UpGrade: Sourcing Student Open-Ended Solutions to Create Scalable Learning Opportunities

UpGrade: Sourcing Student Open-Ended Solutions to Create Scalable Learning Opportunities
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UpGrade:寻找学生开放式解决方案以创造可扩展的学习机会

DOI:
10.1145/3330430.3333614
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发表时间:
2019
期刊:
Proceedings of the Sixth (2019
影响因子:
--
通讯作者:
Koedinger, Kenneth
Koedinger, Kenneth
中科院分区:
--
文献类型:
--
作者:
Wang, Xu;Talluri, Srinivasa Teja;Rose, Carolyn;Koedinger, Kenneth

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在世界各地的学校和大学中,普遍使用开放式家庭作业。然而,此类作业需要教师付出大量努力来评分,并且往往不支持重复练习的机会。我们提出了 UpGrade,这是一种新颖的学习者采购方法,它利用学生之前对开放式问题的解决方案来产生可扩展的学习机会。 UpGrade 创建交互式问题,提供自动化和实时反馈,同时支持重复练习。在大学水平 HCI 课程中进行的为期两周的实验中,学生回答 UpGrade 创建的问题而不是传统的开放式作业,在大约 30% 的时间内取得了无可比拟的学习成果。此外,不需要手动评分。为了加强质量控制,UpGrade 采用了心理测量方法,利用众包工作者的答案自动剔除低质量问题,从而形成超出课堂使用可靠性标准的题库。
In schools and colleges around the world, open-ended home-work assignments are commonly used. However, such assignments require substantial instructor effort for grading, and tend not to support opportunities for repeated practice. We propose UpGrade, a novel learnersourcing approach that generates scalable learning opportunities using prior student solutions to open-ended problems. UpGrade creates interactive questions that offer automated and real-time feedback, while enabling repeated practice. In a two-week experiment in a college-level HCI course, students answering UpGrade-created questions instead of traditional open-ended assignments achieved indistinguishable learning outcomes in ~30% less time. Further, no manual grading effort is required. To enhance quality control, UpGrade incorporates a psychometric approach using crowd workers' answers to automatically prune out low quality questions, resulting in a question bank that exceeds reliability standards for classroom use.
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