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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]

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]

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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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]

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.)

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]

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]

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]

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.)

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]

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]

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]

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]

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]

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]

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]

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]

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]

With the rapid popularization of the new generation of information technology, education informatization has become a major trend in today’s education development, and more and more intelligent educational assistance platforms have emerged. The study proposes an empirical Q-matrix neurocognitive diagnostic model by introducing a neurocognitive diagnostic model, which improves the diagnostic effect of the NCD-Q model by learning the complex interaction function between students and exercises through neural networks. CFR-CD, an enhanced personalized exercise recommendation technique, is proposed to create a learner model of similar exercise expertise using a collaborative filtering algorithm. The CFR-CD algorithm is tested experimentally against other recommendation algorithms by suggesting exercises that are based on students’ knowledge mastery and similar learners’ answer data. Among them, the recommended effect of the CFR-CD model on simple test questions is better than the NMF model, the precision rate of the CFR-CD model is improved by about 3% on 50% and 60% of test question test sets, and the recall rate and F1 value of this model are the best among the three models. Finally, the experimental pre and post-tests were conducted to explore the teaching effect of the CFR-CD model in Civics education, and three dimensions, as well as sub-dimensions, were divided to analyze the influence effect of the CFR-CD model, which concluded that the Civics character, cultural literacy, and value concepts were all improved, among which the improvement effect of Civics character was the most obvious.

In the era of rapid development of computer technology and education technology, personalized teaching can provide learners with learning content that meets their needs and achieves the teaching purpose of “tailor-made teaching”. In this paper, the MOB method is used to reorganize and describe digital teaching resources after the coding process so as to achieve the development and management of digital teaching resources in sensor courses. Then, the hybrid differential evolution algorithm is used to cluster similar learning resources and learners in the learning process, and the recommendation of personalized digital teaching resources in sensor courses for students is realized through the operations of mutation and selection. Based on this, a personalized teaching model is constructed for teaching sensor courses. After testing, the personalized teaching model was found to be able to accurately analyze the learners’ knowledge of each knowledge point, and its accuracy is much better than the benchmark model. The empirical analysis shows that after applying the personalized teaching model to assist teaching, the average score of students’ sensor course test scores increased from 65.26 to 90.22 (P

Network general knowledge course is a meaningful way to cultivate comprehensive quality talents in Chinese higher education based on the modern information technology environment, and the optimization of network general knowledge course teaching is directly related to whether the network general knowledge course can be effectively implemented, which affects the overall effect of talent cultivation. In this paper, starting from the purpose and goal of humanities general education courses, the optimization direction of teaching general education courses using artificial intelligence is clarified. Skeletal sequence feature extraction is used to obtain students’ general education classroom behavior data, and a stackable multimodal attention module consisting of a self-attention layer and a multimodal co-attention layer is designed for feature fusion and action prediction based on multimodal characteristics. After data integration and processing, the HGCL-LG network model is proposed to complete the diagnosis of students’ general knowledge cognition. The trend of interest assessment and multimodal model calculation value is consistent. In contrast, the student’s interest attention in general knowledge classroom above 0.33 accounts for more than 40% of the overall information, and the interest attention is good. Students’ scores on the objective questions of general knowledge are good; the average score range is between 1.5 and 2 points, the degree of dispersion is small, and the mastery of basic knowledge of general knowledge is good.

A good teacher-student relationship is an important part of the educational community and a symbol of spiritual civilization. Effective teaching in the classroom requires even better communication and interactive actions between teachers and students, and measures to improve teacher-student relationships are proposed for the sake of classroom teaching. In this paper, an adaptive learning system based on cognitive diagnosis is constructed, which utilizes the DINA cognitive diagnosis model to obtain the learner’s cognitive structure, perceive the changes in the student’s cognitive structure in real time, and allow the learning task to make adaptive adjustments. The K-means clustering algorithm randomly selects the number of K objects as the center of mass for the initial clustering, and after the output of clustering and layering results, the students will be classified according to the categorization of the student’s learning needs. The students are divided into three groups that are similar within the group. By doing this, the learning task becomes adaptive, and appropriate learning tasks are adjusted for different levels of learners. Most of them believe that the adaptive learning system can help learners improve their learning efficiency and are very satisfied with the effectiveness of the system. The proportion of teachers who were satisfied with overall perception reached 89.1%, and the proportion of students who were satisfied was also high. The overall perceived effect of the improved teacher-student relationship is excellent, suggesting that the adaptive learning system can effectively improve the teacher-student relationship.

Cognitive diagnosis, as an important means of personalized learning, has the advantage of being able to diagnose students’ knowledge levels and individual differences and provide targeted learning services. In this paper, we use the Markov Monte Carlo method to estimate the parameters in the FuzzyCDF model. Then, we calculate students’ English proficiency level and knowledge mastery by constructing fuzzy sets to realize the diagnosis and analysis of students’ English cognitive levels. Finally, the teaching model of English education in colleges and universities is built using the cognitive diagnosis method. The study shows that the teaching model can accurately and dynamically detect students’ cognitive level for each knowledge point in English teaching. After the implementation of the teaching model, the average score of the overall knowledge water of the students in the experimental class in English teaching reaches 77.25. There is a highly significant difference between it and the students in the control class (P=0.001

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.

In this paper, firstly, the complementary grouping method is realized through the complementary grouping of cooperative shared learning supported by cognitive diagnosis, the knowledge relationship dependency graph construction, expected response pattern construction, and dynamic stratification process, and then the complementary grouping method is realized. Then, a cognitive mapping embedded resource sharing platform based on memory network and graph attention is proposed, including test factors based on graph attention and student factors based on memory network. The results show that in the FrcSub dataset DINA model deviates from the other two models in the 1st, 3rd, 8th, 12th, and 14th question prediction effects, and the overall question prediction performance is stable with a deviation of ±0.05. The cognitive mapping embedded resource sharing platform is efficient and feasible and also provides a new way of thinking for the development of macro education in universities.

Big data technology provides a detailed development of English language teaching, which is targeted through the assessment of cognitive level. In this paper, ConvSLSTM is used to describe the problem of learners’ knowledge level and build a cognitive framework. With the support of the framework, “recall rate” was introduced to quantify the English vocabulary test results, PSTM ability was adopted to reflect the acquisition of syntactic variants, and attention control was borrowed to evaluate pronunciation characteristics. Based on the initial quantification mentioned above, a hierarchical-level calculation was accomplished by combining fuzzy logic. Meanwhile, based on the estimation of different sides, a rule space model was created, and some corrections were made to achieve the cognitive diagnosis of the group level. In terms of the syntactic developmental trajectory of the girl learners, the maximum value of the range of syntactic variation of the subjects appeared in the middle and late stages. During the early stages of development, the variation range is between 10-35, and the bandwidth is 25, with very little variation. However, in the middle stage of development, it basically starts to oscillate significantly between 10-85, at which time the bandwidth increases to 75. Big data technology has created a comprehensive measurement framework for learners’ English proficiency.

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 technology-assisted 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 high-quality, 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.

Learning trajectory describes the student's learning progress or steps in one area through which teaching and learning can be linked. The cognitive diagnostic model (CDM) is an emerging evaluation theory in education measures. Researchers can determine students' mastery of fine-grained knowledge points by describing the learning trajectory based on CDM. The present study is based on the Trends in International Mathematics and Science Study (TIMSS) 2019, particularly, 21 mathematical items in the fourth grade. It analyzes the response data of 2,854 students from 17 countries from 10 attributes based on CDM. This study explores students' different learning trajectories in the content field by analyzing the relationships between knowledge states and attribute patterns. The study found that the 17 countries differ in learning trajectories but have commonalities. The learning starting points of 17 countries can be roughly divided into two categories and geometry attribute and fractions and decimals attribute are the last two attributes to master. [ABSTRACT FROM AUTHOR]

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In the realm of advanced technology, deep learning capabilities are harnessed to analyze and predict novel data, once it has absorbed existing information. When applied to the sphere of education, this transformative technology becomes a catalyst for innovation and reform, leading to advancements in teaching modes, methodologies, and curricula. In light of these possibilities, the application of deep learning technology to teaching resource recommendations is explored in this article. Within the context of the study, a bespoke recommendation algorithm for teaching resources is devised, drawing upon the integration of deep learning and cognitive diagnosis (ADCF). This intricately constructed model consists of two core elements: the Multi-layer Perceptron (MLP) and the Generalized Matrix Factorization (GMF), operating cohesively through stages of linear representation and nonlinear learning of the interaction function. The empirical analysis reveals that the ADCF model achieves 0.626 and 0.339 in the hits ratio (HR) and the Normalized Discounted Cumulative Gain (NDCG) respectively due to the traditional model, signifying its potential to add significant value to the domain of teaching resource recommendations. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis models (CDMs) are used in educational, clinical, or personnel selection settings to classify respondents with respect to discrete attributes, identifying strengths and needs, and thus allowing to provide tailored training/treatment. As in any assessment, an accurate reliability estimation is crucial for valid score interpretations. In this sense, most CDM reliability indices are based on the posterior probabilities of the estimated attribute profiles. These posteriors are traditionally computed using point estimates for the model parameters as approximations to their populational values. If the uncertainty around these parameters is unaccounted for, the posteriors may be overly peaked, deriving into overestimated reliabilities. This article presents a multiple imputation (MI) procedure to integrate out the model parameters in the estimation of the posterior distributions, thus correcting the reliability estimation. A simulation study was conducted to compare the MI procedure with the traditional reliability estimation. Five factors were manipulated: the attribute structure, the CDM model (DINA and G-DINA), test length, sample size, and item quality. Additionally, an illustration using the Examination for the Certificate of Proficiency in English data was analyzed. The effect of sample size was studied by sampling subsets of subjects from the complete data. In both studies, the traditional reliability estimation systematically provided overestimated reliabilities, whereas the MI procedure offered more accurate results. Accordingly, practitioners in small educational or clinical settings should be aware that the reliability estimation using model parameter point estimates may be positively biased. R codes for the MI procedure are made available [ABSTRACT FROM AUTHOR]