Intelligent personalised exercise recommendation: A weighted knowledge graph‐based approach

Lv, Pin; Wang, Xiaoxin; Xu, Jia; Wang, Junbin

doi:10.1002/cae.22395

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…They exploit knowledge graphs to get the semantic information of learning resources embedded into a low dimensional space, and fuse the semantic similarity between resources into the CF recommendation algorithms to improve the recommendation performance. Considering that the existing research on exercise recommendation only analyzes the learners' cognitive ability and ignores the sequential relationship of knowledge concepts in the learning process, Lv et al [19] present an exercise recommendation method based on weighted knowledge graphs, in which the knowledge concepts are modeled as nodes and weighted with the corresponding learner's knowledge status. All the above approaches use a small volume of data to construct knowledge graphs and fail to discuss the calculation efficiency problem with graph data in real datasets [9].…”

Section: Learning Resource Recommendation Via Knowledge Graphsmentioning

confidence: 99%

Learning resource recommendation via knowledge graphs and learning style clustering

Tang

Zhang

et al. 2023

IFS

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An e-learning system should recommend learners appropriate learning resources according to their actual needs and cognitive status for improving their learning performance. To overcome the deficiencies of existing approaches (e.g., poor interpretability, limited efficiency and accuracy of recommendation), we propose a new recommendation approach to learning resources via knowledge graphs and learning style clustering. In this approach, the knowledge graphs of an online learning environment are constructed based on a generic ontology model, and the graph embedding algorithm and graph matching process are applied to optimize the efficiency of graph computation for identifying similar learning resources. By introducing learning style theory, learners are clustered based on their learning styles. Based on the clustering results, the learners’ degrees of interest in similar learning resources are measured, and the recommendation results are obtained according to the degrees of interest. Finally, the experiments demonstrate that the proposed approach significantly enhances the computational efficiency and the quality of learning resource recommendation compared with the existing approaches in large-scale graph data scenarios.

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Section: Learning Resource Recommendation Via Knowledge Graphsmentioning

confidence: 99%

Learning resource recommendation via knowledge graphs and learning style clustering

Tang

Zhang

et al. 2023

IFS

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“…In addition, Wu et al [24] designed a novel recommendation approach that can recommend the exercise of a given difficulty without setting the difficult level of each exercise. Most previous deep recommendation methods ignore the essential relationships between knowledge points, Lv et al [25] proposed a weighted knowledge graph recommendation framework, wherein takes the knowledge concept weighted by the ability of a student as entities, and an arrowed edge between two knowledge concepts represents their prerequisite relationship. The authors in [26] used the learning-related contextualized factors plus a personalization mechanism to enhance the students' knowledge.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning

Zhang

Shang

Ren

et al. 2022

Complex Intell. Syst.

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Sequence-wise recommendation, where recommend exercises to each student step by step, is one of the most exciting tasks in the field of intelligent tutoring systems (ITS). It is important to develop a personalized sequence-wise recommendation framework that immerses students in learning and helps them acquire as much necessary knowledge as possible, rather than merely focusing on providing non-mastered exercises, which is referred to optimize a single objective. However, due to the different knowledge levels of students and the large scale of exercise banks, it is difficult to generate a personalized exercise recommendation for each student. To fully exploit the multifaceted beneficial information collected from e-learning platforms, we design a dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning, i.e., DMoSwR-DRL, which automatically select the most suitable exercises for each student based on the well-designed domain-objective rewards. Within this framework, the interaction between students and exercises can be explicitly modeled by integrating the actor–critic network and the state representation component, which can greatly help the agent perform effective reinforcement learning. Specifically, we carefully design a state representation module with dynamic recurrent mechanism, which integrates concept information and exercise difficulty level, thus generating a continuous state representation of the student. Subsequently, a flexible reward function is designed to simultaneously optimize the four domain-specific objectives of difficulty, novelty, coverage, and diversity, providing the students with a trade-off sequence-wise recommendation. To set up the online evaluation, we test DMoSwR-DRL on a simulated environment which can model qualitative development of knowledge level and predicts their performance for a given exercise. Comprehensive experiments are conducted on four classical exercise-answer datasets, and the results show the effectiveness and advantages of DMoSwR-DRL in terms of recommendation quality.

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“…They jointly model current abilities and future goals and, through a unique mechanism for identifying needs, obtain the development needs of different learners, thereby recommending the most suitable course. Lv et al [17] proposed a knowledge point recommendation method based on a weighted knowledge graph, where each node represents a knowledge point and is weighted based on students' mastery level. Wan et al [18] designed a learner impact model (LIM) to capture learners' interpersonal relationships, optimized the learner impact model using intuitionistic fuzzy logic, and found the most suitable learner through self-organization.…”

Section: Introductionmentioning

confidence: 99%

Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering

Niu,

Lin,

Xue

2023

Applied Sciences

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This study aims to solve the problem of limited learning efficiency caused by information overload and resource diversity in online course learning. We adopt a recommendation algorithm that combines knowledge graph and collaborative filtering, aiming to provide an application that can meet users’ personalized learning needs and consider the semantic information of learning resources. In addition, this article collects and models implicit data in online courses and compares the impact of video and text learning resources on user learning needs under different weights in order to deeply understand the different contributions of video and text learning resources to meeting learning needs. The experimental results show that the video high-weight experimental group performs better than the text high-weight experimental group; students tend to prefer video resources. This experiment can help students cope with the challenges brought by numerous types of learning resources and provide personalized and high-quality learning experiences for learners. At the same time, adjusting and innovating teaching models for teachers has great reference value.

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Intelligent personalised exercise recommendation: A weighted knowledge graph‐based approach

Cited by 10 publications

References 29 publications

Learning resource recommendation via knowledge graphs and learning style clustering

Learning resource recommendation via knowledge graphs and learning style clustering

Dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning

Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering

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