Research trends in measurement and intervention tools for self-regulated learning for e-learning environments-systematic review (2008-2018)

被引:92
作者
Araka, Eric [1 ]
Maina, Elizaphan [1 ]
Gitonga, Rhoda [1 ]
Oboko, Robert [2 ]
机构
[1] Kenyatta Univ, POB 43844, Nairobi 00100, Kenya
[2] Univ Nairobi, Nairobi, Kenya
关键词
Self-regulated learning; Learning management systems; Measuring; Promoting; Educational data mining; Learner analytics; ONLINE; CHALLENGES; UNIVERSITY; EDUCATION; SUPPORT; MOOCS;
D O I
10.1186/s41039-020-00129-5
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
For the last one decade, research in self-regulated learning (SRL) and educational psychology has proliferated. Researchers and educators have focused on how to support leaners grow their SRL skills on both face-to-face and e-learning environments. In addition, recent studies and meta-analysis have greatly contributed to the domain knowledge on the use of SRL strategies and how they contribute and boost academic performance for learners. However, there is little systematic review on the literature on the techniques and tools used to measure SRL on e-learning platforms. This review sought to outline recent advances and the trends in this area to make it more efficient for researchers to establish the empirical studies and research patterns among different studies in the field of SRL. The findings from this study are concurrent with existing empirical evidence that traditional methods designed for classroom supports are being used for measuring SRL on e-learning environments. Few studies have used learner analytics and educational data mining (EDM) techniques to measure and promote SRL strategies for learners. The paper finally points out the existing gaps with the tools presently used to measure and support SRL on learning management systems and recommends further studies on the areas of EDM which can support SRL.
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页数:21
相关论文
共 71 条
  • [1] Adam Noor Latiffah, 2017, Advances in Visual Informatics. 5th International Visual Informatics Conference, IVIC 2017. Proceedings: LNCS 10645, P143, DOI 10.1007/978-3-319-70010-6_14
  • [2] Personalised Learning Object System Based on Self-Regulated Learning Theories
    Alharbi, Ali
    Henskens, Frans
    Hannaford, Michael
    [J]. INTERNATIONAL JOURNAL OF ENGINEERING PEDAGOGY, 2014, 4 (03): : 24 - 35
  • [3] Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment
    Aljohani, Naif Radi
    Fayoumi, Ayman
    Hassan, Saeed-Ul
    [J]. SUSTAINABILITY, 2019, 11 (24)
  • [4] Allen I., 2013, CHANGING COURSE
  • [5] [Anonymous], 2011, LAK 11
  • [6] [Anonymous], P 2 INT C LEARN AN K, DOI 10.1145/2330601.2330661
  • [7] A Conceptual Model for Measuring and Supporting Self-Regulated Learning using Educational Data Mining on Learning Management Systems
    Araka, Eric
    Maina, Elizaphan
    Gitonga, Rhoda
    Oboko, Robert
    [J]. 2019 IST-AFRICA WEEK CONFERENCE (IST-AFRICA), 2019,
  • [8] Arnold K.E., 2012, P 2 INT C LEARN AN K, P267, DOI 10.1145/2330601.2330666
  • [9] Azevedo R., 2009, Proceedings of the AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems, P14
  • [10] Azevedo R., 2009, HDB METACOGNITION ED, P319