Facilitating code-writing in PI classes

Zingaro, Daniel; Cherenkova, Yuliya; Karpova, Olessia; Petersen, Andrew

doi:10.1145/2445196.2445369

Cited by 37 publications

(16 citation statements)

References 16 publications

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“…The instructors occasionally used the web-based Python Classroom Response System (PCRS) [15] to pose programming questions from the worksheets. Students who brought an internet-enabled device (typically a laptop or tablet) could submit their work electronically, and the system ran a set of test cases against their submissions to check for correctness.…”

Section: Course Deliverymentioning

confidence: 99%

Evaluating an inverted CS1

Campbell

Horton

Craig

et al. 2014

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

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This case study explores an inverted classroom offering of an introductory programming course (CS1). Students prepared for lecture by watching short lecture videos and completing required in-video quiz questions. During lecture, the students worked through exercises with the support of the instructor and teaching assistants. We describe the course implementation and its assessment, including pre-and postcourse surveys. We also discuss lessons learned, modifications that we plan to make for the next offering, and recommendations for others teaching inverted courses.

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Section: Course Deliverymentioning

confidence: 99%

Evaluating an inverted CS1

Campbell

Horton

Craig

et al. 2014

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

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“…The goal of teaching CS students to write code is among the most important goals for CS majors. Although work has focused on teaching concepts through Peer Instruction, active learning that addresses the higher-level skill of writing code has not been as well developed [2]. Writing code to solve problems, the industrystandard interview test to find capable job candidates, is a higherlevel skill (i.e., application level in Bloom's taxonomy) than the lower levels of knowledge and conceptual understanding.…”

Section: Background and Related Workmentioning

confidence: 99%

Active Learning with Code Writing in Large Lectures

Klefstad

2020

Proceedings of the 51st ACM Technical Symposium on Computer Science Education

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Studies have found that many CS students perform poorly on basic programming tasks, and interviewers in industry complain that CS graduates cannot write code. Learning gains have been shown from active learning techniques such as Peer Instruction, but only for multiple-choice questions, not for writing code. Studies of multiple choice questions such as code tracing have shown weak correlations to code writing, and extremely small improvements in syntax in only one third of students. These results are consistent with a

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“…Coding exercises with automatic assessment. The system asks user to complete a partial code skeleton and then, it checks the submitted answer using a set of tests [21].…”

Section: Concept-level Design Analytics For Blended Learningmentioning

confidence: 99%

Concept-Level Design Analytics for Blended Courses

Albó

Barria-Pineda

Brusilovsky

et al. 2019

Lecture Notes in Computer Science

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Although many efforts are being made to provide educators with dashboards and tools to understand student behaviors within specific technological environments (learning analytics), there is a lack of work in supporting educators in making data-informed design decisions when designing a blended course and planning learning activities. In this paper, we introduce concept-level design analytics, a knowledge-based visualization, which uncovers facets of the learning activities that are being authored. The visualization is integrated into a (blended) learning design authoring tool, edCrumble. This new approach is explored in the context of a higher education programming course, where teaching assistants design labs and home practice sessions with online smart learning content on a weekly basis. We performed a within-subjects user study to compare the use of the design tool both with and without the visualization. We studied the differences in terms of cognitive load, design outcomes and user actions within the system to compare both conditions to the objective of evaluating the impact of using design analytics during the decision-making phase of course design.

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Facilitating code-writing in PI classes

Cited by 37 publications

References 16 publications

Evaluating an inverted CS1

Evaluating an inverted CS1

Active Learning with Code Writing in Large Lectures

Concept-Level Design Analytics for Blended Courses

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