Intelligent learning system based on personalized recommendation technology

Li, Hui; Li, Haining; Zhang, Shu; Zhong, Zhaoman; Cheng, Jia-Hui

doi:10.1007/s00521-018-3510-5

Cited by 49 publications

(30 citation statements)

References 12 publications

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“…Of the studies reviewed, 21 adopted an online platform for implementation purposes (Table 9), of which [19,20,33,41,55,73,75,77,80,82,83,85,87,88,103,116] used universities/university platforms as a source of data, and [38,44,60,72,92,94,109,117] used learning platforms. Moodle was used in the case of [24,66], while four of the recommendation systems sought information from online resources, namely [47,62,84,95].…”

Section: Type Of Platform Implemented In Recommendation Systemsmentioning

confidence: 99%

Recommendation Systems for Education: Systematic Review

2021

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Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

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Section: Type Of Platform Implemented In Recommendation Systemsmentioning

confidence: 99%

Recommendation Systems for Education: Systematic Review

2021

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“…v j  {1, ...,7} is the value selected for question j. We used the Cronbach-α coefficient [64] method shown in (14) to verify the sample's reliability.…”

Section: Data Acquisition and Quantitative Definitionmentioning

confidence: 99%

“…In recent years, the research on teaching recommendations can be summarized as follows： (ⅰ) Teaching resources recommendation. Li et al [14] propose a hybrid recommendation algorithm based on content recommendation and collaborative filtering (CF) recommendation algorithm to implement the personalized intelligent educational resources recommendation system (RS). Oliveira et al [15] customized content recommendations according to each student's style.…”

Section: Introductionmentioning

confidence: 99%

A Course Teacher Recommendation Algorithm Based on Improved Latent Factor Model and PersonalRank

Yao

Deng

2021

IEEE Access

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To scientifically and accurately recommend suitable teachers for university courses and improve teaching quality, designing an effective recommendation algorithm is necessary. Therefore, we construct quantitative models of teacher characteristics, course characteristics, and teaching evaluations under the theories and methods of education and build a sparse experimental data matrix based on the quantified data. On this basis, we propose a teacher recommendation algorithm (PRLFM) based on the improved latent factor model (LFM) and the improved PersonalRank algorithm. Firstly, the improved LFM is used to predict the evaluation scores of those courses that teachers have not taught. The scores which are higher than the specified threshold are used to fill the corresponding missing items in the sparse matrix to reduce the matrix's sparsity. Then, the bipartite graph model based on the teacher set and course set is constructed according to the filled experimental data matrix. The weight of edges in the bipartite graph is replaced by the teacher and course's evaluation score multiplied by the course difficulty, which reflects the correlation between course and evaluation score. Next, an improved probability transition matrix based on the bipartite graph is constructed. The access probability in the matrix is replaced by the node's out degree's reciprocal multiplied by the edge's weight. The correlation degree between the course and all teachers is quickly calculated using the matrix algorithm of PersonalRank. Finally, a teacher recommendation model is constructed to realize teachers' top-N recommendation by combining the correlation degree with teachers' characteristics. Experiments show that the PRLFM algorithm can effectively improve the accuracy of prediction and top-N recommendation. It solves the problem of lack of scientific basis in recommending suitable teachers for university courses and improving the teaching quality.

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“…As shown in Figure 2, the system is divided into three parts: course management, user management, and comment management. is paper focuses on the personalized online learning platform based on collaborative filtering algorithms, with the main goal of breaking the limitations of time and space, increasing the ways and means of learning, and using novel user enthusiasm for learning and improving the efficiency of user learning [25]. Online learning is one of the more popular ways of learning that allows students who do not have the time or the conditions to attend face-to-face courses to understand and learn what they need to know through online courses.…”

Section: Online Education System Designmentioning

confidence: 99%

Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm

Zhou

2020

Complexity

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In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online.

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Intelligent learning system based on personalized recommendation technology

Cited by 49 publications

References 12 publications

Recommendation Systems for Education: Systematic Review

Recommendation Systems for Education: Systematic Review

A Course Teacher Recommendation Algorithm Based on Improved Latent Factor Model and PersonalRank

Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm

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