Improving Educational Outcomes: Developing and Assessing Grading System (ProGrader) for Programming Courses

Author:

Nafa Fatema,Sreeramareddy Lakshmidevi,Mallapuram Sriharsha,Moulema Paul

Publisher

Springer Nature Switzerland

Reference45 articles.

1. Khalid, A., Lundqvist, K., Yates, A.: A literature review of implemented recommendation techniques used in Massive Open online Courses. Expert Syst. Appl. 187, 115926 (2022)

2. Graf, S., Rita, S., Leo, T., Kinshuk, S.: In-depth analysis of the felder-silverman learning style dimensions. J. Res. Technol. Educ. 40(1), 79–93 (2007)

3. National Center for Education Statistics (NCES) Homepage, as part of the U.S. Department of Education. https://nces.ed.gov/programs/digest/d13/tables/dt13_303.10.asp. Accessed 2 Feb 2023

4. Gordon, C., Lysecky, R., Vahid, F.: The rise of program auto-grading in introductory CS courses: a Case Study of zyLabs. In: 2021 ASEE Annual Conference and Exposition, ASEE (American Society for Engineering Education), Virtual (2021)

5. Hsu, S., Li, T., Zhang, Z., Fowler, M., Karahalios, K.: Attitudes surrounding an imperfect AI Autograder. In: CHI 2021:Chi Conference on Human Factors in Computing Systems, pp. 1–15. ACM (Association for Computing Machinery), New York, USA (2021)

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