Towards an Adaptive Learning Framework for MOOCs

Ardchir, Soufiane; Talhaoui, Mohamed Amine; Azzouazi, Mohamed

doi:10.1007/978-3-319-59041-7_15

Cited by 13 publications

(7 citation statements)

References 15 publications

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“…On the other hand, some researchers are interested in applying Big Data Analytics [47] and Data Mining algorithms for MOOCs [48]. For example, one of the studies designs a model of a MOOC adaptive learning system based on Intelligent Push, Adaptive Learning Path, Diagnostic Assessments and Intelligent Feedback [49].…”

Section: Rq3: What Are the Methodologies To Provide An Adaptive Mooc/...mentioning

confidence: 99%

E-Learning through an Adaptive cMOOC: Is it Worthy of Further Research?

Emrani

Palomo-Duarte

Mota

et al. 2022

EAI Endorsed Scal Inf Syst

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This paper describes the types of MOOC considered by researchers, and highlights the latter’s focus on Connectivist MOOC. In addition, it analyses MOOC methodologies, and learners’ interest in MOOC based on the concepts of adaptability, connectivism, and socio-constructivism. This is to address the high dropout rate issue on MOOC platforms. The main objective of this work is to review the empirical results reported in these studies. To reach this goal, a Systematic Literature Review of 798 papers was carried out from 2013 until April 2021, where 446 papers were selected as primary studies. The results obtained from the classification and the analysis of the collected data confirmed the importance of continuing research in the field. Based on the concepts of socio-constructivism and adaptability, the objective is to provide an adaptive cMOOC for the profile and the needs of each learner; blending learning styles and pedagogical models with machine learning technologies.

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Section: Rq3: What Are the Methodologies To Provide An Adaptive Mooc/...mentioning

confidence: 99%

E-Learning through an Adaptive cMOOC: Is it Worthy of Further Research?

Emrani

Palomo-Duarte

Mota

et al. 2022

EAI Endorsed Scal Inf Syst

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show abstract

“…To analyze the type and trends of work found in the literature, we grouped the research work with respect to the area of MOOCs where the recommender system is applied. Adaptive content Ardchir, Talhaoui, & Azzouazi (2017).…”

Section: Content/qualitative Analysismentioning

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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“…Soufiane Ardchir, Mohamed Amine Talhaoui, Mohamed Azzouazi (2017) [3] have proposed to collect and analyze the following data to provide personalization in the MOOCs: 1. Objective information, which incorporate data provided directly by the learner like: personal data, previous knowledge, preferences, etc.…”

Section: Terminologymentioning

confidence: 99%

Adaptive Technology for Support Students in the Learning Process With Using the Massive Online Courses in the Cloud Infrastructure

Parfenov¹,

Veronika²

2017

Theoretical & Applied Science

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Impact Factor:ISRA (India) = 1.344 ISI (Dubai, UAE) = 0.829 GIF (Australia) = 0.564 JIF = 1.500 SIS (USA) = 0.912 РИНЦ (Russia) = 0.234 ESJI (KZ) = 3.860 SJIF (Morocco) = 2.031 ICV (Poland) = 6.630 PIF (India) = IntroductionThe appearance of MOOCs can be regarded as one of the ways to resolve the contradiction between the need for continuous professional selfimprovement throughout life, the rapid renewal of the content of professional competencies in the era of the development of high technology and a rather long process of training and retraining in traditional universities. However, each student needs his own set of knowledge, skills, competencies, so we need to use such e-learning technologies that will ensure their receipt most quickly, qualitatively and effectively [1,5,9,11,16].A serious disadvantage of MOOCs is the lack of an individual approach to each student due to the "hard" set by the author of the trajectory of training. The model of adaptive MOOC proposed by the authors allows each instructor to propose to move along an individual trajectory based on the dynamic collection, analysis and estimation of a big data. Such data include the level of preparedness of the student, information about learning content of the course and current academic achievements, personal characteristics of the student (gender, age, interests in the subject area, learning goals, memorization,

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Towards an Adaptive Learning Framework for MOOCs

Cited by 13 publications

References 15 publications

E-Learning through an Adaptive cMOOC: Is it Worthy of Further Research?

E-Learning through an Adaptive cMOOC: Is it Worthy of Further Research?

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

Adaptive Technology for Support Students in the Learning Process With Using the Massive Online Courses in the Cloud Infrastructure

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