Learning Path Recommendation Based on Knowledge Tracing Model and Reinforcement Learning

Cai, Dehua; Zhang, Yuan; Dai, Bintao

doi:10.1109/iccc47050.2019.9064104

Cited by 22 publications

(9 citation statements)

References 4 publications

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“…In past, various algorithm was proposed which use the concept of collaborative filtering such as Partially Observable Markov Decision Process (POMDP) [140] recommends exercises. Similarly, Advantage Actor-Critic (A2C) -reinforcement learning method was build to recommend a personalized learning path according to the actual learning requirements of learner [141]. A graph-based recommendation system has also been proposed [142].…”

Section: E Personalized Recommender Systemmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: E Personalized Recommender Systemmentioning

confidence: 99%

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

et al. 2022

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“…The lack of accurate and descriptive information about each of the attributes in the datasets also hinders experiments. An example is in the ASSISTments datasets where terminology used is confusing 15 or lacking 16 . This may explain the different AUC values reported to the same approach and dataset pairs presented in Table 7.…”

Section: Considerationsmentioning

confidence: 99%

“…Such a graph is clustered into node groups and for each group a shared embedding is constructed using GNNs to get the final recommendations. Cai et al [15] followed an interactive recommendation approach in which a reinforcement learning recommender agent selects learning materials to recommend based on a reward signal calculated from the progress in a knowledge state estimated by a KT model. Huang et al [49] proposed an interactive educational video recommender model that follows a multi-objective setup of rewards.…”

Section: Recommender Systemsmentioning

confidence: 99%

Knowledge Tracing: A Survey

Abdelrahman¹,

Wang²,

Nunes³

2022

Preprint

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Humans' ability to transfer knowledge through teaching is one of the essential aspects for human intelligence. A human teacher can track the knowledge of students to customize the teaching on students' needs. With the rise of online education platforms, there is a similar need for machines to track the knowledge of students and tailor their learning experience. This is known as the Knowledge Tracing (KT) problem in the literature. Effectively solving the KT problem would unlock the potential of computer-aided education applications such as intelligent tutoring systems, curriculum learning, and learning materials' recommendation. Moreover, from a more general viewpoint, a student may represent any kind of intelligent agents including both human and artificial agents. Thus, the potential of KT can be extended to any machine teaching application scenarios which seek for customizing the learning experience for a student agent (i.e., a machine learning model). In this paper, we provide a comprehensive and systematic review for the KT literature. We cover a broad range of methods starting from the early attempts to the recent state-of-the-art methods using deep learning, while highlighting the theoretical aspects of models and the characteristics of benchmark datasets. Besides these, we shed light on key modelling differences between closely related methods and summarize them in an easy-to-understand format. Finally, we discuss current research gaps in the KT literature and possible future research and application directions.

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“…Cai developed a knowledge mapping and reinforcement learning-based technique for designing learning paths. Te algorithm can efectively predict students' learning progress, model learners' knowledge levels over time, and defne students' learning conceptual frameworks [6]. Zhu proposed using long-term and short-term virtual memory (LSTM) to resolve this issue and select a unique learning path from among the suggested solutions.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Learning Path for French Study in Colleges Based on a Big Data Knowledge Map

Xiao

2023

Scientific Programming

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The education industry is gradually improving with the rapid development of information technology. The learners use networks and computers to alter the traditional instructional framework based on educational information technology and achieve personalized learning. This teaching method emphasizes each learner’s identity and autonomy. However, due to the huge number of learning resources available on the Internet, students lack relevant courses, clear learning tasks, and the connection between various knowledge points, resulting in an unsatisfactory effect on the learning process. Knowledge maps for different learner types are created using historical learners’ conceptual knowledge and the segmentation and correlation technique of big data knowledge maps. Using a big data method in this process will automatically generate a set of weak conceptual learning pathways. For this problem, in the era of big data, people put forward the concept of knowledge map and used the algorithm based on the big data knowledge map to study the personalized learning path for college French. The content, structure, and relationship of college French knowledge points can be accurately expressed using this method, which is preferred by college administrators and teachers. This paper aims to investigate the personalized learning path for college French using a big data knowledge map, starting with the characteristics of a college French field of study. This study provides technical support in the establishment of a big data knowledge map based on a learning path recommendation framework. So, after the performance of several commonly used learning path recommendation algorithms, three French students have been selected at random for learning path planning. The results show that personalized learning path planning can be realized based on a knowledge map pre-repair relationship and objective attributes. In the analysis, not only the proposed technique is compared with the conventional optimization approach, but also a comparison study on the benefits of several learning effect prediction models is also performed. The results of this study suggest that this algorithm has a high learning efficiency and that the effective implementation of recommendations produced using our proposed strategy has a significant advantage.

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Learning Path Recommendation Based on Knowledge Tracing Model and Reinforcement Learning

Cited by 22 publications

References 4 publications

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

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

Knowledge Tracing: A Survey

A Personalized Learning Path for French Study in Colleges Based on a Big Data Knowledge Map

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