Personalized Online Learning: Context Driven Massive Open Online Courses

被引:4
|
作者
Nadira B. [1 ]
Makhlouf D. [2 ]
Amroune M. [1 ]
机构
[1] Larbi Tebessi University, Tebessa
[2] Oum Bouaghi University, Oum Bouaghi
关键词
Adaptive Learning; Context; Learning Platform; Massive Open Online Courses; Personalized; Personalized Learning;
D O I
10.4018/IJWLTT.20211101.oa8
中图分类号
学科分类号
摘要
The success of MOOCs (massive open online courses) is rapidly increasing. Most educational institutions are highly interested in these online platforms, which embrace intellectual and educational objectives and provide various opportunities for lifelong learning. However, many limitations, such as learners diversity and lack of motivation, affected learners outcomes, which unfortunately raised the dropout rate. Thus, multiple solutions were afforded on MOOC platforms to tackle these common problems. This paper suggests a model outline of a customizable system context-driven massive open online courses that could be implemented in any learning environment, and that goes hand in hand with learners context to boost their motivation towards learning, and to help identify their learning needs. The paper introduces CD-MOOC following a learner-based approach by employing two types of users data long-Term and short-Term data assembled from learners online traces when interacting on the platform. The data help users design their own learning path based on their context and preferences. © 2021 IGI Global. All rights reserved.
引用
收藏
相关论文
共 50 条
  • [21] Researching massive open online courses for language teaching and learning
    Martin-Monje, Elena
    Borthwick, Kate
    RECALL, 2021, 33 (02) : 107 - 110
  • [22] Project Based Case Learning and Massive Open Online Courses
    Jian, Bo
    Yang, Cheng
    INTERNATIONAL JOURNAL OF DISTANCE EDUCATION TECHNOLOGIES, 2015, 13 (03) : 53 - 60
  • [23] FAQ chatbot and inclusive learning in massive open online courses
    Han, Songhee
    Lee, Min Kyung
    COMPUTERS & EDUCATION, 2022, 179
  • [24] MATHEMATICAL MODELS OF LEARNING ANALYTICS FOR MASSIVE OPEN ONLINE COURSES
    Sinitsyn, E.
    Tolmachev, A.
    Larionova, V
    Ovchinnikov, A.
    EDULEARN19: 11TH INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES, 2019, : 4395 - 4404
  • [25] Learning Engagement and Persistence in Massive Open Online Courses (MOOCS)
    Jung, Yeonji
    Lee, Jeongmin
    COMPUTERS & EDUCATION, 2018, 122 : 9 - 22
  • [26] Learning engagement in massive open online courses: A systematic review
    Wang, Rui
    Cao, Jie
    Xu, Yachen
    Li, Yanyan
    FRONTIERS IN EDUCATION, 2022, 7
  • [27] Massive open online courses - the modern concept in education and learning
    Kostyuk, Yury L.
    Levin, Ilya S.
    Fuks, Alexander L.
    Fuks, Irina L.
    Yankovskaya, Anna E.
    VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE, 2014, 26 (01): : 89 - 98
  • [28] Deep Learning to Monitor Massive Open Online Courses Dynamics
    Botticelli, Marco
    Gasparetti, Fabio
    Sciarrone, Filippo
    Temperini, Marco
    METHODOLOGIES AND INTELLIGENT SYSTEMS FOR TECHNOLOGY ENHANCED LEARNING, 2022, 326 : 114 - 123
  • [29] Evaluation of Learning Efficiency of Massive Open Online Courses Learners
    Li, Yong
    INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING, 2022, 17 (17) : 50 - 61
  • [30] A Preliminary Study on the Learning Assessment in Massive Open Online Courses
    Yuan, Quan
    Gao, Qin
    Chen, Yue
    CROSS-CULTURAL DESIGN, 2017, 10281 : 592 - 602