Background Research

This workshop is grounded in our previous research. On this page, we present a brief summary and provide references for additional reading.

• Previous research [5] has shown that students often self-assess their programming abilities negatively in moments that are typical in the programming process
• For example, a student may believe they are doing poorly when they need to stop to plan on their assignment, even though their professors would expect them to do that as a part of the programming process.
• Women also tend to be more self-critical of their programming abilities in these moments, and students of all genders tend to be more self-critical when comparing themselves to women [6]
• Our recent work [1] has explored students' reasons for their self-assessments (e.g., they think that they should know enough to not need to plan), as well as *sources* of information for their self-assessment criteria (e.g., they see their peers work without planning)
• We found that students cited their professors as powerful sources for self-assessment criteria, but these sources are not available for all self-assessment moments.
In lieu of information about what is expected of them as learners, students make assumptions from incomplete information about what is expected of them.
• This motivates our desire to co-design with faculty interventions that leverage students’ trust in their professor to provide sources of self-assessment criteria to students in a scalable manner.

References

[1] Melissa Chen, Yinmiao Li, and Eleanor O’Rourke. 2024. Understanding the Reasoning Behind Students’ Self-Assessments of Ability in Introductory Computer Science Courses. In ACM Conference on International Computing Education Research V.1 (ICER ’24 Vol. 1), August 13–15, 2024, Melbourne, VIC, Australia. ACM, New York, NY, USA, 13 pages. [PDF]

[2] Jamie Gorson, Kathryn Cunningham, Marcelo Worsley, and Eleanor O’Rourke. 2022. Using Electrodermal Activity Measurements to Understand Student Emotions While Programming. In Proceedings of the 2022 ACM Conference on International Computing Education Research V.1, August 03, 2022. ACM, Lugano and Virtual Event Switzerland, 105–119. [LINK]

[3] Jamie Gorson, Nicholas LaGrassa, Cindy Hsinyu Hu, Elise Lee, Ava Marie Robinson, and Eleanor O’Rourke. 2021. An Approach for Detecting Student Perceptions of the Programming Experience from Interaction Log Data. In Artificial Intelligence in Education, Ido Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin and Vania Dimitrova (eds.). Springer International Publishing, Cham, 150–164. [LINK]

[4] Jamie Gorson and Eleanor O’Rourke. 2019. How Do Students Talk About Intelligence?: An Investigation of Motivation, Self-efficacy, and Mindsets in Computer Science. In Proceedings of the 2019 ACM Conference on International Computing Education Research, July 30, 2019. ACM, Toronto ON Canada, 21–29. [LINK]

[5] Jamie Gorson and Eleanor O’Rourke. 2020. Why do CS1 Students Think They’re Bad at Programming? Investigating Self-efficacy and Self-assessments at Three Universities. In Proceedings of the 2020 ACM Conference on International Computing Education Research (ICER ’20), August 07, 2020. Association for Computing Machinery, New York, NY, USA, 170–181. [LINK]

[6] Jamie Gorson and Eleanor O’Rourke. 2021. CS1 Student Assessments of Themselves Relative to Others: The Role of Self-Critical Bias and Gender. ISLS (2021), 597–600. [LINK]

[7] Yinmiao Li, Melissa Chen, Ayse Hunt, Haoqi Zhang, and Eleanor O’Rourke. 2024. Exploring the Interplay of Metacognition, Affect, and Behaviors in an Introductory Computer Science Course for Non-Majors. In ACM Conference on International Computing Education Research V.1 (ICER ’24 Vol. 1), August 13–15, 2024, Melbourne, VIC, Australia. ACM, New York, NY, USA, 15 pages. [PDF]