Recommender Systems for university elective course recommendation

Bhumichitr, Kiratijuta; Channarukul, Songsak; Saejiem, Nattachai; Jiamthapthaksin, Rachsuda; Nongpong, Kwankamol

doi:10.1109/jcsse.2017.8025933

Cited by 49 publications

(35 citation statements)

References 5 publications

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“…As the online education market proliferates, online course websites such as edX, Coursera and K-MOOC are becoming widespread and seeing increasing numbers of subscribers. Therefore, the range of courserelated information available to users is increasing rapidly [6]. Several studies show that users face difficult situational problems due to the large amount of information available when choosing a course on an online education website [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Kim

2021

Applied Sciences

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Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Kim

2021

Applied Sciences

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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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“…Though the learners of COCO may not be representative of general learners in the recommender system, our analysis in "Exploratory Analysis" indicates that optimizing recommendation algorithms only for learners' interests may result in undermining other essential properties conveyed by the learning opportunities proposed to them. Ranges of educational recommender systems, such as those provided by Bridges et al (2018), Rieckmann (2018), and Bhumichitr et al (2017, can thus capitalize on our definitions, metrics, and procedures as a means for assessing recommendations' consistency. However, the principles proposed in this paper, derived from curriculum design beliefs, would need to be empirically-validated.…”

Section: Discussionmentioning

confidence: 99%

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Marras

Boratto

Ramos

et al. 2021

Int J Artif Intell Educ

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Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we shape a blueprint of the decisions and processes to be considered in the context of equality of recommended learning opportunities, based on principles that need to be empirically-validated (no evaluation with live learners has been performed). To this end, we first provide a formalization of educational principles that model recommendations’ learning properties, and a novel fairness metric that combines them to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. This paper provides a theoretical foundation for future studies of learners’ preferences and limits concerning the equality of recommended learning opportunities.

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Recommender Systems for university elective course recommendation

Cited by 49 publications

References 5 publications

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Recommendation Systems for Education: Systematic Review

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

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