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Exploring the Non-significant Difference on Students’ Cognitive Load Imposed by Robotics Tasks in Pair Learning

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Abstract

Currently, the cognitive load imposed by tasks with different complexity during collaborative learning or pair learning has not been well studied. This study examined the effect of three types of tasks with different complexity on students’ cognitive load during pair learning. We randomly selected a total of 76 pupils to participate in a one-semester robotics course for the experiment. The results showed that the difference in students’ cognitive load imposed by pair learning and robotics tasks with different complexity was not significant. However, it is complicated to explain the results. From one side of the coin, pair learning may be a key factor. The more complex the task is, the more possible that an individuals would collaborate with partners to promote the equal allocation of intrinsic cognitive load. In contrast, the simple tasks would cause free-riding and thus impede the equal allocation of cognitive load. From another side, the essence of germane cognitive load might play an important role too, since students’ schemas were not sufficiently automated when they encountered the easy tasks at the early learning stage, but automated to some extent when they complete complex tasks during the later learning period. Implications for practitioners are also discussed in this study.

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Acknowledgements

The authors would like to thank Liying Xia, Qiuju Si, and Zehui Zhan for checking this manuscript, and deeply grateful to the anonymous reviewers and editors for their constructive and insightful feedback on this manuscript.

Funding

This study is funded by the General Project for Education from National Social Science Fund of China (Study on Pair Learning Model in Robotics Education in K-12, Grant No. BCA190088).

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Correspondence to Baichang Zhong.

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Zhong, B., Wang, J. Exploring the Non-significant Difference on Students’ Cognitive Load Imposed by Robotics Tasks in Pair Learning. Int J of Soc Robotics 14, 3–13 (2022). https://doi.org/10.1007/s12369-021-00764-y

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