Using learning analytics to explore help-seeking learner profiles in MOOCs

被引:32
作者
Corrin, Linda [1 ]
de Barba, Paula G. [2 ]
Bakharia, Aneesha [3 ]
机构
[1] Univ Melbourne, Level 6,111 Barry St, Melbourne, Vic 3010, Australia
[2] Univ Melbourne, Elisabeth Murdoch Bldg, Melbourne, Vic 3010, Australia
[3] Univ Queensland, Learning Innovat Bldg, St Lucia, Qld 4072, Australia
来源
SEVENTH INTERNATIONAL LEARNING ANALYTICS & KNOWLEDGE CONFERENCE (LAK'17) | 2017年
基金
澳大利亚研究理事会;
关键词
Learning analytics; MOOCs; Help-seeking; Learning design;
D O I
10.1145/3027385.3027448
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In online learning environments, learners are often required to be more autonomous in their approach to learning. In scaled online learning environments, like Massive Open Online Courses (MOOCs), there are differences in the ability of learners to access teachers and peers to get help with their study than in more traditional educational environments. This exploratory study examines the help-seeking behaviour of learners across several MOOCs with different audiences and designs. Learning analytics techniques (e.g., dimension reduction with t-sne and clustering with affinity propagation) were applied to identify clusters and determine profiles of learners on the basis of their help-seeking behaviours. Five help seeking learner profiles were identified which provide an insight into how learners' help-seeking behaviour relates to performance. The development of a more in-depth understanding of how learners seek help in large online learning environments is important to inform the way support for learners can be incorporated into the design and facilitation of online courses delivered at scale.
引用
收藏
页码:424 / 428
页数:5
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