A Suggested Algorithm of Recommender System to Recommend crawled-Web Open Educational Resources to Course Management System

Itmazi, Jamil; Hijazi, Haytham

doi:10.15849/icit.2015.0050

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…And OER could be relative to learning pathways in MOOCs. General OER Recommender systems [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [30], [30], [30], [30], [42] , [44], [45] , [46], [49], [50], [51], [52], [53], [54], [56], [57], [58], [59] [100], [104], [111], [113], [114], [126], [127], [129], [131] OER in MOOCs [28],…”

Section: Figmentioning

confidence: 99%

Systematic Literature Review on Open Educational Resources Recommender Systems

Rima

Meriem²,

Ajhoun³

2022

Int. J. Interact. Mob. Technol.

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background: In recent years, recommender systems (RS) have grown in popularity among learning and education stakeholders, while open educational resources (OER) have begun to be deployed to improve the education field. The selection of OERs should not be haphazard; rather, it should be based on the learning objectives and (RS) commonly utilized to overcome the abundance of OER. Objective: The aim of this paper is to examine the current advances in OER recommenders and the different commonly used recommendation approaches and technical features, to position our future research alongside the existing. Method: A systematic literature review with a deductive approach using the grounded theory technique, combining quantitative and qualitative analysis by adopting open coding (inductive and deductive) for content analysis. Databases: we employed Google Scholar as a search engine to crawl data. Results: The findings are presented according to the (Bernard, H. Russell .2000) process. We present the results of the analysis using quotes from interviews that illuminate the theory (exemplars). Conclusion: The review reveals an emerging interest in the area; nevertheless, hybrid interactive systems; chatbot-based, dedicated to course creators are lacking. Further and future research is needed

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Section: Figmentioning

confidence: 99%

Systematic Literature Review on Open Educational Resources Recommender Systems

Rima

Meriem²,

Ajhoun³

2022

Int. J. Interact. Mob. Technol.

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“…More than half the work we reviewed focused on the implementation of recommender systems and the work concerns content-based filtering or hybrid algorithms (Cooper et al, 2018a;Itmazi & Hijazi, 2015;. Researchers have designed recommender systems for different types of learning elements, such as video clips, next page, and additional resources helpful to the learner.…”

Section: Learning Elements 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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“…On the one hand, many scholars have studied the recommendation system and recommendation mechanism (Liao et al . 2014;Itmazi & Hijazi 2015); on the other hand, some scholars have studied the intention to recommend from the perspective of word of mouth (Eisingerich et al . 2014) .…”

Section: Introductionmentioning

confidence: 99%

Identifying the Reasons why Users in China Recommend Bike Apps

Zhang

Wang

2017

International Journal of Market Research

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This study examines factors affecting users' intention to recommend bike-sharing apps from the perspective of internal and external benefits perception. Since the end of 2016, bike sharing has suddenly taken off in China, and competition is fierce. Bike-sharing users' intention to recommend bike apps is particularly important as it can help operators attract more potential users. However, little research to date has focused on bike-sharing users' intention to recommend bike apps in the Chinese context. This study aims to fill this research gap using structural equation modelling. Data were collected from 209 bike-sharing app customers and the results show the following key findings: (1) the usefulness of the bike-sharing app is the most important factor contributing to users' intention to recommend, followed by economic incentive and ease of use; (2) users' trust that interacts with perceived ease of use has positive effect on users' intention to recommend, while users' trust that interacts with economic incentive has negative effect on users' intention to recommend. Implications for researchers and practice are discussed.

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A Suggested Algorithm of Recommender System to Recommend crawled-Web Open Educational Resources to Course Management System

Cited by 3 publications

References 14 publications

Systematic Literature Review on Open Educational Resources Recommender Systems

Systematic Literature Review on Open Educational Resources Recommender Systems

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

Identifying the Reasons why Users in China Recommend Bike Apps

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