An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests

Perumal, Sankar Pariserum; Ganapathy, Sannasi; Kannan, A.

doi:10.1007/s11227-019-02791-z

Cited by 42 publications

(16 citation statements)

References 40 publications

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“…Many scholars (Essadek & Rabeyron, 2020; Ghazawy et al, 2021; Nishimura et al, 2021; Rodríguez‐Rey et al, 2020; Villani et al, 2021; Wang, Hegde et al, 2020; Zhang et al, 2020) have used questionnaires to analyze the psychological impact of college students. Some scholars (Ansari et al, 2016; Antony Rosewelt et al, 2020; Dahdouh et al, 2019; Medio et al, 2020; Pariserum Perumal et al, 2019) have used artificial intelligence methods to study students' mental health, academic performance, and other performance and to analyze students' browsing behavior, reading characteristics, and hobbies. We believe that the integration of these two methods will produce better results.…”

Section: Discussionmentioning

confidence: 99%

“…As seen in Table 2, we have summarized five papers with the highest relevance to our study topic (Ansari et al, 2016;Antony Rosewelt et al, 2020;Dahdouh et al, 2019;Medio et al, 2020;Pariserum Perumal et al, 2019), indicating that the use of recommender systems in education has gradually gained popularity. The MoodleREC recommender system model was developed by Medio et al, 2020. To determine students' learning interests by the length of their stops in each module (Pariserum Perumal et al, 2019). The CodERS recommender system model studied by Ansari et al (2016) has proven the use of user behavior analysis (Dahdouh et al, 2019) to be highly effective in finding more suitable learning materials for students.…”

Section: Research Of Ai Recommendation System Applied In Education Fieldmentioning

confidence: 99%

“…The MoodleREC recommender system model was developed by Medio et al, 2020. To determine students' learning interests by the length of their stops in each module (Pariserum Perumal et al, 2019). The CodERS recommender system model studied by Ansari et al (2016) has proven the use of user behavior analysis (Dahdouh et al, 2019) to be highly effective in finding more suitable learning materials for students.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Exploration of college students' psychological problems based on online education under COVID‐19

Cai

Wang

Zou

2023

Psychology in the Schools

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As a result of the COVID‐19 pandemic, many college students have been isolated at home and unable to walk into class as usual. This series of protective measures to avoid the spread of the disease may have an additional psychological impact on the lives of college students. The purpose of this study was to propose a strategy for using an intelligent online learning system based on content recommendations and electronic questionnaires in the educational domain. We invited 3000 isolated university students (47.6% male and 52.4% female) to an online trial. It proved to be effective in helping us intervene in students' psychological problems quickly, objectively, efficiently, and in real‐time. In addition, our analysis of the data collected from the intelligent online learning system showed that the degree to which college students' psychological problems were affected by isolation was closely related to students' grade level, family background, major category, and computer proficiency. The current study suggests that the mental health of college students should be well monitored during segregation. Targeted psychological counseling is more necessary for students in upper grades, low‐income families, liberal arts majors, and those with weak computer proficiency to reduce the emotional impact of segregation on students.

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Section: Discussionmentioning

confidence: 99%

Section: Research Of Ai Recommendation System Applied In Education Fieldmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Exploration of college students' psychological problems based on online education under COVID‐19

Cai

Wang

Zou

2023

Psychology in the Schools

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“…Finally, they realized the system intervention mechanism based on the dashboard. Perumal et al [4] believed that the user preferences are crucial to the decision-making of intelligent learning system, and the system should provide appropriate learning content for users' dynamically changing learning preferences.…”

Section: Introductionmentioning

confidence: 99%

A study on the personalized learning path recommendation of intelligent learning system based on user behavior characteristic analysis

Zhao,

Deng

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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At present, the rapid development of Internet information technology has brought convenience to education and teaching. E-learning based on network has become an important way of learning. However, the traditional E-learning learning environment usually only provides course learning resources by the fixed preset program, which is not friendly to the user's personalized learning experience. Intelligent learning system (ILS) is a support environment that can dynamically provide personalized and intelligent learning services by diagnosing the individual differences of learners' users. It mainly provides current users with suitable learning paths or learning resources via personalized recommendation technology, so as to meet users' learning needs and improve their learning experience. However, the current research on intelligent learning system is still in the exploratory stage. Many researchers have studied intelligent learning system from different perspectives, but their research achievements still need to be explored and improved on the effect of intelligent recommendation. Based on this, this paper will further explore the key technology of personalized recommendation of intelligent learning system on the basis of the related research case analysis. In order to improve the recommendation accuracy of intelligent learning system, we will model and analyze the user behavior characteristics, and further explore the recommendation solution of personalized learning path of the system. The simulation experiment shows that the research results have a good personalized recommendation effect, which can effectively diagnose and analyze the current user's learning state characteristics, and then dynamically provide a suitable personalized learning path to meet the user's learning needs.

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“…Therefore, the aim of this study is to use a hybrid collaborative filtering algorithm to solve the recommendation cold-start problem for elderly users in a public elderly service system, so as to achieve personalised recommendation results that meet users' needs. There are currently more studies using collaborative filtering algorithms to complete user personalised recommendation services [ 24 , 25 ]. Park et al [ 26 ] applied collaborative filtering algorithms to a travel recommendation system to provide effective travel paths and tourist attractions based on user preferences.…”

Section: Introductionmentioning

confidence: 99%

Design of Key Technologies for Elderly Public Network Services Based on Intelligent Recommendations

Zhang

2022

Computational Intelligence and Neuroscience

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As the world’s population continues to increase, the proportion of elderly people is also rising. The existing elderly public service system is no longer able to meet the needs of the elderly for their daily lives. The elderly population is significantly less receptive to emerging matters than the younger population, resulting in the public elderly service system not being able to access the initial data of elderly users in a timely manner, which causes the system to make incorrect recommendations. Therefore, the elderly cannot enjoy all kinds of online services provided by the Internet platform. In order to solve this problem, an elderly intelligent recommendation method based on hybrid collaborative filtering is proposed. First, the data of elderly users and elderly service items are scored, and modelling is completed by a collaborative filtering algorithm. Then, the XGBoost model is combined to solve the optimal objective function, so that the recommended data set with the highest score in the nearest neighbour set is obtained. The experimental results show that the proposed hybrid algorithm effectively solves the cold start phenomenon that occurs when the elderly population uses the web to make recommendations for elderly services. In addition, the proposed hybrid algorithm has a higher recommendation footprint accuracy than other recommendation algorithms.

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An intelligent fuzzy rule-based e-learning recommendation system for dynamic user interests

Cited by 42 publications

References 40 publications

Exploration of college students' psychological problems based on online education under COVID‐19

Exploration of college students' psychological problems based on online education under COVID‐19

A study on the personalized learning path recommendation of intelligent learning system based on user behavior characteristic analysis

Design of Key Technologies for Elderly Public Network Services Based on Intelligent Recommendations

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