MOOCs Recommender System using Ontology and Memory-based Collaborative Filtering

Rabahallah, Kahina; Mahdaoui, Latifa; Azouaou, Faiçal

doi:10.5220/0006786006350641

Cited by 21 publications

(6 citation statements)

References 18 publications

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“…They identified networking, opportunities, pedagogy, content, technology to be driving factors in success of any MOOCs. [6] has identified that lack of proper mechanism for proper selection of courses can be a possible reason for students dropping out of course and loosing interest. Hence, they proposed a recommendation system based on ontology.…”

Section: Literature Reviewmentioning

confidence: 99%

Course Recommendation, Exploratory Data Analysis And Visualizations of Massive Open Online Courses (MOOCS)

Islam¹,

Khan²,

Ahmed³

et al. 2022

JISR-C

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Users and learners nowadays are seeking their education on online platforms such as Open edX, Udemy, Udacity, and Coursera. However, online learners are faced with cumbersome tasks and various challenges to search for the required courses matching their individual goals, knowledge, and interest. With the huge amount of data and material available over the internet for MOOCs, users often face the difficulty of making the right decision to choose the right course that perfectly defines their interest and fulfills their learning requirements. Hence, the lack of targeted recommendations for MOOCs can drive users to choose irrelevant MOOCs. Recommender System (RS) plays a crucial role in assisting learners to find appropriate MOOCs to improve learners’ engagements and their satisfaction/completion rates on the courses that satisfy their learning requirements. Basically, the aim is to visualize the student's areas of interest, which should not differ from the course recommendation and the overall structure of the course.

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Section: Literature Reviewmentioning

confidence: 99%

Course Recommendation, Exploratory Data Analysis And Visualizations of Massive Open Online Courses (MOOCS)

Islam¹,

Khan²,

Ahmed³

et al. 2022

JISR-C

View full text Add to dashboard Cite

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“…Ontologies have also shown promise in the field of artificial intelligence, owing to the ease with which such data can be used by ma-chines of multiple types. The greatest applicability comes from how ontologies can be built, scaled, and operated upon in different programming constructs and platforms [9] [10] [11].…”

Section: Fig 1 Fslsm Categories Of Learnersmentioning

confidence: 99%

“…al. [9] have proposed a recommender system to suggest relevant MOOCs to online learners by combining memory-based Collaborative Filtering (CF) and ontologies. A pertinent issue with collaborative filtering is how data hungry the process initially is which can lead to the cold-start problem.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Identification of Learning Styles in MOOC Environment Using Ontology Based Browser Extension

Mishra

Agarwal

Kolekar

2021

Int. J. Emerg. Technol. Learn.

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With the advent of the era of big data and Web 3.0 on the horizon, different types of online deliverable resources in the pedagogical field have also become raft. Massive Open Online Courses (MOOCs) are the most important of such learning resources that provide many courses at different levels for the learners on the go. The data generated by these MOOCs, however, is often unorganized and difficult to track or is not used to the extent that allows identification of learner types to facilitate better learning. The proposed approach in this paper aims to detect the learning style of a learner, interacting with the MOOC portal, dynamically and automatically through a novel, indigenous and in-built browser extension. This extension is used to capture the usage parameters of the learner and analyze learning behavior in real-time. The usage parameters are captured and stored as a learner ontology to ease sharing and operating across different platforms. The learning style so deduced is based on the Felder Silverman Learning Style Model (FSLSM), where learner’s behavior under multiple criteria, vis-`a-vis perception, input, understanding, and processing are measured. Based on the generated ontological semantics of learner’s behavior, multiple models can be made to facilitate precise and efficient learning. The result shows that this state-of-the-art approach identifies and detects the learning styles of the learners automatically and dynamically, i.e., changing over time

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“…After 2016, along with collaborative and content-based filtering for course recommendation (Boratto et al, 2019;He et al, 2017;Hou et al, 2018;Rabahallah et al, 2018) researchers started to use neural networks, pattern mining, and deep learning for preprocessing of data and recommendations (Agrebi et al, 2019;Jain & Anika, 2018;Jing & Tang, 2017;H. Zhang et al, 2019).…”

Section: Course Recommender Systemsmentioning

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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MOOCs Recommender System using Ontology and Memory-based Collaborative Filtering

Cited by 21 publications

References 18 publications

Course Recommendation, Exploratory Data Analysis And Visualizations of Massive Open Online Courses (MOOCS)

Course Recommendation, Exploratory Data Analysis And Visualizations of Massive Open Online Courses (MOOCS)

Dynamic Identification of Learning Styles in MOOC Environment Using Ontology Based Browser Extension

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

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