Recommendation for MOOC with Learner Neighbors and Learning Series

Pang, Yanxia; Liao, Chang; Tan, Wenjun; Wu, Yueping; Zhou, Chunyi

doi:10.1007/978-3-030-02925-8_27

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Hence the dropout rate can be reduced by increasing the user's satisfaction. One such study conducted by Pang et al utilized collaborative filtering along with learning series techniques to maximize user satisfaction [43]. They proposed a methodology that is based on Recommendations with Learner's Neighbour and Learning Series (RLNLS).…”

Section: Collaborative Filteringmentioning

confidence: 99%

“…In future, some suggested tags can also be utilized keeping in view the experts' generated tags to ensure vast coverage of LOs. [38] [39] They proposed MoodelREC teacher centric system that utilized popular LO repositories In future, feature reduction can be performed to extract data only from relevant variables to maximize efficiency of the algorithm [43]. The precision results showed between 1% to 3% accuracy for sparse datasets [48].…”

Section: Critical Analysismentioning

confidence: 99%

See 1 more Smart Citation

Improved E-learning Based Recommender Systems: A Survey

Maryam,

Ashraf,

Jhanjhi

2023

Preprint

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Online learning is gaining massive popularity with time. The e-learning platforms operate differently from traditional educational institutions and hence need different strategy for course recommendations. This survey aims to cover the major emerging research areas in e-learning recommender systems. Our study covers different areas of research including graph-based methodologies, ITS, query optimization, content-based, and collaborative filtering, big data, and association rules mining. This survey aimed to explore all major emerging directions of recommender systems in education. This study analyzed existing literature in all of the areas mentioned before and performed objective-based analysis. A brief performance analysis was also done for the researches where the values were comparable. Limitations of existing researches were also identified and studied to shed some light on future directions.

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Section: Collaborative Filteringmentioning

confidence: 99%

Section: Critical Analysismentioning

confidence: 99%

Improved E-learning Based Recommender Systems: A Survey

Maryam,

Ashraf,

Jhanjhi

2023

Preprint

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“…Learning analytics were used by Li and Mitros [63] showing how learners could collaborate by improving resources for remediation. 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.…”

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

confidence: 99%

“…Mothukuri et al [94] proposed a feedback capturing agent to analyze learner style by monitoring learner progress to update cognitive profile of the learner in order for effective recommendation. Pang et al [117] proposed solution using recommendation based on learner neighbor and learner series (RLNLS). Harrathi et al…”

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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“…We also observed the introduction of association rule mining and hybrid algorithms (Xiao et al, 2018;Y. Li & Li, 2017;Pang et al, 2018) Gope and Jain (2017) used the learning style of the student to recommend courses. Their prototype was based on a learning system model and worked exclusively with edX courses.…”

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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Recommendation for MOOC with Learner Neighbors and Learning Series

Cited by 7 publications

References 16 publications

Improved E-learning Based Recommender Systems: A Survey

Improved E-learning Based Recommender Systems: A Survey

A Systematic Mapping Review on MOOC Recommender Systems

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

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