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Seamless Embedding of Programming IDEs into Computer-Based Testing Software

Published: 03 March 2022 Publication History

Abstract

Interest in computer-based assessment has increased in recent years, most certainly due to a shift to online learning due to the COVID pandemic. Instructors are creating questiongenerators for Computer Science classes on PrairieLearn (PL), an open-source platform developed at the University of Illinois at Urbana-Champaign PrairieLearn. The software generates differentvariants of each question to students through randomization. The challenge up to now has been that automatically graded coding problems in RISC-V or Snap!, some of the significant languages used in undergraduate Computer Science courses at our university, weren't possible to do within the software. Thequestion could be displayed, but then the student would have to load their favorite integrated development environment (IDE), code it up, and thenreturn to PL to upload their solution. This poster discusses our approach to embedding interactive development environments for Venus (RISC-V) and Snap! directly into PrairieLearn, so students never have to leave the browser tab!

References

[1]
UC Berkeley. 2021. Snap! (Build Your Own Blocks). http://snap.berkeley.edu
[2]
RISC-V Consortium. 2021. RISC-V International. https://riscv.org
[3]
UIUC. 2021. PrairieLearn. https://www.prairielearn.org/

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  1. Seamless Embedding of Programming IDEs into Computer-Based Testing Software

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    cover image ACM Conferences
    SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2
    March 2022
    254 pages
    ISBN:9781450390712
    DOI:10.1145/3478432
    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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    New York, NY, United States

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    Published: 03 March 2022

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    1. computer-based testing
    2. mastery learning

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

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