Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for LatentSkill Discovering

Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress.To this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural networks.First,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.

New development of cognitive diagnosis models

Cognitive diagnosis is the judgment of the student’s cognitive ability, is a wide-spread concern in educational science. The cognitive diagnosis model (CDM) is an essential method to realize cognitive diagnosis measurement. This paper presents new research on the cognitive diagnosis model and introduces four individual aspects of probability-based CDM and deep learning-based CDM. These four aspects are higher-order latent trait, polytomous responses, polytomous attributes, and multilevel latent traits. The paper also sorts on the contained ideas, model structures and respective characteristics, and provides direction for developing cognitive diagnosis in the future.

A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

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 ig-nore 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 random-ness of proficiency evolving to get both accurate and interpretable diagnosis results.Specifically,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness factors.Furthermore,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects 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 inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

Study of Priority Recommendation Method Based on Cognitive Diagnosis Model

In the context of personalized learning,the recommendation method aims to provide appropriate exercises for each student.And individualized knowledge status may give more effective recommendation.In this study,a priority recommendation method based on cognitive diagnosis model is proposed,and cosine similarity algorithm is applied to improve the accuracy and interpretability of recommendation.Then the performance of the methods was compared under cognitive diagnosis models.The experimental results show that the method proposed achieves more accurate results and better performance.

Learning Behavior-Aware Cognitive Diagnosis for Online Education Systems

Cognitive diagnosis,which aims to diagnose students’knowledge proficiency,is crucial for numerous online education applications,such as personalized exercise recommendation.Existing methods in this area mainly exploit students’exercising records,which ignores students’full learning process in online education systems.Besides,the latent relation of exercises with course structure and texts is still underexplored.In this paper,a learning behavior-aware cognitive diagnosis(LCD)framework is proposed for students’cognitive modeling with both learning behavior records and exercising records.The concept of LCD was first introduced to characterize students’knowledge proficiency more completely.Second,a course graph was designed to explore rich information existed in course texts and structures.Third,an interaction function was put forward to explore complex relationships between students,exercises and videos.Extensive experiments on a real-world dataset prove that LCD predicts student performance more effectively,the output of LCD is also interpretable.

Classification of Attribute Mastery Patterns Using Deep Learning

It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment. There are many methods to classify the attribute mastery patterns and many studies have been done to diagnose what the individuals have mastered and or Montel Carl Computer Simulation is used to study the classification of the attribute mastery patterns by Deep Learning. Four results were found. Firstly, Deep Learning can be used to classify the attribute mastery patterns efficiently. Secondly, the complication of the structures will decrease the accuracy of the classification. The order of the influence is linear, convergent, unstructured and divergent. It means that the divergent is the most complicated, and the accuracy of this structure is the lowest among the four structures. Thirdly, with the increasing rates of the slipping and guessing, the accuracy of the classification decreased in verse, which is the same as the existing research results. At last, the results are influenced by the sample size of the training, and the proper sample size is in need of deeper discussion.

KG-based memory recommendation algorithm for learning path

In intelligent education,most student-oriented learning path recommendation algorithms are based on either collaborative filtering methods or a 0-1 scoring cognitive diagnosis model.Unfortunately,they fail to provide a detailed report about the students’mastery of knowledge and skill and explain the recommendation results.In addition,they are unable to offer realistic learning path recommendations based on students’learning progress.Knowledge graph based memory recommendation algorithm(KGM-RA)was proposed to solve these problems.On the one hand,KGM-RA can provide more accurate diagnosis information by continuously fitting the students’knowledge and skill proficiency vector(SKSV)in a multi-level scoring cognitive diagnosis model.On the other hand,it also proposes the forgetting recall degree(FRD)according to the statistical results of the human forgetting phenomenon.It also calculates closeness centrality in the knowledge graph to achieve the recommended recall effect consistent with the human forgetting phenomenon.Experiments show that the KGM-RA can obtain the actual learning path recommendations for students,provides the adjustable ability of FRD,and has better reliability and interpretability.