Personalized Recommendation of Open Educational Resources in MOOCs

Hajri, Hiba; Bourda, Yolaine; Popineau, Fabrice

doi:10.1007/978-3-030-21151-6_9

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…Similarly, Pang et al [117] proposed solution using recommendation based on learner neighbor and learner series (RLNLS). Open educational resource (OER) recommender system was proposed by Hajri et al [130] that could be plugged in an OLE to provide resource recommendations. Ndiyae et al [131] proposed an automatic analysis of learner's response with knowledge tests to provide personalized recommendation for each learner.…”

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

confidence: 99%

“…Zhang et al [122] used Multi-Grained-BKT and Historical-BKT, two knowledge tracing models to evaluate learning state to recommend learning material to the students identifying their weak points. A MOOC based open educational resource (OER) recommender system was proposed by Hajri et al [130] that could be plugged in an OLE to provide recommendation of OER to the learner. Ndiyae et al [131] proposed an automatic analysis of learner's response with knowledge tests to provide personalized recommendation for each learner.…”

Section: Knowledge-based Filtering (Kbf)mentioning

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%

Section: Knowledge-based Filtering (Kbf)mentioning

confidence: 99%

A Systematic Mapping Review on MOOC Recommender Systems

et al. 2021

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“…While preprocessing the dataset, we observed an increase in the use of neural networks, pattern mining, and machine learning in more recent years (Cooper et al, 2018a;Hajri et al, 2019;Xiao et al, 2018;Pardos et al, 2017;H. Zhang et al, 2019).…”

Section: Learning Elements Recommender Systemsmentioning

confidence: 99%

“…Hajri et al (2017);Harrathi et al (2017);Hajri et al (2018);Harrathi et al (2018);Xiao, Wang, Jiang, & Li (2018);Chanaa & Faddouli (2019);Hajri et al (2019); H..Suggestion to studyCorbi & Burgos (2014);Niu et al (2015).Video /lectures/clip recommender Agrawal, Venkatraman, Leonard, & Paepcke (2015); Gómez-Berbís & Lagares-Lemos (2016); Bhatt et al (2018); Cooper et al (2018a, 2018b); Mawas, Gilliot, Garlatti, Euler, & Pascual (2018); Zhao et al (2018); Belarbi et al (2019a, 2019b). , Lezcano, Drachsler, & Sloep (2016); Gómez-Berbís & Lagares-Lemos (2016); Hou et al (2016); Piao & Breslin (2016); Symeonidis & Malakoudis (2016); Dai et al (2017); Gope & Jain (2017); He, Liu, & Zhang (2017); Jing & Tang (2017); Y. Li & Li (2017); Ouertani & Alawadh (2017); Shaptala, Kyselova, & Kyselov (2017); EL Alami, Eddine, & Mohamed (2017); Yuqin Wang, Liang, Ji, ShiweiWang, & YiqiangChen (2017); Yuanyuan Wang, Maruyama, Yasui, Kawai, & Akiyama (2017); H. Zhang et al (2017); Assami et al (2018); Campos et al (2018a, 2018b); Chen et al (2018); Hou et al (2018); Iniesto & Rodrigo (2018); Jain & Anika (2018); Jun Xiao et al (2018); X. Li, Wang, Wang, & Tang (2018); Pang, Liao, Tan, Wu, & Zhou (2018); Rabahallah, Mahdaoui, & Azouaou (2018); Symeonidisa & Malakoudis (2018); H. Zhang et al (2018); Agrebi, Sendi, & Abed (2019); Aryal et al (2019); Boratto, Fenu, & Marras (2019.…”

mentioning

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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“…A mature English online learning platform should be able to meet students' demand for learning resources, improve their access efficiency to each learning project, and optimize their learning experience. Now a few resource recommendation systems that can effectively alleviate the problems of large resource volume and slow screening speed have been applied to various Internet platforms [4][5][6][7][8]. The basic idea of these resource recommendation systems is to mine the data of students' preferences or abilities based on their operation history or evaluation information to further realize the prediction and recommendation services of their requirements and interests [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Resource Recommendation Algorithm for Online English Learning Systems Based on Learning Ability Evaluation

Zhou

2021

Int. J. Emerg. Technol. Learn.

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Mature online English learning platforms should provide students with necessary learning resources, ensure efficient access to learning projects, and offer the optimal learning experience. However, the traditional recommendation methods for English learning resources cannot satisfy the in-depth learning demand of students. To solve the problem, this paper designs a resource recommendation algorithm for online English learning systems based on learning ability evaluation. Firstly, the workflow of the designed algorithm was introduced, and a four-layer test system was developed for students’ English learning ability evaluation. Next, an English learning ability evaluation method was proposed based on the maximum expectation algorithm, as well as the estimation methods for parameters like learning ability, degree of discrimination, difficulty, and guess coefficient. Experimental results demonstrated the good effect of the proposed resource recommendation algorithm. The research findings provide a reference for resource recommendation of other online learning systems.

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Personalized Recommendation of Open Educational Resources in MOOCs

Cited by 11 publications

References 8 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)

A Resource Recommendation Algorithm for Online English Learning Systems Based on Learning Ability Evaluation

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