Exploring learning behaviour under an integrated mobile and web-based learning environment

被引:2
作者
Lam, Sze-Sing [1 ]
Choi, Samuel Ping-Man [1 ]
Ng, Chun-Yu [1 ]
机构
[1] Open Univ Hong Kong, Lee Shau Kee Sch Business & Adm, Hong Kong, Peoples R China
关键词
learning behaviour; mobile learning; web-based learning; multi-platform learning; SELF-REGULATION; ANALYTICS; STRATEGIES; MOTIVATION;
D O I
10.1504/IJMLO.2021.114520
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Mobile learning plays a paramount role in supporting e-learning and distance learning through enabling learners to carry out learning activities independent of time or space constraints. In designing and developing an integrated mobile and web-based learning environment to fulfil the ever-changing needs of learners, a good understanding of how they utilise such platforms in their learning is essential. This study investigated students' learning behaviour across the mobile and web-based platforms by examining how learning activities were mixed and matched on the platforms. Exploratory analysis and clustering technique were applied to gain holistic understanding of usage pattern and learning sequence from click stream data. Findings show that students used the mobile platform to supplement the web-based learning and self-regulated their learning across the mobile and web-based platforms leading to considerable differences in the learning behaviour on the platforms for different performing students. Strategies to improve self-efficacy of students in learning across mobile and web platforms are discussed.
引用
收藏
页码:130 / 148
页数:19
相关论文
共 30 条
[1]   A holistic self-regulated learning model: A proposal and application in ubiquitous-learning [J].
Adriana Cardenas-Robledo, Leonor ;
Pena-Ayala, Alejandro .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 123 :299-314
[2]   Process mining techniques for analysing patterns and strategies in students' self-regulated learning [J].
Bannert, Maria ;
Reimann, Peter ;
Sonnenberg, Christoph .
METACOGNITION AND LEARNING, 2014, 9 (02) :161-185
[3]   Profiles in Self-Regulated Learning in the Online Learning Environment [J].
Barnard-Brak, Lucy ;
Lan, William Y. ;
Paton, Valerie Osland .
INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING, 2010, 11 (01) :61-79
[4]   Using Learning Analytics to Understand the Learning Pathways of Novice Programmers [J].
Berland, Matthew ;
Martin, Taylor ;
Benton, Tom ;
Smith, Carmen Petrick ;
Davis, Don .
JOURNAL OF THE LEARNING SCIENCES, 2013, 22 (04) :564-599
[5]   Application of learning analytics using clustering data Mining for Students' disposition analysis [J].
Bharara, Sanyam ;
Sabitha, Sai ;
Bansal, Abhay .
EDUCATION AND INFORMATION TECHNOLOGIES, 2018, 23 (02) :957-984
[6]   Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review [J].
Broadbent, J. ;
Poon, W. L. .
INTERNET AND HIGHER EDUCATION, 2015, 27 :1-13
[7]   Self-regulated learning: the role of motivation, emotion, and use of learning strategies in students' learning experiences in a self-paced online mathematics course [J].
Cho, Moon-Heum ;
Heron, Michele L. .
Distance Education, 2015, 36 (01) :80-99
[8]   Self-regulation in online learning [J].
Cho, Moon-Heum ;
Shen, Demei .
DISTANCE EDUCATION, 2013, 34 (03) :290-301
[9]  
Doering T, 2012, E LEADER BERLIN, P109
[10]  
Gabadinho A, 2011, J STAT SOFTW, V40, P1