Adaptive Assessment Experiment in a HarvardX MOOC
Adaptive Assessment Experiment in a HarvardX MOOC
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HarvardX MOOC 中的自适应评估实验
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Glenn Lopez
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文献类型：
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作者：
I. Rushkin;Y. Rosen;Andrew M. Ang;Colin Fredericks;D. Tingley;Mary Jean Blink;Glenn Lopez
我们报告了自适应学习功能的实验性实现在一个自定进度的哈佛MOOC（大规模开放式在线课程）。在MOOC中，需要基于证据的教学设计，为学习者创造最佳条件，这些学习者具有广泛的先前知识，技能和动机。但是，在这样的课程中，用户可以自由地以他们认为合适的任何顺序探索课程材料，并可能随时退出，这使得很难预测实施适应性的实际挑战，以及它的效果，而不进行实验。本研究探讨了edX平台中自适应功能对课程（重新）设计的技术可行性和影响。此外，它旨在为未来研究MOOC在学习成果，参与和辍学率方面的适应功能奠定基础。我们的初步研究结果表明，我们使用的那种适应性导致更高的学习效率（没有对学习结果的不利影响，学习者通过课程更快，尝试更少的问题，因为问题是有针对性的方式提供给他们）。需要进一步的研究来证实这些发现，并探索其他可能的影响。
We report an experimental implementation of adaptive learning functionality in a self-paced HarvardX MOOC (massive open online course). In MOOCs there is need for evidence-based instructional designs that create the optimal conditions for learners, who come to the course with widely differing prior knowledge, skills and motivations. But users in such a course are free to explore the course materials in any order they deem fit and may drop out any time, and this makes it hard to predict the practical challenges of implementing adaptivity, as well as its effect, without experimentation. This study explored the technological feasibility and implications of adaptive functionality to course (re)design in the edX platform. Additionally, it aimed to establish the foundation for future study of adaptive functionality in MOOCs on learning outcomes, engagement and drop-out rates. Our preliminary findings suggest that the adaptivity of the kind we used leads to a higher efficiency of learning (without an adverse effect on learning outcomes, learners go through the course faster and attempt fewer problems, since the problems are served to them in a targeted way). Further research is needed to confirm these findings and explore additional possible effects.