Time-and-Concept Enhanced Deep Multidimensional Item Response Theory for interpretable Knowledge Tracing

Su, Yu; Cheng, Zeyu; Luo, Pengfei; Wu, Jinze; Zhang, Lei; Liu, Qi; Wang, Shijin

doi:10.1016/j.knosys.2021.106819

Cited by 26 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, input the features of the student response data, use the histogram algorithm to find the feature with the greatest gain, and determine the optimal splitting point of the decision tree based on this feature; use the Leafwise leaf growth strategy with depth limitation to generate the CART tree; then calculate the residual value of the CART tree, use the residual result of the previous tree as the training sample, train the next CART tree, and repeat the training; finally, the CART tree generated by each training is weighted and summed to obtain the final prediction model. e CART trees generated from each training session are weighted and summed to obtain the final prediction model [12]. e LightGBM algorithm measures the importance of a feature attribute based on the total number of times the feature is used as a segmentation point.…”

Section: Multifeature Selection Algorithmmentioning

confidence: 99%

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

Chuan

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

As the mainstream of Confucianism, the Song and Ming philosophies played an irreplaceable role in the 800 years of history from the Song dynasty to the end of the Qing dynasty. The theoreticians were concerned with the principles of heaven, nature, and physics and especially with the principles of human nature, which changed in the Qing Dynasty to a practice-oriented way of thinking. However, the practical thinking of Qing dynasty Neo-Confucianism is difficult to apply to practice because of the obscure text and the limitations of the time period, so it is worth further discussing how to deconstruct and promote the learning of Qing dynasty practical thinking under the new technology. This paper systematically reviews the ideological origins and political practices of Qing dynasty Neo-Confucianism and proposes a knowledge-tracking model around deep learning technology, which not only provides a contemporary technological tool for deconstructing the practical thinking of Qing dynasty Neo-Confucianism but also provides an application path for the integration of technology and knowledge.

show abstract

Section: Multifeature Selection Algorithmmentioning

confidence: 99%

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

Chuan

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Item Response Theory (IRT) refers to the evaluation of all learners through the same scale while attempting dissimilar assessments by linking the learners' assessment scores. Deep Learning based IRT implementations have been widely employed to provide thorough insights about the learners abilities in adaptive E-learning systems [60] [61]. Many deep learning based frameworks have been developed in order to track and evaluate the knowledge progression of each learner [62].…”

Section: ) Item Response Theorymentioning

confidence: 99%

AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

et al. 2022

View full text Add to dashboard Cite

A personalized e-learning system is effective in imparting enhanced learning to its users. As compared to a conventional e-learning system, which provides similar contents to each learner, a personalized learning system provides specific learning contents and assessments to the learners. Personalization is based on Artificial Intelligence (AI) based techniques in which appropriate contents for each learner are determined using the level of comprehension of the learner and the preferred modes of learning. This paper presents requirements and challenges for a personalized e-learning system. The paper is focused in elaborating four research questions, which are related to identifying key factors of personalized education, elaborating on state of the art research in the domain, utilizing benefits of AI in personalized education, and determining future research directions. The paper utilizes an extensive survey of current research papers in answering these questions. It provides a comprehensive review of existing solutions in offering personalized e-learning solutions. It also elaborates on different learning models and learning theories, which are significant in providing personalized education. It proposes an efficient framework, which can offer personalized elearning to each learner and identifies significant directions for future research. The paper is beneficial for academicians and researchers in understanding the requirements of such a system, comprehending its methodologies, and identifying challenges which are needed to be addressed.

show abstract

“…On the basis of IRT, the students' knowledge state cognitive model based on factor analysis was later proposed: LFA [ 28 ] and PFA [ 29 ]. These logistic regression models predict students' mastery of knowledge concepts by analyzing the relationship among factors that have an impact on students' answering accuracy [ 30 , 31 ].…”

Section: Related Workmentioning

confidence: 99%

NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills

Qiang

Sun³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Knowledge tracing (KT) is the task of modelling students’ knowledge state based on their historical interactions on intelligent tutoring systems. Existing KT models ignore the relevance among the multiple knowledge concepts of a question and characteristics of online tutoring systems. This paper proposes a neural Turing machine-based skill-aware knowledge tracing (NSKT) for conjunctive skills, which can capture the relevance among the knowledge concepts of a question to model students’ knowledge state more accurately and to discover more latent relevance among knowledge concepts effectively. We analyze the characteristics of the three real-world KT datasets in depth. Experiments on real-world datasets show that NSKT outperforms the state-of-the-art deep KT models on the AUC of prediction. This paper explores details of the prediction process of NSKT in modelling students’ knowledge state, as well as the relevance of knowledge concepts and conditional influences between exercises.

show abstract

Time-and-Concept Enhanced Deep Multidimensional Item Response Theory for interpretable Knowledge Tracing

Cited by 26 publications

References 18 publications

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills

Contact Info

Product

Resources

About