Dissecting learning tactics in MOOC using ordered network analysis
Dissecting learning tactics in MOOC using ordered network analysis
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DOI：
10.1111/jcal.12735
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发表时间：
2022-08-30
影响因子：
5
通讯作者：
Gasevic, Dragan
中科院分区：
文献类型：
--
作者：
Fan, Yizhou;Tan, Yuanru;Gasevic, Dragan
背景选择并制定适当的学习策略，以推进学习被认为是成功完成高度灵活的在线课程（如大规模开放式在线课程（MOOC））的关键技能。然而，受以往使用的分析方法如频度分布、序列挖掘、过程挖掘等的限制，我们对MOOC学习者使用的学习策略缺乏深入、全面、细致的了解。目的本研究提出了四个主要维度来更好地解释和理解学习策略，即学习行为在策略中的频率、连续性、顺序性和作用。本研究的目的是检验一种新的分析技术，有序网络分析（ONA），在多大程度上可以加深对MOOC学习策略的理解。方法特别是，我们进行了细粒度的学习策略分析，从超过400万个学习事件中检测到的行为痕迹数据的8788名学习者参加了大规模的MOOC“翻转课堂”。结果与结论我们检测到八种学习策略，然后选择一种典型策略作为示例，展示ONA技术如何揭示所有四个维度，并为这种MOOC学习策略提供更深入的见解。最重要的是，通过与过程挖掘等不同方法的比较，我们发现ONA方法提供了一个独特的机会和新颖的见解，不同的学习行动在战术中的作用，这是过去被忽视的。综上所述，我们认为ONA是一种很有前途的技术，可以帮助学习策略的研究，并最终通过加强策略支持而使MOOC学习者受益。
Background Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and process mining, we lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners. Objectives In the present study, we proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics. The aim of this study was to examine to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of MOOC learning tactics compared to using other methods. Methods In particular, we performed a fine-grained analysis of learning tactics detected from more than 4 million learning events in the behavioural trace data of 8788 learners who participated in a large-scale MOOC 'Flipped Classroom'. Results and Conclusions We detected eight learning tactics, and then chose one typical tactic as an example to demonstrate how the ONA technique revealed all four dimensions and provided deeper insights into this MOOC learning tactic. Most importantly, based on the comparison with different methods such as process mining, we found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past. Takeaway In summary, we conclude that ONA is a promising technique that can benefit the research on learning tactics, and ultimately benefit MOOC learners by strengthening the strategic support.