Supporting Users of Open Online Courses with Recommendations: An Algorithmic Study

Fazeli, Soude; Rajabi, Enayat; Lezcano, Leonardo; Drachsler, Hendrik; Sloep, Peter

doi:10.1109/icalt.2016.119

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…In terms of RSs that use the collaborative approach, they are thought to evidence the cold-start problem, with works [36,41,56] suggesting a greater volume of data to improve performance, and [22,48] adding more parameters to the user profile, such as learning styles or reading tastes-in general, it is suggested that this be combined with other approaches in order to improve performance. Ref.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Recommendation Systems for Education: Systematic Review

2021

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“…Even if the information is found, it may not be the solution to the real cause of the problem; moreover, it may not be completely understood because the basics are not complete. For students who are just beginning, this may cause a confidence blow, and reduce their willingness to learn (Fazeli et al, 2016;Long, 2016). Therefore, through the Bayesian classifier, the points that will potentially cause problems among the entire learning data set are classified, labels and weights are set to determine the possible probabilities of the causes of the problems encountered by the students, and the probability and weight are used to determine the ultimate causes of the problems.…”

Section: Bayesian Algorithmmentioning

confidence: 99%

Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning

Yao

2022

International Journal of Information and Communication Technology Education

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With the impacts of Covid-19 epidemic, e-learning has become a popular research issue. Therefore, how to upgrade the interactivity of e-learning, and allow learners to quickly access personalized and popular learning information from huge digital materials, is very important. However, chatbots are mostly used in automation, as well as simple occasions of general standard question and answer. But to solve the different problems of e-learners in the learning process, chatbots are used to filter the blind spots of learners and to provide further relevant information, so that e-learning can improve in efficiency and interactivity. This study utilizes AI, two-stage Bayesian algorithm, and crawler technology to provide customized learning materials according to learner's current learning situation. The experimental results show that this research system can indeed correctly understand and judge the blind spots of digital learners, and effectively find the relevant e-learning and video information. The accuracy rate reaches nearly 90%.

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“…For instance, to calculate the sub-similarity "how to learn" between users, we consider the vectors of both users in the Boolean user-LO matrix and compute the similarity using the Jaccard coefficient. We use this coefficient because the matrix is the same of the user-item rating matrix in the User-based Nearest Neighbor recommendation approach, and [Fazeli et al 2016] showed that this measure is better for LO recommendation over implicit feedback. In Subsection 4.2, we define how to compute each sub-similarity in the MERLOT e-learning platform.…”

Section: A) Multidimensional User Preference Modelmentioning

confidence: 99%

Recommender System for Learning Objects based in the Results of Learner Choices in E-learning Systems - Application in the MERLOT Platform

Dias¹,

Wives²

2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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In this paper, we briefly review the recommendation approach of learning objects (LOs) that uses the result of the choices made by the learner user during learning (“what to learn”, “how to learn”, etc.) as a source of information. This LO recommendation approach is an implementation of collaborative filtering based on an instance-based machine learning method. The goals of this paper are: to present how to apply this LO recommendation approach in an e-learning platform used for non-formal learning – the MERLOT platform, and to perform an experimental evaluation on the MERLOT’s dataset. The evaluation showed that the LO recommendation approach presents higher prediction accuracy than the baseline approaches.

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Supporting Users of Open Online Courses with Recommendations: An Algorithmic Study

Abstract: Abstract-

Cited by 12 publications

References 16 publications

Recommendation Systems for Education: Systematic Review

Recommendation Systems for Education: Systematic Review

Intelligent and Interactive Chatbot Based on the Recommendation Mechanism to Reach Personalized Learning

Recommender System for Learning Objects based in the Results of Learner Choices in E-learning Systems - Application in the MERLOT Platform

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