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Authoring Semi-automated Feedback for Python Code with Pedal

Published: 05 March 2021 Publication History

Abstract

This demo introduces attendees to Pedal, a Python framework that streamlines the process of authoring semi-automated feedback on students? Python code. As a pure Python package, Pedal is compatible with a wide range of autograding platforms, including GradeScope, VPL, WebCAT, and BlockPy - as long as the platform allows package installation, Pedal should work. Pedal is a collection of modular program analysis tools exposed with a declarative interface, built around a centralized infrastructure. These tools include a sandboxed execution environment for running students' code with enhanced tracebacks, pattern matching syntax for specifying common student mistakes, basic type inference and flow analysis, random question pools, and a library of over 60 high-level, pedagogically-oriented assertions. Pedal's model for these tools synthesizes the detection of conditions and their instructor-mediated responses, encapsulated into dedicated feedback functions that can be tracked and modified as first-class objects. Our goal is to elevate Feedback with Software Engineering and Instructional Design practices, to become a central part of your course's development rather than an afterthought. Our toolchain also includes command lines utilities for unit testing your feedback to verify behavior and analyze collected programming snapshot data. Our hope is that adoptees will find that Pedal expands the power of their autograder and opens new avenues of research.

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Published In

cover image ACM Conferences
SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
March 2021
1454 pages
ISBN:9781450380621
DOI:10.1145/3408877
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 March 2021

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Author Tags

  1. autograder
  2. autograding
  3. blockpy
  4. cs1
  5. feedback
  6. gradescope
  7. grading
  8. pedal
  9. program analysis
  10. python
  11. webcat

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SIGCSE '21
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Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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SIGCSE TS 2025
The 56th ACM Technical Symposium on Computer Science Education
February 26 - March 1, 2025
Pittsburgh , PA , USA

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