A Comparative Study of the Mobile Learning Approaches

被引:2
|
作者
Baccari, Sameh [1 ]
Mendes, Florence [2 ]
Nicolle, Christophe [2 ]
Soualah-Alila, Fayrouz [2 ]
Neji, Mahmoud [1 ]
机构
[1] Univ Sfax, MIRACL, Sfax, Tunisia
[2] Univ Bourgogne, LE2I, UMR CNRS 6306, Dijon, France
关键词
Mobile technology; E-learning; M-learning; Context-change management; Learning;
D O I
10.1007/978-3-319-50463-6_7
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the emergence of mobile devices (Smart Phone, PDA, UMPC, game consoles, etc.), and the growth of offers and needs of a company under formation in motion, multiply the work to identify relevant new learning platforms to improve and facilitate the process of distance learning. The next stage of distance learning is naturally the port of e-learning to new mobile systems. This is called m-learning (mobile learning). Because of the mobility feature, m-learning courses have to be adapted dynamically to the learner's context. Several researches addressed this issue and implemented a mobile learning environment. In this paper, we compare a list of mobile learning architectures with methods presented in the literature. The evaluation presents a set of criteria specifically identified to qualify m-learning architectures dedicated to the context-change management.
引用
收藏
页码:76 / 85
页数:10
相关论文
共 50 条
  • [31] Pedestrian Trajectory Prediction With Learning-based Approaches: A Comparative Study
    Li, Yang
    Xin, Long
    Yu, Dameng
    Dai, Pengwen
    Wang, Jianqiang
    Li, Shengbo Eben
    [J]. 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19), 2019, : 919 - 926
  • [32] Predictive modeling in urgent care: a comparative study of machine learning approaches
    Tang, Fengyi
    Xiao, Cao
    Wang, Fei
    Zhou, Jiayu
    [J]. JAMIA OPEN, 2018, 1 (01) : 87 - 98
  • [33] A comparative study of machine learning approaches for modeling concrete failure surfaces
    Reuter, Uwe
    Sultan, Ahmad
    Reischl, Dirk S.
    [J]. ADVANCES IN ENGINEERING SOFTWARE, 2018, 116 : 67 - 79
  • [34] A comparative study of ensemble learning approaches in the classification of breast cancer metastasis
    Zhang, Wangshu
    Zeng, Feng
    Wu, Xuebing
    Zhang, Xuegong
    Jiang, Rui
    [J]. 2009 INTERNATIONAL JOINT CONFERENCE ON BIOINFORMATICS, SYSTEMS BIOLOGY AND INTELLIGENT COMPUTING, PROCEEDINGS, 2009, : 242 - +
  • [35] Manufacturing Quality Prediction Using Intelligent Learning Approaches: A Comparative Study
    Bai, Yun
    Sun, Zhenzhong
    Deng, Jun
    Li, Lin
    Long, Jianyu
    Li, Chuan
    [J]. SUSTAINABILITY, 2018, 10 (01)
  • [36] A Comparative Study of Conventional and Deep Learning Approaches for Demosaicing Mastcam Images
    Kwan, Chiman
    Chou, Bryan
    [J]. SIGNAL PROCESSING, SENSOR/INFORMATION FUSION, AND TARGET RECOGNITION XXVIII, 2019, 11018
  • [37] A Comparative Study of Deep Learning Approaches to Rooftop Detection in Aerial Images
    Cai, Yuwei
    He, Hongjie
    Yang, Ke
    Fatholahi, Sarah Narges
    Ma, Lingfei
    Xu, Linlin
    Li, Jonathan
    [J]. CANADIAN JOURNAL OF REMOTE SENSING, 2021, 47 (03) : 413 - 431
  • [38] ASSESSMENT PREFERENCES AND APPROACHES TO LEARNING OF MBA AND MPA STUDENTS: A COMPARATIVE STUDY
    Kwan, James
    [J]. ICERI2016: 9TH INTERNATIONAL CONFERENCE OF EDUCATION, RESEARCH AND INNOVATION, 2016, : 8403 - 8411
  • [39] University students' acceptance of mobile learning: A comparative study in Turkey and Kyrgyzstan
    Adanir, Gulgun Afacan
    Muhametjanova, Gulshat
    [J]. EDUCATION AND INFORMATION TECHNOLOGIES, 2021, 26 (05) : 6163 - 6181
  • [40] Mobile agents localization in ad hoc networks: a comparative study of centralized and distributed approaches
    Zafoune, Y.
    Kanawati, R.
    Mokhtari, A.
    [J]. MEDIA CONVERGENCE: MOVING TO THE NEXT GENERATION, 2007, : 269 - 275