Exploring the behavioral patterns of Co-regulation in mobile computer-supported collaborative learning

Zheng, Lanqin; Yu, Junhui

doi:10.1186/s40561-016-0024-4

Cited by 44 publications

(26 citation statements)

References 36 publications

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“…However, when taking into account the nature of the learning tasks, which included performing different types of physics experiments, it is not surprising that students engaged mostly in metacognitive monitoring during task enactment. This is due to the fact that, when performing these experiments, the students received immediate feedback on whether their task solution was correct or not, which often led to changing the task enactment strategy (Zheng & Yu, 2016). The high frequency of monitoring during the task execution can be explained by the nature of the learning tasks, whereby the students can engage in trial and error to solve the problem.…”

Section: Discussionmentioning

confidence: 99%

Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning

Malmberg

Fincham

Pijeira‐Díaz

et al. 2021

Computer Assisted Learning

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Using hidden Markov models (HMM), the current study looked at how learners' metacognitive monitoring is related to their physiological reactivity in the context of collaborative learning. The participants (N = 12, age 16–17 years, three females and nine males) in the study were high school students enrolled in an advanced physics course. The results show that during collaborative learning, the students engaged in monitoring in each self‐regulated learning phase such as task understanding, planning and goal setting, task enactment, adaptation and reflection. The results of the HMM indicated that the learners' physiological reactivity was low when monitoring occurred. The associations between the states based on the HMM provide insights not only into how learners engage in metacognitive monitoring but also about their level of physiological reactivity in each state. In conclusion, exploring aspects of metacognitive monitoring in collaborative learning can be done with the help of physiological reactions.

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Section: Discussionmentioning

confidence: 99%

Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning

Malmberg

Fincham

Pijeira‐Díaz

et al. 2021

Computer Assisted Learning

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“…The emergence of wireless technology and a variety of mobile-device innovations have received a great deal of attention in the field of education ( Sung, Chang, & Liu, 2016 ; Sung, Chang, & Yang, 2015 ). Mobile devices offer features of portability, social connectivity, context sensitivity, and individuality, which desktop computers might not offer ( Chinnery, 2006 ; Gao, Liu, & Paas, 2016 ; Lan & Lin, 2016 ; Song, 2014 ; Zheng & Yu, 2016 ). Mobile devices have made learning movable, real-time, collaborative, and seamless ( Kukulska-Hulme, 2009 ; Wong & Looi, 2011 ), which may be called “mobile learning” in general.…”

mentioning

confidence: 99%

The Effects of Mobile-Computer-Supported Collaborative Learning: Meta-Analysis and Critical Synthesis

Sung¹,

Yang

Lee

2017

Review of Educational Research

122

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One of the trends in collaborative learning is using mobile devices for supporting the process and products of collaboration, which has been forming the field of mobile-computer-supported collaborative learning (mCSCL). Although mobile devices have become valuable collaborative learning tools, evaluative evidence for their substantial contributions to collaborative learning is still scarce. The present meta-analysis, which included 48 peer-reviewed journal articles and doctoral dissertations written over a 16-year period (2000–2015) involving 5,294 participants, revealed that mCSCL has produced meaningful improvements for collaborative learning, with an overall mean effect size of 0.516. Moderator variables, such as domain subject, group size, teaching method, intervention duration, and reward method were related to different effect sizes. The results provided implications for future research and practice, such as suggestions on how to appropriately use the functionalities of mobile devices, how to best leverage mCSCL through effective group learning mechanisms, and what outcome variables should be included in future studies to fully elucidate the process and products of mCSCL.

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“…We recorded the behaviours of interest based on established coding schemes and analysed the relationship among behaviours by exploring significant sequences. With LSA, we captured the discussion processes among the online learners (Hou, 2010;L. Zheng & Yu, 2016), differentiated online learning patterns among groups (Hou, 2012;L.…”

Section: Lsamentioning

confidence: 99%

“…With LSA, we captured the discussion processes among the online learners (Hou, 2010;L. Zheng & Yu, 2016), differentiated online learning patterns among groups (Hou, 2012;L. Zheng & Yu, 2016) and identified how students learn asynchronously (Hou, 2010(Hou, , 2012L.…”

Section: Lsamentioning

confidence: 99%

Acting in secret: Interaction, knowledge construction and sequential discussion patterns of partial role-assignment in a MOOC

Chen

Yeh²

2021

AJET

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Forum discussions have been utilised widely as a means of facilitating learning interaction and social-knowledge construction in online learning. Much research has been conducted on the instructional interventions that benefit asynchronous discussions. Role-playing, or assigning roles to discussants, has been proven effective in promoting interactivity and knowledge construction in the context of both face-to-face and online learning. However, assigning and rotating roles to thousands of learners in massive open online courses (MOOCs) and preparing them to act properly in their roles sounds impractical to MOOC instructors due to work overload. The present study provided three types of role assignment in a MOOC during various course offerings: fall offerings with no role-assignment, spring offerings with partial role-assignment and summer offerings with full role-assignment. Through the examination of the discussion patterns and role-assignment differences among 4,239 students and 5,439 posts in 56 forums, we suggest that partial role-assignment is as effective as full-role assignment. By assigning as few as 10 students with rotating roles, MOOC instructors can leverage this effective strategy while minimising their effort in preparing the discussants and moderating the discussions. These students act behind the scenes and improve the behavioural patterns of asynchronous discussions. Implications for practice or policy: MOOC instructors and teaching assistants can leverage a partial role-assignment strategy to improve asynchronous discussion quality with manageable effort. MOOC platform leaders and instructional designers may explore work-smart teaching strategies that are viable in practice without overburdening instructors.

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Exploring the behavioral patterns of Co-regulation in mobile computer-supported collaborative learning

Cited by 44 publications

References 36 publications

Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning

Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning

The Effects of Mobile-Computer-Supported Collaborative Learning: Meta-Analysis and Critical Synthesis

Acting in secret: Interaction, knowledge construction and sequential discussion patterns of partial role-assignment in a MOOC

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