Research on Teaching Resource Recommendation Algorithm Based on Deep Learning and Cognitive Diagnosis

With the increasing abundance of network teaching resources, the recommendation technology based on network is becoming more and more mature. There are differences in the effect of recommendation, which leads to great differences in the effect of recommendation algorithms for teaching resources. The existing teaching resource recommendation algorithm either takes insufficient consideration of the students’ personality characteristics, cannot well distinguish the students’ users through the students’ personality, and pushes the same teaching resources or considers the student user personality not sufficient and cannot well meet the individualized learning needs of students. Therefore, in view of the above problem, combining TDINA model by the user for the students to build cognitive diagnosis model, we put forward a model based on convolution (CUPMF) joint probability matrix decomposition method of teaching resources to recommend the method combined with the history of the students answer, cognitive ability, knowledge to master the situation, and forgetting effect factors. At the same time, CNN is used to deeply excavate the test question resources in the teaching resources, and the nonlinear transformation of the test question resources output by CNN is carried out to integrate them into the joint probability matrix decomposition model to predict students’ performance on the resources. Finally, the students’ knowledge mastery matrix obtained by TDINA model is combined to recommend corresponding teaching resources to students, so as to improve learning efficiency and help students improve their performance.

Cognitively Diagnostic Analysis Using the G-DINA Model in R

Cognitive diagnosis models (CDMs) have increasingly been applied in education and other fields. This article provides an overview of a widely used CDM, namely, the G-DINA model, and demonstrates a hands-on example of using multiple R packages for a series of CDM analyses. This overview involves a step-by-step illustration and explanation of performing Q-matrix evaluation, CDM calibration, model fit evaluation, item diagnosticity investigation, classification reliability examination, and the result presentation and visualization. Some limitations of conducting CDM analysis in R are also discussed.

A Deterministic Learning Algorithm Estimating the Q-Matrix for Cognitive Diagnosis Models

The goal of an exam in cognitive diagnostic assessment is to uncover whether an examinee has mastered certain attributes. Different cognitive diagnosis models (CDMs) have been developed for this purpose. The core of these CDMs is the Q-matrix, which is an item-to-attribute mapping, traditionally designed by domain experts. An expert designed Q-matrix is not without issues. For example, domain experts might neglect some attributes or have different opinions about the inclusion of some entries in the Q-matrix. It is therefore of practical importance to develop an automated method to estimate the Q-matrix. This research proposes a deterministic learning algorithm for estimating the Q-matrix. To obtain a sensible binary Q-matrix, a dichotomizing method is also devised. Results from the simulation study shows that the proposed method for estimating the Q-matrix is useful. The empirical study analyzes the ECPE data. The estimated Q-matrix is compared with the expert-designed one. All analyses in this research are carried out in R.

Deep learning assisted cognitive diagnosis for the D-Riska application

In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 % of Accuracy, 94 % of Precision, 91 %, of Recall and 92% of F1-Score.

Deep Learning Assisted Cognitive Diagnosis for the D-Riska Application

In this article, we expose a system developed that extends the Acquired Brain Injury (ABI) diagnostic application known as D-Riska with an artificial intelligence module that supports the diagnosis of ABI enabling therapists to evaluate patients in an assisted way. The application is in charge of collecting the data of the diagnostic tests of the patients, and due to a multi-class. Convolutional Neural Network classifier (CNN), it is capable of making predictions that facilitate the diagnosis and the final score obtained in the test by the patient. To find out the best solution to this problem, different classifiers are used to compare the performance of the proposed model based on various classification metrics. The proposed CNN classifier makes predictions with 93 \% of Accuracy, 94 \% of Precision, 91 \%, of Recall and 92\% of F1-Score.

Investigating the Relationships Between Listening Skills and Genre Competence Through Cognitive Diagnosis Approach

Although research on listening skills has been frequently conducted to discover the nature of listening comprehension, there is little study about listening genre competence which is related with knowledge about listening text types. In order to find out whether listening skills and listening genre competence are related, cognitive diagnosis, a quantitative method to disclose finer-grained latent attributes, was adopted in this study. The generalized deterministic inputs, noisy “and” gate (G-DINA) model, which takes attribute compensation and attribute interaction into consideration, was used to carry out cognitive diagnostic analysis. The listening comprehension subtest of Band 4 of Test for English Majors (TEM) which is a large scale English proficiency test for English Majors in China was used as the proficiency test for homogenizing the participants. Three genres in the subtest, dialog, lecture, and news, were investigated. The 2,285 subjects were sophomore English major college students and also test-takers of the same TEM4 examination. They were chosen by random sampling from the nationwide test population in China. The study analyzed three types of relationships between listening skills and genre competence. By analyzing how mastery of certain listening text genres goes with mastery of listening skills according to latent class distribution, the coexistence relationship was discovered. By comparing the average number of skills/genres mastered when the number of genres/skills mastered increases through One-Way ANOVA, compensatory and contributory relationships were revealed. The study also found that the subjects mastering Lecture genre got higher listening scores.