Pair programming is a collaborative learning mode to foster novice learners’ computer programming. Previous empirical research has reported contrasting conclusions about the effect of pair programming on student learning. To further understand students’ pair programming, this study uses a mixed method to analyze three contrasting pairs’ collaborative behaviors, discourses, and perceptions from a multi-dimensional perspective. The analysis results show that the high-ranked student pair is characterized as the interactive, socially-supportive, and goal-oriented pair; the middle-ranked student pair is characterized as the highly-interactive, socially-supportive, and process-oriented pair; and the low-ranked student pair is characterized as the lowly-interactive, socially-unsupportive, and programming-distracted pair. The research reveals complex relations between collaborative behaviors, discourses, and performances, which have critical influences on students’ pair programming quality. Based on the results, this research proposes pedagogical, analytical, and theoretical implications for future instructional design, learning analytics, and empirical research of collaborative programming.

ChatGPT has proven to facilitate computer programming tasks through the strategic use of prompts, which effectively steer the interaction with the language model towards eliciting relevant information. However, the impact of specifically designed prompts on programming learning outcomes has not been rigorously examined through empirical research. This study adopted a quasi-experimental framework to investigate the differential effects of prompt-based learning (PbL) versus unprompted learning (UL) conditions on the programming behaviors, interaction qualities, and perceptions of college students. The study sample consisted of 30 college students who were randomly assigned to two groups. A mixed-methods approach was employed to gather multi-faceted data. Results revealed notable distinctions between the two learning conditions. First, the PbL group students frequently engaged in coding with Python and employed debugging strategies to verify their work, whereas their UL counterparts typically transferred Python code from PyCharm into ChatGPT and posed new questions within ChatGPT. Second, PbL participants were inclined to formulate more complex queries independently, prompted by the guiding questions, and consequently received more precise feedback from ChatGPT compared to the UL group. UL students tended to participate in more superficial-level interactions with ChatGPT, yet they also obtained accurate feedback. Third, there were noticeable differences in perception observed before and after the ChatGPT implementation, UL group reported a more favorable perception in the perceived ease of use in the pre-test, while the PbL group experienced an improvement in their mean scores for perceived usefulness, ease of use, behavioral intention to utilize, and a significant difference regarding the attitude towards utilizing ChatGPT. Specifically, the use of structured output and delimiters enhanced learners’ understanding of problem-solving steps and made learning more efficient with ChatGPT. Drawing on these outcomes, the study offers recommendations for the incorporation of ChatGPT into future instructional designs, highlighting the structured prompting benefits in enhancing programming learning experience.

Although previous research has provided some insights into the effects of block-based and text-based programming modalities, there is a dearth of a detailed, multi-dimensional analysis of the transition process from different introductory programming modalities to professional programming learning. This study employed a quasi-experimental design to address this gap, involving 64 secondary school students in two groups. For the beginning five weeks, the first group used an introductory block-based programming environment, while the second group used an introductory text-based programming environment. Then, both groups transitioned to professional text-based programming for the subsequent eight weeks. The results showed that participants who transitioned from introductory text-based programming to professional text-based programming (1) significantly outperformed in computational thinking skills; (2) had more code-writing and debugging behaviors and fewer irrelevant behaviors, and (3) had more interactions with the instructor. No significant differences were observed between the two groups regarding enjoyment, confidence, and interest in programming. Drawing on these findings, this study proposes pedagogical implications that could facilitate the adoption of programming modalities within the broader context of STEM education.

Real-time collaborative programming (RCP), which allows multiple programmers to work concurrently on the same codebase with changes instantly visible to all participants, has garnered considerable popularity in higher education. Despite this trend, little work has rigorously examined how undergraduates engage in collaborative programming when using RCP. This study utilized a quasi-experimental design to investigate the impact of RCP on undergraduates’ programming knowledge and skills, behaviors, and attitudes toward collaboration. Eighty-two participants were randomly assigned to either the RCP group or the Traditional Collaborative Programming (TCP) group. The data analysis yielded noteworthy findings. First, RCP significantly improved learners’ problem-solving skills and led to higher knowledge test scores compared to TCP. Second, there were no significant differences in attitudes toward collaboration between the two groups. Third, analysis of programming behaviors indicated that the RCP group exhibited more efficient coding practices, including producing a greater volume of code and executing it more successfully. Additionally, the RCP group’s code-testing activities were predominantly concentrated at the first half of the class sessions. Based on the empirical research results, three major pedagogical implications were proposed. These findings highlight the benefits of integrating RCP into programming education and suggest avenues for future research into collaborative learning dynamics.