Effects of automated interventions in programming assignments: evidence from a field experiment

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI:10.1145/3231644.3231650

Ralf Teusner, Thomas Hille, T. Staubitz

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引用次数: 14

Abstract

A typical problem in MOOCs is the missing opportunity for course conductors to individually support students in overcoming their problems and misconceptions. This paper presents the results of automatically intervening on struggling students during programming exercises and offering peer feedback and tailored bonus exercises. To improve learning success, we do not want to abolish instructionally desired trial and error but reduce extensive struggle and demotivation. Therefore, we developed adaptive automatic just-in-time interventions to encourage students to ask for help if they require considerably more than average working time to solve an exercise. Additionally, we offered students bonus exercises tailored for their individual weaknesses. The approach was evaluated within a live course with over 5,000 active students via a survey and metrics gathered alongside. Results show that we can increase the call outs for help by up to 66% and lower the dwelling time until issuing action. Learnings from the experiments can further be used to pinpoint course material to be improved and tailor content to be audience specific.

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编程作业中自动化干预的影响:来自现场实验的证据

mooc的一个典型问题是，课程指导没有机会单独帮助学生克服他们的问题和误解。本文介绍了在编程练习中自动干预困难学生的结果，并提供同伴反馈和量身定制的奖金练习。为了提高学习的成功率，我们不想废除教学上期望的试错，而是减少广泛的斗争和丧失动力。因此，我们开发了自适应自动及时干预，以鼓励学生在需要比平均工作时间多得多的时间来解决一个练习时寻求帮助。此外，我们还为学生提供了针对他们个人弱点量身定制的额外练习。通过调查和收集的指标，该方法在5000多名活跃学生的现场课程中进行了评估。结果表明，该方法可将呼救次数提高66%，并缩短发出行动前的停留时间。从实验中获得的经验可以进一步用于确定需要改进的课程材料，并根据具体的受众定制内容。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the Fifth Annual ACM Conference on Learning at Scale

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