Can peer instruction be effective in upper-division computer science courses?
Can peer instruction be effective in upper-division computer science courses?
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同伴指导在高级计算机科学课程中能否有效？
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Leo Porter
中科院分区：
文献类型：
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作者：
C. Lee;Saturnino Garcia;Leo Porter
同伴教学是一种主动学习的教学方法。PI讲座为学生提供了一系列的多项选择题，他们可以单独或分组回答。PI在物理科学领域取得了广泛的成功，最近，它已被计算机科学教师成功地应用于低年级的入门课程。在这项工作中，我们挑战读者考虑PI为他们的高年级课程以及。我们提出了两个高年级计算机科学课程的PI课程：计算机体系结构和计算理论。这些课程包括在高年级课程中采用PI的几个公认的挑战，包括：抽象思想的探索，工程设计权衡的高层次判断的发展，以及行使先进的数学复杂性。这项工作包括选定的课程材料，说明如何克服这些挑战，学习收益的结果比较这些上师课程与以前的低师的结果在文献中，学生的态度调查结果（N = 501），务实的建议，未来的开发商和采用者。我们提出了三个主要发现。首先，我们发现，这些高年级课程取得了学生的学习收益相当于那些成功的低年级计算课程。第二，我们发现学生对每门课的反馈都是非常积极的，88%的学生建议在其他计算机科学课程中使用PI。第三，我们发现，采用这里介绍的材料的教师能够复制的教师谁开发的材料方面的学生学习收益和学生反馈的结果。
Peer Instruction (PI) is an active learning pedagogical technique. PI lectures present students with a series of multiple-choice questions, which they respond to both individually and in groups. PI has been widely successful in the physical sciences and, recently, has been successfully adopted by computer science instructors in lower-division, introductory courses. In this work, we challenge readers to consider PI for their upper-division courses as well. We present a PI curriculum for two upper-division computer science courses: Computer Architecture and Theory of Computation. These courses exemplify several perceived challenges to the adoption of PI in upper-division courses, including: exploration of abstract ideas, development of high-level judgment of engineering design trade-offs, and exercising advanced mathematical sophistication. This work includes selected course materials illustrating how these challenges are overcome, learning gains results comparing these upper-division courses with previous lower-division results in the literature, student attitudinal survey results (N = 501), and pragmatic advice to prospective developers and adopters. We present three main findings. First, we find that these upper-division courses achieved student learning gains equivalent to those reported in successful lower-division computing courses. Second, we find that student feedback for each class was overwhelmingly positive, with 88% of students recommending PI for use in other computer science classes. Third, we find that instructors adopting the materials introduced here were able to replicate the outcomes of the instructors who developed the materials in terms of student learning gains and student feedback.