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Integrating Programming Learning Analytics Across Physical and Digital Space
IEEE Transactions on Emerging Topics in Computing ( IF 5.9 ) Pub Date : 2020-01-01 , DOI: 10.1109/tetc.2017.2701201
I-Han Hsiao , Po-Kai Huang , Hannah Murphy

In this work, we study students' learning effectiveness through their use of a homegrown innovative educational technology, Web Programming Grading Assistant (WPGA), which facilitates grading and feedback delivery of paper-based assessments. We designed a lab study and a classroom study from a lower-division blended-instruction computer science course. We evaluated a partial credit assignment algorithm. We tracked and modeled students’ learning behaviors through their use of WPGA. Results showed that students demonstrated an effort and desire to review assessments regardless of if they were graded for academic performance or for attendance. Diligent students achieved higher exam scores on average and were found to review their exams and the correct questions frequently. Additionally, student cohorts exhibited similar initial reviewing patterns, but different in-depth reviewing and reflecting strategies. Ultimately, the work contributes to multidimensional learning analytics aggregation across the physical and cybersphere.

中文翻译:

跨物理和数字空间集成编程学习分析

在这项工作中,我们通过使用本土创新教育技术网络编程评分助手 (WPGA) 来研究学生的学习效率,该技术有助于纸质评估的评分和反馈交付。我们从低年级混合教学计算机科学课程中设计了一项实验室研究和一项课堂研究。我们评估了部分信用分配算法。我们通过使用 WPGA 跟踪和模拟学生的学习行为。结果表明,学生表现出努力和渴望审查评估,无论他们是根据学业成绩还是出勤率评分。勤奋的学生平均会获得更高的考试成绩,并且会经常复习他们的考试和正确的问题。此外,学生群体表现出类似的初始审查模式,但不同的深入回顾和反思策略。最终,这项工作有助于跨物理和网络领域的多维学习分析聚合。
更新日期:2020-01-01
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