A Course Recommender System based on Graduating Attributes

Bakhshinategh, Behdad; Spanakis, Gerasimos; Zaı̈ane, Osmar R.; ElAtia, Samira

doi:10.5220/0006318803470354

Cited by 23 publications

(20 citation statements)

References 10 publications

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“…From the analysis, it can then be observed that RSs take into account user preferences when making suggestions based on recommendations from similar users, while [21,30,35,45,53] make recommendations based on learning style, and [24,31,46,50,61,64] based on diagnosis/student progress and the knowledge group. Likewise, another element they take into account are user skills and/or competences [40,42,56,69] and competences related to work associated with their profile within Internet job search portals.…”

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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“…CFRS-based course RS used different techniques to recommend courses to students such as; nearest neighbor and MF user-based CF to predict top-n courses for students [131], topic models to partition users based on demographic profiles, student interests and recommend courses based on the cluster [132] and recommending courses by calculating course score distribution of the most similar senior students [133]. Furthermore, one study results showed that better performance can be obtained from K-nearest neighbor by selecting neighborhood between 10 to 15 students [134].…”

Section: Collaborative Filtering-based Recommendation System (Cfrs)mentioning

confidence: 99%

“…Bozyigit et al [16] Cold-start problem is overcome by taking Weighted Averaging of most recent grades of the courses taken by the students Bakhshinategh et al [134] The results showed that better performance can be obtained by selecting the number of users in the neighborhood for K-nearest neighbor between 10 to 15 students Huang et al [133] Novel cross-user-domain CF by using course-score distribution of the most similar senior students, scalability issues is addressed by selecting the top t optional courses with the highest predicted scores. Extensive testing has been conducted.…”

Section: Collaborative Filtering-based Recommendation System (Cfrs)mentioning

confidence: 99%

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

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In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

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“…Neural Network and association rules were applied in [17], but in study [18] only the association rule was applied. Otherwise [19], researchers used the correlation threshold and nearest neighbor approach. [20] proposed a system recommending optional courses.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Recommender Systems for Matching Students with Suitable Specialization: An Exploratory Study at King Abdulaziz University

Alshaikh

Bahurmuz

Torabah

et al. 2021

Int. J. Emerg. Technol. Learn.

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In Saudi Arabia, all high school graduates who want join local universities have to go through a preparatory year before selecting their specific specialization/major. One of the most concerning issues for those fresh undergraduate college students is the selection of their specialization. College specialization selection is critical for them, as their academic and career future will be affected by this decision. An un-suitable specialization selection will have unfortunate consequences, not only on the students' future but also on the university’s resources and budget. This paper sug-gests a solution to this problem by introducing a preliminary study of a recommend-er system (RS), which will recommend the appropriate specialization for the students based on various tests and grades during the preparatory year at King Abdulaziz University (KAU). The proposed system guides students through their specialization selection process based on their abilities. The collaborative filtering technique was used to build the RS and K-fold cross-validation was adopted to evaluate its accura-cy and performance. The results showed the prediction of a specialization for each student with good accuracy ratio. These promising initial results provide a feasible solution to assess this issue further in future studies.

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A Course Recommender System based on Graduating Attributes

Cited by 23 publications

References 10 publications

Recommendation Systems for Education: Systematic Review

Recommendation Systems for Education: Systematic Review

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Using Recommender Systems for Matching Students with Suitable Specialization: An Exploratory Study at King Abdulaziz University

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