The Study of User Model of Personalized Recommendation System Based on Linked Course Data

2014 ◽  
Vol 519-520 ◽  
pp. 1609-1612
Author(s):  
Ji Yan Wu ◽  
Chan Le Wu
Keyword(s):  
Linked Data ◽  
Recommendation System ◽  
User Model ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learning Path ◽  
E Learning ◽  
Linked Courses ◽  
Efficient Learning ◽  
Massive Information

The massive information on Internet makes users obtain information become in efficient.Similarly,the users in the field of e-learning face with the problems that learning resources are inefficient and learning paths unreasonable,etc .Linked Courses data is a standard of W3C proposed will be organized the courses in the national excellent courses into a linked data database. On the basis the author design a user model to meet the user's learning requirements, which will be the most suitable learning Resources recommended to the user. Finally, the author validate the model with experimental ,the application shows that the model can indicate the user's learning requirements,and rational and efficient learning path is recommended.

scholarly journals Adaptive Learning Recommendation Strategy Based on Deep Q-learning

2019 ◽  
Vol 44 (4) ◽  
pp. 251-266 ◽  
Author(s):  
Chunxi Tan ◽  
Ruijian Han ◽  
Rougang Ye ◽  
Kani Chen
Keyword(s):  
Adaptive Learning ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Early Stopping ◽  
Q Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Efficient Learning ◽  
Recommendation Strategy ◽  
Full Utilization

Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.

scholarly journals Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaohua Fang ◽  
Qiuyun Lu
Keyword(s):  
Learning Process ◽  
Data Science ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
Learning Ability ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learner Model ◽  
Internet Education

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

Learning Path Recommendation System for Programming Education Based on Neural Networks

2020 ◽  
Vol 18 (1) ◽  
pp. 36-64 ◽  
Author(s):  
Tomohiro Saito ◽  
Yutaka Watanobe
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommendation System ◽  
Current Knowledge ◽  
Learning System ◽  
Machine Learning Techniques ◽  
Programming Education ◽  
Learning Path ◽  
E Learning ◽  
Trained Teachers

Programming education has recently received increased attention due to growing demand for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting this demand. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. This article proposes a learning path recommendation system that applies a recurrent neural network to a learner's ability chart, which displays the learner's scores. In brief, a learning path is constructed from a learner's submission history using a trial-and-error process, and the learner's ability chart is used as an indicator of their current knowledge. An approach for constructing a learning path recommendation system using ability charts and its implementation based on a sequential prediction model and a recurrent neural network, are presented. Experimental evaluation is conducted with data from an e-learning system.

Recommend Strategies for E-Learning 2.0 Communities

2014 ◽  
Vol 568-570 ◽  
pp. 1547-1550
Author(s):  
Bing Wu ◽  
Chen Yan Zhang
Keyword(s):  
Learning Communities ◽  
Knowledge Sharing ◽  
Recommendation System ◽  
Current Knowledge ◽  
Research Direction ◽  
Personalized Recommendation ◽  
Future Research ◽  
Rapid Progress ◽  
General Recommendation ◽  
E Learning

With the rapid progress of Web 2.0, E-Learning has evolved into E-Learning 2.0, which has been highlighted as an effective method for interactive learning. To improve the efficiency of learning, many researches focused on the personalized recommendation for knowledge sharing. However, these researches proposed the general recommendation system without considering the current knowledge sharing status in E-learning 2.0 communities. Therefore, the purpose of this study is to proposed recommend strategies according to characteristics of E-Learning communities based on social network analysis. Firstly, knowledge activity nodes in E-Learning communities are identified into four types. Secondly, based on four node types, E-Learning communities are classified into four corresponding types. Then, different recommend strategies are provided according to the types of E-Learning communities. Finally, conclusion and future research direction are discussed in the end.

scholarly journals Optimal Hierarchical Learning Path Design With Reinforcement Learning

2020 ◽  
Vol 45 (1) ◽  
pp. 54-70
Author(s):  
Xiao Li ◽  
Hanchen Xu ◽  
Jinming Zhang ◽  
Hua-hua Chang
Keyword(s):  
Reinforcement Learning ◽  
Learning System ◽  
Learning Material ◽  
Optimal Learning ◽  
Model Free ◽  
Learning Path ◽  
The Hierarchical Structure ◽  
E Learning ◽  
Efficient Learning ◽  
Skills Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.

Recommendation of sustainable economic learning course based on text vector model and support vector machine

2020 ◽  
pp. 1-11
Author(s):  
Xiangfei Ma
Keyword(s):  
Support Vector Machine ◽  
Recommendation System ◽  
Comprehensive Analysis ◽  
Information Space ◽  
Vector Model ◽  
Personalized Recommendation ◽  
Support Vector ◽  
Retrieval Accuracy ◽  
Recommendation Algorithms ◽  
Massive Information

The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses.

scholarly journals A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhihao Zhang
Keyword(s):  
Collaborative Filtering ◽  
Dimensional Space ◽  
Learning System ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Resources ◽  
Search Methods ◽  
E Learning ◽  
Predicted Values ◽  
Collaborative Knowledge

Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.

scholarly journals Design of personalized recommendation system for online learning resources based on improved collaborative filtering algorithm

2020 ◽  
Vol 214 ◽  
pp. 01051
Author(s):  
Baiqiang Gan ◽  
Chi Zhang
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Online Course ◽  
Learning Resources ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Open Courses ◽  
Online Learning Resources

In recent years, under the guidance of the educational concept of equality and sharing, universities at home and abroad have increased the development and application of online course learning resources. In China, online open courses are open to all learners on the platform of major portals. Due to the increasing number of online courses, it is increasingly difficult for learners to find the content they are interested in on the website. In addition, the traditional collaborative filtering has the problems of sparse data, cold start, and low accuracy of recommendation results, etc. Therefore, the personalized recommendation system studied in this paper adds the collaborative filtering recommendation technology of user and project attributes. The recommendation system can actively discover the interest of learners according to their behavior characteristics, and provide them with online learning resources of interest, and improve the accuracy of the recommendation results by improving the collaborative filtering algorithm. In this paper, personalized recommendation technology is applied to online course website, aiming at providing personalized, automated and intelligent recommendation system for online learners.

scholarly journals Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

2022 ◽  
Vol 16 ◽  
pp. 122-131
Author(s):  
Mingxia Zhong ◽  
Rongtao Ding
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learning Behaviors ◽  
Filtering Algorithm ◽  
Learning Platform ◽  
Collaborative Filtering Algorithm ◽  
Learning Platforms

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms.

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