Why do CS1 Students Think They're Bad at Programming?

Gorson, Jamie; O’Rourke, Eleanor

doi:10.1145/3372782.3406273

Cited by 59 publications

(22 citation statements)

References 40 publications

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“…There has been a plethora of investigations into root causes for this phenomenon (Ihantola et al, 2015;Quille & Bergin, 2019) as well as explorations of potential interventions (Sobral, 2020) and alternative pedagogies which might address the situation (Salguero et al, 2020). Given that the problem has been studied since the mid-1980s (Butcher & Muth, 1985) and still consistently plagues undergraduate computer science education (Bornat & Dehnadi, 2008;Gorson & O'Rourke, 2020), there is merit in continuing to study the problem. Accordingly, the goal of this research was to find the earliest point in the course assessment sequence it might be possible to predict final grade outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Other work has found improvement in retention and CS1 pass rates through media computation, paired programming, and peer instruction (Salguero et al, 2020). Studies focused on student self-assessment and self-efficacy determined that self-predictions were seldom accurate and discovered that students reporting more frequent negative self-assessment had lower self-efficacy (Gorson & O'Rourke, 2020;Sobral, 2020). Quille and Bergin's (2019) predictive model developed over 13 years was found to be 71% effective in determining early success, can identify weak students at 80-89% accuracy leading to intervention that improved grades up to 9%.…”

Section: Related Workmentioning

confidence: 99%

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How Early Is Early Enough: Correlating Student Performance With Final Grades

2021

JHETP

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Student retention is one of the greatest challenges facing computer science programs. Difficulties in an introductory programming class often snowball, resulting in poor student performance or dropping the major completely. In this paper, we present an analysis of 197 students over 6 semesters from 11 sections of an introductory programming class at a private four-year liberal arts university in the southeastern United States. The goal of this research was to find the earliest point in the assessment sequence which could predict final grade outcomes. Accordingly, we measured the degree of correlation between student performance on quizzes, labs, programs, and tests compared to final course grade. Overall, the results show a strong positive correlation for all four assessment modalities. These results hold significance for educators and researchers insofar as the body of computing education research is extended by evaluating the relative effectiveness of early semester subsets of each of the four categories of student data to model class outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

How Early Is Early Enough: Correlating Student Performance With Final Grades

2021

JHETP

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“…Several papers [1][2] [3], show that strong students' self-efficacy has an influence on their ability to learn programming and tackle many kinds of problems which arise (compiler message, logical faults, etc.). Students with a lower self-efficacy have a higher negative self-assessment.…”

Section: Observation and Theorical Foundationmentioning

confidence: 99%

“…J et.al. [1] studied why students think they are bad at programming by using self-assessment by using 13 moments of programming. The study was carried out in computer science courses for novice programmers at.…”

Section: Observation and Theorical Foundationmentioning

confidence: 99%

Introduction of process in embedded programming supporting students’ self-efficacy - case study

Schultz¹,

Błaszczyk²

2022

Towards a New Future in Engineering Education, New Scenarios That European Alliances of Tech Universities Open Up

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This work is directed towards the improvement of programming skills for students on the 2nd semester of Electrical Engineering (EE) BEng signed up for the “Digital Electronics and Programming” (DEP) course. The goal is to support students who tend to drop out in the first half of the semester or give up and not show up at oral exams. In our research hypothesis we state: Decreasing and eliminating negative emotional experiences will increase student’s self-efficacy thereby lowering dropout. We experiment with a programming process guide in text and video to gain students' capability in solving programming problems and increase metacognitive awareness. Additionally, we measure students' emotional experience of programming by using a special self-assessment vignette inquiry. For comparison purposes we introduced the same measurement methodology in 2021 on the courses where there is no focus on the process guideline. The results show a positive effect on lowering the negative emotional impact when students use a programming process guide. The article describes the theoretical background based on literature studies for both the process and the students’ self-assessment, and discusses results achieved in relation to the findings in the literature. The preliminary results are described in [8] and in paper here we present recent outcomes for the autumn 2021 and spring 2022 semester over 9 weeks.

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“…CER work has found that learning to write programs is difficult. Investigations have focused on student factors like self-efficacy [9], teaching strategies like pair programming, or ways to sidestep program writing altogether [6]. Most of the studies that have analyzed keystroke data on program writing have focused on the time between two successive key presses.…”

Section: Related Workmentioning

confidence: 99%

Towards an Understanding of Program Writing as a Cognitive Process: Analysis of Keystroke Logs

Davidson

2021

Proceedings of the 17th ACM Conference on International Computing Education Research

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Program writing is difficult to teach, learn, and assess. One challenge is a lack of theory or understanding about what program writing is. My dissertation will address this challenge by applying theories from natural language (NL) writing to try and understand program writing as a cognitive process. By analyzing keystroke logs collected during program writing, I plan to identify similarities with NL writing, potential diagnostic information, and how program writing changes as students become more proficient.

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Why do CS1 Students Think They're Bad at Programming?

Cited by 59 publications

References 40 publications

How Early Is Early Enough: Correlating Student Performance With Final Grades

How Early Is Early Enough: Correlating Student Performance With Final Grades

Introduction of process in embedded programming supporting students’ self-efficacy - case study

Towards an Understanding of Program Writing as a Cognitive Process: Analysis of Keystroke Logs

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