This content originally appeared on HackerNoon and was authored by Pair Programming AI Agent
Table of Links
3.1 Instruments and 3.2 Quantitative Analysis
5 Discussion and 5.1 Answering the research questions
5.2 Threats to validity and 5.3 Limitations and generalizability
6 Conclusion, Acknowledgments, and References
4 Results
4.1 Quantitative Results
Five of the 35 students in two university classrooms were females, and thirty were males. The students’ software engineering experience ranged from zero to five years, with a mean of 1.6 years and a standard deviation of 1.3 years.
\ We can assume the normality of the reported motivational levels from the output obtained in the Shapiro-Wilk normality test (W = 0.98694, p-value = 0.4713). The p-value is greater than 0.05. Hence, the distribution of the reported intrinsic motivational levels is not significantly different from the normal distribution and permits the usage of parametric tests such as ANOVA.
\ From the output obtained in a one-way ANOVA test (F-value = 6.618, p-value = 0.00206 **), where the p-value is less than α = .05, we successfully reject the null hypothesis that the groups representing chosen programming roles are similar in reported motivational level. This translates to the variance between those groups being large relative to their internal variance, and, therefore, concluding that this nominal variable (chosen programming role) holds distinct relations toward motivation.
\ The reported intrinsic motivational levels in the programming roles of Pilot (μ = 28.41, σ = 2.91), Navigator (μ = 27.87, σ = 3.24), and Solo (μ = 25.11, σ = 2.66) are reported in Table I with their respective means and standard deviations.
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4.2 Qualitative Results
Seven overarching themes were discovered that depict the psychological aspects of pair programming in educational settings. The themes and the codes that established them are displayed in Table II. The first column identifies the themes. Authentic excerpts in the second column capture the students’ mindsets. The codification elements that led to the creation of the themes are listed in the third column. Finally, observed subthemes are presented in column number four.
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:::info Author:
(1) Marcel Valový, Department of Information Technologies, Prague, Czech Republic (marcel.valovy@vse.cz).
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:::info This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Pair Programming AI Agent
Pair Programming AI Agent | Sciencx (2025-02-10T16:12:21+00:00) How Pair Programming Affects Student Motivation and Learning. Retrieved from https://www.scien.cx/2025/02/10/how-pair-programming-affects-student-motivation-and-learning/
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