MOOCRec 2 for Humanities - Learning Style Based MOOC Recommender and Search Engine

Fazuludeen, Fazlan; Vijayakumaran, Gigarthan; Mahroof, Zuhair Ahamed; Kodagoda, Nuwan; Suriyawansa, Kushnara

doi:10.1109/icac49085.2019.9103397

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…The MRSE model aims to overcome the deficiencies in the literature. Particularly, comparing to the proposed hybrid solutions [23], [27], [28], the MRSE supports the CLIR by collecting a diversity of recent Arabic and English courses from eight high-ranked MOOCs. In comparison, the previous studies retrieved only English courses in narrow domains from only two MOOCs, edX and Coursera.…”

Section: Discussionmentioning

confidence: 99%

“…Though, the system allows the user to search only Computer Science courses from edX and Coursera. As an attempt to upgrade the system, Fazuludeen et al [27], proposed a second version named MoocRec2 for Humanities courses based on the same concept. Nevertheless, both systems still need to be validated with actual users and real-world data [27], [28].…”

Section: Unified Moocs Retrieving Techniquesmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Unified Moocs Retrieving Techniquesmentioning

confidence: 99%

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

Alzahrani¹,

Meccawy

2021

IEEE Access

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“…In the same way, Sankalpa et al [156] described recommendation based on learner's learning style and preferred video style and showed categorized the courses for recommendations.. Moreover, the VERK learning model was used by Fazuludeen et al [144] to provide a personalized learning path by mapping learning styles with lecture video styles, course reading material and quizzes.…”

Section: Rq1 How Many Studies Supported Their Claim With Experiments and Which Datasets Were Used In The Studies?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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“…• Course Recommender: Piao and Breslin (57) used TF-IDF to identify the skills of the learners from their Linkedin profiles and course information, and used this information for recommendations. Fazuludeen et al (133) used TF-IDF to identify the learning styles of the learners from the content and used this for the recommendation.…”

Section: Term Frequency-inverse Document Frequency (Tf-idf)mentioning

confidence: 99%

Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)

Khalid

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Massive Open Online Courses (MOOCs) have witnessed a surge in popularity among learners and providers, prompting researchers to explore the enhancement of MOOC utilisation through recommendation systems. However, while existing efforts primarily focus on designing recommender systems capable of providing recommendations across diverse areas of MOOCs, less attention is given to essential characteristics of a robust recommender system, such as practicality and scalability. This literature gap leads to a lack of practical and scalable recommender systems capable of effectively handling increasing volumes of data. Furthermore, current systems leveraging neural networks and deep learning for user behavioural data-based recommendations face challenges in accommodating new data points without retraining. Consequently, scalable and practical recommendation algorithms for MOOCs remain limited in the current literature.To address these shortcomings, this research aims to contribute to the development of scalable and practical recommender systems for MOOCs by utilising demographic and behavioural data of learners, alongside the unique characteristics of MOOCs, for personalised recommendations. In line with these objectives, the study introduced the Novel online Recommender system for MOOCs (NoR-MOOCs) specifically designed for course recommendation. Preliminary studies on the COCO dataset demonstrate the algorithm's promising performance. NoR-MOOCs constructs user profiles based on course ratings provided by users, and its performance evaluation employs predictive and classification accuracy metrics (RMSE, ROC, precision, recall and coverage). A comparison with traditional K-Means and collaborative Filtering algorithms validates the effectiveness of NoR-MOOCs.Additionally, considering the abundant user behavioural and demographic data available in MOOCs, a Novel online Multi-attribute-based Recommendation algorithm for MOOCs (NoM-MOOCs) is designed. This algorithm creates user profiles based on their behavioural and demographic characteristics. Extensive experiments on the Edx and CAN datasets demonstrate the algorithm's significant out-performance compared to the Kernal Mapping recommender system (KMR) in terms of predictive and classification accuracy metrics. Moreover, the analysis of behavioural data from MOOCs logs contributes to forming valuable learning pathways for learners. Existing literature has extensively explored the design and implementation of learning pathway recommendation systems, with such recommender systems being intricately designed using neural networks and deep learning. To this end, a fast online learning path recommender system that incorporates demographic and behavioural data of learners to recommend learning paths is designed. This system identifies similar learners by leveraging demographics and behavioural data and recommends pats paths taken by successful students to struggling students with similar behavioural and demographic profiles. To evaluate the results of this proposed algorithm and the potential impact of the learning path recommender system on learners, an interview with the MOOC professor is conducted.Overall, this research strives to contribute to the advancement of scalable and practical recommender systems for MOOCs, enriching the learning experiences of users and promoting the effective utilisation of MOOCs platforms.

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MOOCRec 2 for Humanities - Learning Style Based MOOC Recommender and Search Engine

Cited by 3 publications

References 14 publications

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

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

A Systematic Mapping Review on MOOC Recommender Systems

Scalable and Practical Recommender System in Massive Open Online Courses (MOOCs)

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