Combining Collaborative Reflection based on Worked-Out Examples with Problem-Solving Practice: Designing Collaborative Programming Projects for Learning at Scale

Combining Collaborative Reflection based on Worked-Out Examples with Problem-Solving Practice: Designing Collaborative Programming Projects for Learning at Scale
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将基于已制定示例的协作反思与解决问题的实践相结合:设计用于大规模学习的协作编程项目

DOI:
10.1145/3430895.3460152
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发表时间:
2021
期刊:
Comparing Example-Based Collaborative Reflection to Problem-Solving Practice for Learning during Team-Based Software Engineering Projects
影响因子:
--
通讯作者:
Rosé, Carolyn
Rosé, Carolyn
中科院分区:
--
文献类型:
--
作者:
Sankaranarayanan, Sreecharan;Kandimalla, Siddharth Reddy;Bogart, Christopher;Murray, R. Charles;Hilton, Michael;Sakr, Majd;Rosé, Carolyn

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计算机科学教学法压倒性地倾向于解决问题的实践,而不是参与的方法,如制定的示例研究,特别是在高级课程中。这是因为人们认为,虽然这些替代方法可以提高学生的概念学习,但由于缺乏实践,他们可能会让他们无法执行真正的解决问题的任务。在本文中,我们进行了一个直接的比较,这种权衡在一个同步的协作编程项目,通过调整解决问题和协作反射之间的边界的基础上制定的例子,同时保持任务的总时间不变。我们发现,学生花在工作的例子研究的时间越多,更多的是观察到的改善,在前到后的测试成绩没有显着差异,在随后的问题解决任务的性能。这些结果,因此,挑战的主导地位,解决问题的做法,在先进的课程背景下,并通知规模的协作编程项目的设计。
Computer science pedagogy has overwhelmingly favored problem-solving practice over methods of engagement like worked-out example study especially in advanced classes. This is due to the belief that while these alternative methods may improve student conceptual learning, they may leave them less able to perform on authentic problem-solving tasks from a lack of hands-on practice. In this paper, we perform a direct comparison of this trade-off in a synchronous collaborative programming project by adjusting the boundary between problem-solving and collaborative reflection based on a worked-out example while keeping the total time on task constant. We find that the more time students spent on worked example study, the more was the observed improvement in the pre- to post-test scores with no significant difference in performance on a subsequent problem-solving task. These results, therefore, challenge the dominant place of problem-solving practice in the advanced curricular context and inform the design of collaborative programming projects at scale.
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