A course recommendation system based on grades

Mondal, Bhaskar; Patra, Om; Mishra, Sanket; Patra, Priyadarsan

doi:10.1109/iccsea49143.2020.9132845

Cited by 35 publications

(15 citation statements)

References 13 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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“…Machine learning was also found in literature to recommend courses. Aher and Lobo [55], Li et al [118] and Mondal et al [155] [167] proposed Novel online recommendation algorithm for course recommendation. Hybrid approach in to recommend courses were also found in the literature.…”

Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?mentioning

confidence: 99%

“…[113] utilized improved apriori algorithm, [145] Xia used vector space model (VSM) and finally Mondal et. al [155] used data mining techniques to achieve course recommendation.…”

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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Online learning environments (OLE) are gaining popularity, including learning management systems (LMS) and massive open online courses (MOOCs), which are the best modern alternate solutions available for education in the current era. The luxury to learn irrespective of geographical and temporal restrictions makes it an attractive resource. At the start of 2020, the global pandemic enforced social distance practice worldwide, changing the work environment dynamics, leaving the people with options like online trading, work from home, and online education. The online learning environments gained particular attention in the educational sector, where users could access the online learning resources to fulfil their academic requirements during the lockdown. From massively available content such as MOOC, the learners are overwhelmed with the available choices. In this scenario, recommender systems (RS) come to the rescue to help the learner make appropriate choices for completing the enrolled course. There is tremendous scope and a multitude of opportunities available for researchers to focus on this domain. An exhaustive analysis is required to spotlight the opportunities in this realm. Various studies have been performed to provide such solutions in multiple areas of the MOOC recommendation systems (MOOCRS) such as course recommendation, learner peer recommendation, resource recommendations, to name a few. This is a compendious study into the research conducted in this area, identifying 670 articles out of 116 selected for analysis published from 2013 to 2021. It also highlights multiple areas in MOOC, where the recommendation is required, as well as technologies used by other researchers to provide solutions over time.

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“…However, one limitation of this work is that it only considered one unsupervised clustering algorithm. Mondal et al (2020) also proposed the use of ML algorithms as part of a course recommendation system for online learning environments. More specifically, the authors first used K-means algorithm to group students based on their performance in previous courses.…”

Section: Previous Workmentioning

confidence: 99%

“…Combined k-means and apriori algorithm to recommend courses (Aher and Lobo, 2013) -Evaluate the courses that other students have taken that are related to the skill the student is interested in using multiple metrics. Used a combination of K-means algorithm and collaborative filtering for course recommendation (Mondal et al, 2020) -Consider multiple supervised classification algorithms to study their impact on the effectiveness of recommendation process. Used an improved version of Apriori association rules algorithm for course recommendation (Zhang et al, 2018a) Personalized Learning Combined a DT algorithm and induction rules algorithm to provide learning-pathway recommendations (Elfaki et al, 2014) -Study the performance of different classification algorithms to predict student performance during the course delivery.…”

Section: Course Recommendationmentioning

confidence: 99%

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

Injadat,

Moubayed,

Nassif

et al. 2021

Preprint

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The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.

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A course recommendation system based on grades

Cited by 35 publications

References 13 publications

Recommendation Systems for Education: Systematic Review

Recommendation Systems for Education: Systematic Review

A Systematic Mapping Review on MOOC Recommender Systems

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

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