Stepping up to integrative questions on CS1 exams

Zingaro, Daniel; Petersen, Andrew; Craig, Michelle

doi:10.1145/2157136.2157215

Cited by 22 publications

(19 citation statements)

References 13 publications

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“…Another line of explanation implicates instructors' assessment tools as the source of bimodally distributed grades [33,23]. A common trend on CS exams is to ask a series of long-answer coding questions.…”

Section: Lousy Assessmentmentioning

confidence: 99%

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Evidence That Computer Science Grades Are Not Bimodal

Patitsas

Berlin

Craig

et al. 2016

Proceedings of the 2016 ACM Conference on International Computing Education Research

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Although it has never been rigourously demonstrated, there is a common belief that CS grades are bimodal. We statistically analyzed 778 distributions of final course grades from a large research university, and found only 5.8% of the distributions passed tests of multimodality. We then devised a psychology experiment to understand why CS educators believe their grades to be bimodal. We showed 53 CS professors a series of histograms displaying ambiguous distributions and asked them to categorize the distributions. A random half of participants were primed to think about the fact that CS grades are commonly thought to be bimodal; these participants were more likely to label ambiguous distributions as "bimodal". Participants were also more likely to label distributions as bimodal if they believed that some students are innately predisposed to do better at CS. These results suggest that bimodal grades are instructional folklore in CS, caused by confirmation bias and instructor beliefs about their students.

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Section: Lousy Assessmentmentioning

confidence: 99%

“…When the different conceptual parts of the question were broken up, the resulting grades were normally distributed, whereas the long-answer questions led to grades that the authors described as bimodal [33].…”

Section: Lousy Assessmentmentioning

confidence: 99%

Evidence That Computer Science Grades Are Not Bimodal

Patitsas

Berlin

Craig

et al. 2016

Proceedings of the 2016 ACM Conference on International Computing Education Research

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“…Students learn from their discussions with peers [18] and students and teachers alike value the pedagogy for its effects on learning and argumentation [17]. See the recent reviews [29,28] for further commentary on these and other benefits.…”

Section: Peer Instructionmentioning

confidence: 99%

Peer instruction contributes to self-efficacy in CS1

Zingaro

2014

Proceedings of the 45th ACM Technical Symposium on Computer Science Education

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106

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Recent work in computing suggests that Peer Instruction (PI) is a valuable interactive learning pedagogy: it lowers fail rates, increases retention, and is enjoyed by students and instructors alike. While these findings are promising, they are somewhat incidental if our goal is to understand whether PI is "better" than lecture in terms of student outcomes. Only one recent study in computing has made such a comparison, finding that PI students outperform traditionallytaught students on a CS0 final exam. That work was conducted in a CS0, where the same instructor taught both courses, and where the only outcome measure was final exam grade. Here, I offer a study that complements their work in two ways. First, I argue for and measure self-efficacy as a valued outcome, in addition to that of final exam grade. Second, I offer an inter-instructor CS1 study, whose biases differ from those of intra-instructor studies. I find evidence that PI significantly increases self-efficacy and suggestively increases exam scores compared to a traditional lecture-based CS1 class. I note validity concerns of such an in-situ study and offer a synthesis of this work with the extant PI literature.

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“…Concept questions are small codewriting questions developed to target single concepts rather than multiple concepts, as is typical of code-writing questions. They found that concept questions were highly predictive of performance on the exam as a whole, suggesting that the ability to write small pieces of code from scratch is a valued outcome in CS1 [18]. This further suggests that codebased ConcepTests, which also focus on a small number of expected misconceptions, may also be effective performance evaluators.…”

Section: Introductionmentioning

confidence: 99%

Facilitating code-writing in PI classes

Zingaro

Cherenkova

Karpova

et al. 2013

Proceeding of the 44th ACM Technical Symposium on Computer Science Education

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We present the Python Classroom Response System, a webbased tool that enables instructors to use code-writing and multiple choice questions in a classroom setting. The system is designed to extend the principles of peer instruction, an active learning technique built around discussion of multiplechoice questions, into the domain of introductory programming education. Code submissions are evaluated by a suite of tests designed to highlight common misconceptions, so the instructor receives real-time feedback as students submit code. The system also allows an instructor to pull specific submissions into an editor and visualizer for use as in-class examples. We motivate the use of this system, describe its support for and extension of peer instruction, and offer use cases and scenarios for classroom implementation.

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Stepping up to integrative questions on CS1 exams

Cited by 22 publications

References 13 publications

Evidence That Computer Science Grades Are Not Bimodal

Evidence That Computer Science Grades Are Not Bimodal

Peer instruction contributes to self-efficacy in CS1

Facilitating code-writing in PI classes

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