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CS1 Student Grade Prediction: Unconscious Optimism vs Insecurity?

Sónia Rolland Sobral

Abstract—The difficulties of many students in introductory programming courses and the consequent failure and drop out make it necessary to look for motivation strategies for them to be successful. One of the strategies that is touted in the literature is self-assessment to compromise and motivate students. As we had doubts about the possibility of this strategy, we did an experiment and asked the students to predict the grades of the two tests and the two projects during a semester. Even knowing the correction grid and exercises that involve programming languages, which shows the result to the programmer, we found that the students' forecasts were not very accurate. In the first test we found that the worst students said they were going to get reasonable grades and much better than reality, while the best students thought they had worse grades than they actually had. The other moments of evaluation did not have as severe results, but forecasts continued to be inaccurate. We did tests by gender, by age, for being a freshman or not, for having taken a computer course in high school and for previous knowledge of programming languages: none of these variables proved to be as significant as the students' grades and their corresponding insecurity-fear or optimism-unconscious.

Index Terms—CS1, grade predict, introduction to programming, motivation strategies.

Sónia Rolland Sobral is with REMIT, Universidade Portucalense, Porto, Portugal (e-mail: sonia@upt.pt).


Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (Since 2019), INSPEC (IET), UGC-CARE List (India), EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
  • E-mail: ijiet@ejournal.net

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