Marriott, C., & Abraham, M., & Dillon, H. E. (2023, June), Labor-based Grading in Computer Science: A Student-Centered Practice  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43927

\bibitem{asee_peer_43927}
  Marriott, C., \& Abraham, M., \& Dillon, H. E. (2023, June), \emph{Labor-based Grading in Computer Science: A Student-Centered Practice}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43927

Chris Marriott, Menaka Abraham, and Heather E. Dillon.  "Labor-based Grading in Computer Science: A Student-Centered Practice".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://sftp.asee.org/43927 Internet.  21 Jun, 2024

\bibitem{asee_peer_43927}
  Chris Marriott, Menaka Abraham, and Heather E. Dillon.
  "Labor-based Grading in Computer Science: A Student-Centered Practice".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://sftp.asee.org/43927 Internet.  21 Jun, 2024

@INPROCEEDINGS{asee_peer_43927
author = "Chris Marriott, Menaka Abraham, and Heather E. Dillon"
title = "Labor-based Grading in Computer Science: A Student-Centered Practice"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://sftp.asee.org/43927}
number = {10.18260/1-2--43927}
}

TY  - CPAPER
AB  - Innovation in teaching in STEM fields was explored widely during the COVID pandemic in 2020. This paper describes the adaptation of labor based grading for computer science courses. Labor based grading has been developed for language and writing courses by shifting the grading focus from summative exams to formative and reflective assessments. The method was tested in several computer science courses with two different instructors during the 2020-2021 academic year. Students were surveyed to understand how they perceived grading methods in the course and their own level of anxiety. A total of 69 students completed the survey where 84% reported the method reduced anxiety (4 or 5 on a Likert scale). The study found that labor based grading was an effective way to reduce student anxiety, reduce academic integrity issues, and improve student motivation. 
AU  - Chris Marriott
AU  - Menaka Abraham
AU  - Heather E. Dillon
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Labor-based Grading in Computer Science: A Student-Centered Practice
UR  - https://sftp.asee.org/43927
DO  - 10.18260/1-2--43927
ER  -