to generate targeted diagnostic feedback information for advancing students' learning. To apply the diagnostic classification model to the quantitative analysis of concept maps, this study introduced the novel application of the item expansion‐based diagnostic classification analysis (IE‐DCA) for this purpose. The IE‐DCA can not only assess students' concept mapping abilities along a continuum but also classify students according to their concept mapping attributes when constructing the concept maps. The application and benefits of this approach were illustrated using a physics concept‐mapping item related to particle and rigid body. Results showed that the estimated attribute profiles via the IE‐DCA provided more detailed information about students' latent constructs than the composite score. Overall, this study illustrates the feasibility and potential of applying IE‐DCA to analyze concept maps. Future applications of IE‐DCS in other assessments in science education are discussed. [ABSTRACT FROM AUTHOR]

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As a significant breakthrough in educational measurement, cognitive diagnostic assessment (CDA) has made up for the shortcomings of traditional assessment practice by providing fine-grained information about students' latent knowledge structures beyond single scores. Along with the surging demand for diagnostic feedback in large-scale language tests, an increasing number of CDA studies have emerged primarily for the purpose of facilitating language teaching and learning. In this paper, we conducted a thematic review of 35 empirical studies on cognitive diagnosis in language assessment during the years 2009–2021. In our review, we analyzed three major research topics in this field, namely, application of CDA, optimization of CDA, and CDA for practical and theoretical uses. Along with our analysis, several potential lacunae were discussed, based on which some future directions were provided. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining accurate estimates of skill mastery at the population level. We introduce a novel algorithm (bagging algorithm for deterministic inputs noisy "and" gate) that is inspired by ensemble learning methods in the machine learning literature and produces more stable and accurate estimates of the population skill mastery profile distribution for small sample sizes. Using both simulated data and real data from the Examination for the Certificate of Proficiency in English, we demonstrate that the proposed method outperforms other methods on several metrics in a wide variety of scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts (e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes (e.g., region). In this paper, we aim to explore fairness in cognitive diagnosis and answer two questions: (1) Are the results of existing cognitive diagnosis models affected by sensitive attributes? (2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several well-known cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair proficiency. We design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment (PISA) dataset clearly show the effectiveness of our framework. [ABSTRACT FROM AUTHOR]

As a new generation of assessment theory, Cognitive Diagnostic Assessment (CDA) has unique advantages in diagnosing students' personalized information. Cognitive diagnostic models (CDMs) are the core of CDA, so the selection of models becomes the key link of CDA. Generally, the selection of the models is based on data driven methods, such as comparing Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other indicators. Few studies pay attention to the voice of subject experts. This study selected 10% of Tatsuoka fraction subtraction data, which were analyzed by 5 mathematics education experts according to the criteria of master (1), not master (0), and part master (0.5) for 8 attributes. We further analyzed the Pearson correlation coefficient of expert results and common model analysis results, and concluded that the DINA (the Deterministic Input, Noisy "And" Gate) model diagnosis results had the highest correlation with expert results, with the coefficient reaching 0.8624. The results showed that, from the perspective of mathematical experts, DINA model was most suitable for the diagnosis of fractional subtraction, which provided evidence for the rationality of DINA model diagnosis of fractional subtraction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Q-matrix, which specifies the relationship between items and attributes, is a crucial component of cognitive diagnostic models (CDMs). A precisely specified Q-matrix allows for valid cognitive diagnostic assessments. In practice, a Q-matrix is usually developed by domain experts, and noted as being subjective and potentially containing misspecifications which can decrease the classification accuracy of examinees. To overcome this, some promising validation methods have been proposed, such as the general discrimination index (GDI) method and the Hull method. In this article, we propose four new methods for Q-matrix validation based on random forest and feed-forward neural network techniques. Proportion of variance accounted for (PVAF) and coefficient of determination (i.e., the McFadden pseudo-R2) are used as input features for developing the machine learning models. Two simulation studies are carried out to examine the feasibility of the proposed methods. Finally, a sub-dataset of the PISA 2000 reading assessment is analyzed as illustration. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis models provide diagnostic information on whether examinees have mastered the skills, called "attributes," that characterize a given knowledge domain. Based on attribute mastery, distinct proficiency classes are defined to which examinees are assigned based on their item responses. Attributes are typically perceived as binary. However, polytomous attributes may yield higher precision in the assessment of examinees' attribute mastery. Karelitz (2004) introduced the ordered-category attribute coding framework (OCAC) to accommodate polytomous attributes. Other approaches to handle polytomous attributes in cognitive diagnosis have been proposed in the literature. However, the heavy parameterization of these models often created difficulties in fitting these models. In this article, a nonparametric method for cognitive diagnosis is proposed for use with polytomous attributes, called the nonparametric polytomous attributes diagnostic classification (NPADC) method, that relies on an adaptation of the OCAC framework. The new NPADC method proposed here can be used with various cognitive diagnosis models. It does not require large sample sizes; it is computationally efficient and highly effective as is evidenced by the recovery rates of the proficiency classes observed in large-scale simulation studies. The NPADC method is also used with a real-world data set. [ABSTRACT FROM AUTHOR]

Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind the model's predictions for the next problem, and this limits their opportunity to take an active role in improving the learning process. To address this limitation, this article presents a novel personalized exercise question recommendation model based on knowledge tracing. The approach incorporates graph convolutional neural networks to model the student's abilities, thus enhancing the interpretability of the model. By employing Bidirectional gate recurrent unit (Bi-GRU), the model effectively traces fluctuations in students' abilities over time and predicts their responses to exercise questions. Experimental results demonstrate the effectiveness of this model, achieving an accuracy of 90.8% and 92.6% on ASSISTment 2009 and ASSISTment 2017 datasets, containing 4218 and 1709 student records, respectively. Moreover, the experiment was also conducted to validate the model's exercise difficulty setting. Results indicate an acceptable level of effectiveness in generating appropriate difficulty-level recommendations for individual students. The proposed model contributes to advancing personalized exercise recommendations by offering valuable insights that can lead to more efficient and effective student learning experiences.

The testlet design is very popular in educational and psychological assessments. This article proposes a new cognitive diagnosis model, the multiple-choice cognitive diagnostic testlet (MC-CDT) model for tests using testlets consisting of MC items. The MC-CDT model uses the original examinees' responses to MC items instead of dichotomously scored data (i.e., correct or incorrect) to retain information of different distractors and thus enhance the MC items' diagnostic power. The Markov chain Monte Carlo algorithm was adopted to calibrate the model using the WinBUGS software. Then, a thorough simulation study was conducted to evaluate the estimation accuracy for both item and examinee parameters in the MC-CDT model under various conditions. The results showed that the proposed MC-CDT model outperformed the traditional MC cognitive diagnostic model. Specifically, the MC-CDT model fits the testlet data better than the traditional model, while also fitting the data without testlets well. The findings of this empirical study show that the MC-CDT model fits real data better than the traditional model and that it can also provide testlet information. [ABSTRACT FROM AUTHOR]

In today's knowledge-intensive and digital society, collaborative problem-solving (CPS) is considered a critical skill for students to develop. Moreover, international education research has embraced a new paradigm of communication-focused inquiry, and the commognitive theory helps enhance the understanding of CPS work. This paper aims to enhance the CPS skills by identifying, diagnosing, and visualizing commognitive conflicts during the CPS process, thereby fostering a learning-oriented innovative approach and even giving the script of technologyassisted feedback practices. Specifically, we utilized open-ended mathematical tasks and multi-camera video recordings to analyze the commognitive conflicts in CPS among 32 pairs, comprising 64 Year 7 students. After selecting the highquality, medium-quality, and low-quality student pairs based on the SOLO theory, further investigations were made in the discourse diagnosis and visual analysis for the knowledge dimensions of commognitive conflict. Finally, it was discovered that there is a need to encourage students to focus on and resolve commognitive conflicts while providing timely feedback. Visual studies of commognitive conflict can empower AI-assisted teaching, and the intelligent diagnosis and visual analysis of CPS provide innovative solutions for teaching feedback. [ABSTRACT FROM AUTHOR]

Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model. [ABSTRACT FROM AUTHOR]

In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students' mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets. In this paper, we propose a new cognitive diagnosis model called BNMI-DINA, which stands for Bayesian Network-based Multiprocess Incremental DINA. Our proposed model aims to enhance personalized learning by providing accurate and detailed assessments of students' cognitive abilities. By incorporating a Bayesian network, BNMI-DINA establishes the dependency relationship between knowledge points, enabling more accurate evaluations of students' mastery levels. To enhance model convergence speed, key steps of our proposed algorithm are parallelized. We also provide theoretical proof of the convergence of BNMI-DINA. Extensive experiments demonstrate that our approach effectively enhances model accuracy and reduces computational time compared to state-of-the-art cognitive diagnosis models. [ABSTRACT FROM AUTHOR]

With the continuous development of education, personalized learning has attracted great attention. How to evaluate students' learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student's learning outcomes, such as "scores" or "right/wrong," which seldom reflects the development of students' cognitive level and lacks effective diagnostic information. This article proposes a non-parametric multi-level scoring cognitive diagnosis method based on the KNN and the characteristics of information technology courses named the EW-KNN (E-weight K-Nearest Neighbor). Compared with the KNN, the EW-KNN improved two key points. One is that it takes the number of IRP (Ideal Response Pattern) as the K value to adapt to different types of tests. The other is that the nearest neighbor distance is introduced to solve the problem of misjudgment of the categories. The Monte Carlo simulation method is used to test its performance. The results indicate that the EW-KNN has a higher accuracy rate and is suitable for information technology courses. Furthermore, the method is applied in information technology course to make a cognitive diagnosis of 120 students of high school in Shanghai. Results demonstrate that the EW-KNN can accurately diagnose each student's cognition levels and knowledge structure accurately. [ABSTRACT FROM AUTHOR]

Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom's cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including "knowing", "understanding", and "application", thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom's Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of "knowing", "understanding", and "applying" to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students' knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of "understanding" and "application", the notions of "knowledge group" and "higher-order knowledge group" are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students' knowledge proficiency, affirming the scientific and meaningful integration of Bloom's cognitive hierarchy in knowledge proficiency assessment. [ABSTRACT FROM AUTHOR]

The score profiles could be used to measure learners' skills proficiency via cognitive diagnosis models (CDMs) for predicting their performance in the future examination. The prediction results could provide important decision-making supports for personalized e-learning instruction. However, facing the possible complexity of skills, the uncertainty of learners' skill proficiency and the large-scale volume of score profiles, the existing CDMs have limitations in the measurement mechanisms and diagnostic efficiency. In this paper, we proposed an approach based on a fuzzy cloud cognitive diagnosis framework (FC-CDF) to predicting examinees' performance in e-learning environment. In this approach, the normal cloud models (NCMs) are utilized innovatively to measure the expectation, degree of variation and variation frequency of learners' skill proficiency, and each NCM is transformed into an interval fuzzy number to characterize the uncertainty of the skill proficiency for every learner. Combining the educational psychology hypothesis with the parameter estimation method, we could obtain the learners' skill proficiency level and the slip and guess factors relevant to each test item, based on which the learners' scores could be predicted in a future test. Finally, the experiments demonstrate that the proposed approach provides good accuracy and significantly reduces execution time for predicting examinee performance, compared with the existing methods. [ABSTRACT FROM AUTHOR]

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis is an important issue of intelligent education systems, which aims to estimate students' proficiency on specific knowledge concepts. Most existing studies rely on the assumption of static student states and ignore the dynamics of proficiency in the learning process, which makes them unsuitable for online learning scenarios. In this paper, we propose a unified temporal item response theory (UTIRT) framework, incorporating temporality and randomness of proficiency evolving to get both accurate and interpretable diagnosis results. Specifically, we hypothesize that students' proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporality and randomness factors. Furthermore, based on the relationship between student states and exercising answers, we hypothesize that the answering result at time k contributes most to inferring a student's proficiency at time k, which also reflects the temporality aspect and enables us to get analytical maximization (M-step) in the expectation maximization (EM) algorithm when estimating model parameters. Our UTIRT is a framework containing unified training and inferencing methods, and is general to cover several typical traditional models such as Item Response Theory (IRT), multidimensional IRT (MIRT), and temporal IRT (TIRT). Extensive experimental results on real-world datasets show the effectiveness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT. [ABSTRACT FROM AUTHOR]

The nonparametric classification (NPC) method has been proven to be a suitable procedure for cognitive diagnostic assessments at a classroom level. However, its nonparametric nature impedes the obtention of a model likelihood, hindering the exploration of crucial psychometric aspects, such as model fit or reliability. Reporting the reliability and validity of scores is imperative in any applied context. The present study proposes the restricted deterministic input, noisy "and" gate (R-DINA) model, a parametric cognitive diagnosis model based on the NPC method that provides the same attribute profile classifications as the nonparametric method while allowing to derive a model likelihood and, subsequently, to compute fit and reliability indices. The suitability of the new proposal is examined by means of an exhaustive simulation study and a real data illustration. The results show that the R-DINA model properly recovers the posterior probabilities of attribute mastery, thus becoming a suitable alternative for comprehensive small-scale diagnostic assessments. [ABSTRACT FROM AUTHOR]

As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.