MoocRec: Learning Styles-Oriented MOOC Recommender and Search Engine

Aryal, Saugat; Porawagama, Anjana Shriwantha; Hasith, Makulle Gedara Sajeeva; Thoradeniya, Sachini Chloe; Kodagoda, Nuwan; Suriyawansa, Kushnara

doi:10.1109/educon.2019.8725079

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…Similarly, Felder & Silverman [181] learning styles combined with and topic modeling [182] were utilized in different studies. Likewise, Aryal et al [141] mapped learning styles with video styles to provide personalization of MOOC to the learner. Similarly, Hilmy et al [142] analyzed discussion forums to identify how learner feels about the learning platform and used it as recommendation metric.…”

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

confidence: 99%

“…Likewise, k-mean and apriori algorithms were used by Vélez-Langs and Caicedo-Castro[129]. deep learning techniques combined with learning analytics in were utilized by Aryal et al[141] and Hilmy et al[142] for personalized learning. K-NN clustering with content-based approach was proposed in Cao et al[149] while a top-k method with max cost flow by Apaza et al[58] for course recommendation.…”

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confidence: 99%

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

confidence: 99%

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confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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“…There is also a lack of standardized datasets available for the evaluation of recommender systems in MOOCs. Researchers have mostly used publicly available datasets of Coursera, edX, and, in some cases, datasets from their own institutes to evaluate recommender systems (Aryal et al, 2019;Dai et al, 2017;Kardan et al, 2017;Mi & Faltings, 2016a;Shaptala et al, 2017;Yang, Adamson, et al, 2014;Yang, Piergallini, et al, 2014). Other authors have created datasets (Onah & Sinclair, 2015a;He et al, 2017;Iniesto & Rodrigo, 2019;Zhou et al, 2015).…”

Section: Future Directionsmentioning

confidence: 99%

“…A lack of standardized datasets can be a significant limitation when benchmarking or comparing algorithms or techniques of different researchers. Furthermore, most researchers used datasets from computer science-related courses for testing their recommender systems (Aryal et al, 2019;Bhatt et al, 2018;M. Zhang et al, 2019;Zhou et al, 2015) which limits the research to one academic field.…”

Section: Future Directionsmentioning

confidence: 99%

Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

Khalid

Lundqvist

Yates

2020

IRRODL

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In recent years, massive open online courses (MOOCs) have gained popularity with learners and providers, and thus MOOC providers have started to further enhance the use of MOOCs through recommender systems. This paper is a systematic literature review on the use of recommender systems for MOOCs, examining works published between January 1, 2012 and July 12, 2019 and, to the best of our knowledge, it is the first of its kind. We used Google Scholar, five academic databases (IEEE, ACM, Springer, ScienceDirect, and ERIC) and a reference chaining technique for this research. Through quantitative analysis, we identified the types and trends of research carried out in this field. The research falls into three major categories: (a) the need for recommender systems, (b) proposed recommender systems, and (c) implemented recommender systems. From the literature, we found that research has been conducted in seven areas of MOOCs: courses, threads, peers, learning elements, MOOC provider/teacher recommender, student performance recommender, and others. To date, the research has mostly focused on the implementation of recommender systems, particularly course recommender systems. Areas for future research and implementation include design of practical and scalable online recommender systems, design of a recommender system for MOOC provider and teacher, and usefulness of recommender systems.

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“…Based on those sets of interests, recommendation and personalization approaches can add more value by tailoring each user's results. Some studies have proposed the idea of a unified search approach to MOOCs [21]- [28]; such studies are still limited. Where many of them present models and platforms with implementation deficiencies.…”

Section: Introductionmentioning

confidence: 99%

MOOCs One-Stop Shop: A Realization of a Unified MOOCs Search Engine

Alzahrani¹,

Meccawy

2021

IEEE Access

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Massive open online courses (MOOCs) have gained great popularity among adult learners, as many professionals use them to advance their careers. However, many MOOCs providers offer a large number of courses across many subjects, making the search and selection of the appropriate course cumbersome for the learner through a regular Web search. Hence, there is a crucial need for a unified search engine that acts as a one-stop-shop for all MOOCs. This paper presents the design and implementation of a unified MOOCs search model for retrieving and recommending courses from multiple MOOCs. The evaluation showed that the proposed model is reliable and convenient. Initial results showed that the system could present courses from multiple MOOCs that suit users' needs. Moreover, the recommendation functionality is applied for a better user-centric orientation.

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MoocRec: Learning Styles-Oriented MOOC Recommender and Search Engine

Cited by 12 publications

References 9 publications

A Systematic Mapping Review on MOOC Recommender Systems

A Systematic Mapping Review on MOOC Recommender Systems

Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

MOOCs One-Stop Shop: A Realization of a Unified MOOCs Search Engine

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