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Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses

Author

Listed:
  • Cristian D. González-Carrillo

    (Department of Systems and Industrial Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
    Current address: Google, Mountain View, CA 94043, USA.)

  • Felipe Restrepo-Calle

    (Department of Systems and Industrial Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

  • Jhon J. Ramírez-Echeverry

    (Department of Electrical and Electronics Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

  • Fabio A. González

    (Department of Systems and Industrial Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia)

Abstract

Jupyter notebooks provide an interactive programming environment that allows writing code, text, equations, and multimedia resources. They are widely used as a teaching support tool in computer science and engineering courses. However, manual grading programming assignments in Jupyter notebooks is a challenging task, thus using an automatic grader becomes a must. This paper presents UNCode notebook auto-grader, that offers summative and formative feedback instantaneously. It provides instructors with an easy-to-use grader generator within the platform, without having to deploy a new server. Additionally, we report the experience of employing this tool in two artificial intelligence courses: Introduction to Intelligent Systems and Machine Learning . Several programming activities were carried out using the proposed tool. Analysis of students’ interactions with the tool and the students’ perceptions are presented. Results showed that the tool was widely used to evaluate their tasks, as a large number of submissions were performed. Students expressed positive opinions mostly, giving feedback about the auto-grader, highlighting the usefulness of the immediate feedback and the grading code, among other aspects that helped them to solve the activities. Results remarked on the importance of providing clear grading code and formative feedback to help the students to identify errors and correct them.

Suggested Citation

  • Cristian D. González-Carrillo & Felipe Restrepo-Calle & Jhon J. Ramírez-Echeverry & Fabio A. González, 2021. "Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses," Sustainability, MDPI, vol. 13(21), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12050-:d:669631
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    References listed on IDEAS

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    1. Jeffrey M. Perkel, 2018. "Why Jupyter is data scientists’ computational notebook of choice," Nature, Nature, vol. 563(7729), pages 145-146, November.
    2. Jan Skalka & Martin Drlik & Lubomir Benko & Jozef Kapusta & Juan Carlos Rodríguez del Pino & Eugenia Smyrnova-Trybulska & Anna Stolinska & Peter Svec & Pavel Turcinek, 2021. "Conceptual Framework for Programming Skills Development Based on Microlearning and Automated Source Code Evaluation in Virtual Learning Environment," Sustainability, MDPI, vol. 13(6), pages 1-30, March.
    3. Aldo Gordillo, 2019. "Effect of an Instructor-Centered Tool for Automatic Assessment of Programming Assignments on Students’ Perceptions and Performance," Sustainability, MDPI, vol. 11(20), pages 1-24, October.
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    Cited by:

    1. Chenghong Cen & Guang Luo & Lujia Li & Yilin Liang & Kang Li & Tan Jiang & Qiang Xiong, 2023. "User-Centered Software Design: User Interface Redesign for Blockly–Electron, Artificial Intelligence Educational Software for Primary and Secondary Schools," Sustainability, MDPI, vol. 15(6), pages 1-27, March.

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