A multi-constraint learning path recommendation algorithm based on knowledge map

With a rapid development of Internet, E-Learning is becoming a new learning mode. E-Learning is not limited by time and space. It also has a large number of on-line learning resource. However, it has many disadvantages for students, such as information overload, disorientation, low learning efficiency, low user satisfaction and so on. Our aim is to improve learning efficiency and user satisfaction by overcoming information overload and disorientation of E-Learning system. This paper proposes an algorithm by combining Spreading-Activation Theory and techniques of classifying and sorting knowledge. The algorithm can generate a near optimal navigation learning path(NLP) based on a student's target knowledge unit(TKU) and knowledge map(KM) which it belongs to. NLP provides students an appropriate learning instruction to effectively eliminate disorientation during the process when they are learning interested knowledge units. The essential tasks of the algorithm is to filter redundant information and sort candidate knowledge units. So its realization process can be divided into three phrases: first, generating candidate complement map to overcome information overload. Because the candidate complement map only contains essential candidate knowledge units and learning dependencies among them to master TKU. Second, constructing learning features to discrete the candidate complement map to implement techniques of sorting knowledge conveniently. Final, sorting candidate knowledge units to get an appropriate NLP by using a Secondary Sort Strategy(SSS). The experimental results have shown that our method is sound for improving learning efficiency and users' satisfaction.

Download Full-text

Text Content Tag Recommendation Algorithm Based on Knowledge Map Fusion Reasoning

Proceedings of the 2021 International Conference on Control and Intelligent Robotics ◽

10.1145/3473714.3473843 ◽

2021 ◽

Author(s):

Wen Mi ◽

Zhu Mu-qing

Keyword(s):

Knowledge Map ◽

Recommendation Algorithm ◽

Tag Recommendation ◽

Text Content

Download Full-text

Research of Learning path recommendation algorithm based on Knowledge Graph

Proceedings of the 6th International Conference on Information Engineering - ICIE '17 ◽

10.1145/3078564.3078567 ◽

2017 ◽

Author(s):

Yanru Zhu ◽

Peng Wang ◽

Yaqin Fan ◽

Ying Chen

Keyword(s):

Knowledge Graph ◽

Recommendation Algorithm ◽

Learning Path

Download Full-text

Learning Path Recommendation Method Based on Knowledge Map

Handbook of Research on Managerial Practices and Disruptive Innovation in Asia - Advances in Business Strategy and Competitive Advantage ◽

10.4018/978-1-7998-0357-7.ch009 ◽

2020 ◽

pp. 171-184

Author(s):

Xiaodong Zhou ◽

Yi Li ◽

Liping Yuan ◽

Gaofeng Ma ◽

Xinyun Tan ◽

...

Keyword(s):

Resource Allocation ◽

Social Resources ◽

Knowledge Map ◽

Knowledge And Skills ◽

Learning Path ◽

Analysis Of Knowledge

With the development of society, many industries and professions are more comprehensive and intersecting. Different industries have their own requirements for students with comprehensive backgrounds. For graduates, they may not know the skills required for various occupations, or what kind of jobs and occupations they can take based on their existing knowledge and skills, even how to acquire these abilities after they know the requirements of the jobs they want. In this chapter, authors combine the existing method to predict hot jobs with the analysis of knowledge map, aiming to achieve accurate recommendation of learning path for those who want to find a job. This chapter will help job hunters gradually master skills, and ultimately achieve the goal of optimizing resource allocation and saving social resources.

Download Full-text

Optimization of English Learning Platform Based on a Collaborative Filtering Algorithm

Complexity ◽

10.1155/2021/6624012 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Jiali Tang

Keyword(s):

Collaborative Filtering ◽

Cognitive Ability ◽

Recommendation System ◽

Knowledge Map ◽

English Learning ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Learning Platform ◽

Depth Analysis ◽

Collaborative Filtering Algorithm

This paper provides a detailed description of the recommendation system and collaborative filtering algorithm to optimize the English learning platform through the collaborative filtering algorithm and analyses the algorithmic principles and specific techniques of collaborative filtering. After introducing the recommendation system and collaborative filtering algorithm, this paper elaborates on the theoretical basis and technical principles of the recommendation algorithm based on cognitive ability and difficulty and provides an in-depth analysis of the design and implementation of the recommendation algorithm by combining cognitive diagnosis theory, readability formula, and English knowledge map, which provides a comprehensive and solid theoretical guidance and support for the application development of the online English learning platform. The system is tested by building a Spring Cloud platform, importing actual business data, focusing on the validation of the recommendation model, and connecting the recommendation system to the formal production system to analyse the recommendation effect. Compared with the original recommendation method, the online English learning platform designed and implemented in this paper based on the cognitive ability and difficulty collaborative filtering recommendation algorithm has a better recommendation effect. The system is proved to be well designed and has certain reference and guiding value for the whole web-based online learning platform and has a broader application prospect nowadays and in the future.

Download Full-text

A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map

IEEE Access ◽

10.1109/access.2018.2873106 ◽

2018 ◽

Vol 6 ◽

pp. 57562-57571 ◽

Cited By ~ 3

Author(s):

Haiping Zhu ◽

Yu Liu ◽

Feng Tian ◽

Yifu Ni ◽

Ke Wu ◽

...

Keyword(s):

Knowledge Map ◽

Recommendation Algorithm ◽

Video Recommendation ◽

Associated Knowledge

Download Full-text

Knowledge Graph Entity Similarity Calculation under Active Learning

Complexity ◽

10.1155/2021/3522609 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Lianhuan Li ◽

Zheng Zhang ◽

Shaoda Zhang

Keyword(s):

Adaptive Learning ◽

Knowledge Graph ◽

Knowledge Map ◽

Alignment Algorithm ◽

Learning Resources ◽

Learner Model ◽

Learning Platform ◽

Learning Path ◽

Alignment Algorithms ◽

Starting Point

To address the objectives of the adaptive learning platform, the requirements of the system in terms of business, functionality, and performance are mainly analysed, and the design of functions and database is completed; then, an updatable learner model is constructed based on the cognitive diagnosis model and resource preference attributes; then, the construction of the knowledge map is completed based on embedding to achieve knowledge point alignment, and based on this, the target knowledge points of learners are located with the help of deep learning; at the same time, the target knowledge points are taken as the starting point to generate the best learning path by traversing the knowledge map, and the corresponding learning resources and test questions are recommended for them with the help of the architecture; finally, the adaptive learning platform is developed in the environment using the architecture. Also, the target knowledge point is used as the starting point to traverse the knowledge map to generate the best learning path, and the corresponding learning resources and test questions are recommended for the learner in combination with the learner model; finally, this study adopts an architecture for the development of an adaptive learning platform in the environment to realize online tests, score analysis, resource recommendation, and other functions. A knowledge graph fusion system supporting interactive facilitation between entity alignment and attribute alignment is implemented. Under a unified conceptual layer, this system can combine entity alignment and attribute alignment to promote each other and truly achieve the final fusion of the two graphs. Our experimental results on real datasets show that the entity alignment algorithm proposed in this paper has a great improvement in accuracy compared with the previous mainstream alignment algorithms. Also, the attribute alignment algorithm proposed in this paper, which calculates the similarity based on associated entities, outperforms the traditional methods in terms of accuracy and recall.

Download Full-text