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The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches

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INTERNATIONAL JOURNAL OF STEM EDUCATION 2025年第1期12卷 1-17页

作者： Fan, Guangrui Liu, Dandan Zhang, Rui Pan, Lihu Taiyuan Univ Sci & Technol Sch Comp Sci & Technol 66 Waliu Rd Taiyuan 030024 Shanxi Peoples R China Univ Malaya Fac Arts & Social Sci Dept Media & Commun Studies Kuala Lumpur 50603 Wilayah Perseku Malaysia

Purpose This study investigates the impact of AI-assisted pair programming on undergraduate students' intrinsic motivation, programming anxiety, and performance, relative to both human-human pair programming and individual programming approaches. Methods A quasi-experimental design was conducted over two academic years (2023-2024) with 234 undergraduate students in a Java web application development course. Intact class sections were randomly assigned to AI-assisted pair programming (using GPT-3.5 Turbo in 2023 and Claude 3 Opus in 2024), human-human pair programming, or individual programming conditions. Data on intrinsic motivation, programming anxiety, collaborative perceptions, and programming performance were collected at three time points using validated instruments. Results Compared to individual programming, AI-assisted pair programming significantly increased intrinsic motivation (p < .001, d = 0.35) and reduced programming anxiety (p < .001), producing outcomes comparable to human-human pair programming. AI-assisted groups also outperformed both individual and human-human groups in programming tasks (p < .001). However, human-human pair programming fostered the highest perceptions of collaboration and social presence, surpassing both AI-assisted and individual conditions (p < .001). Mediation analysis revealed that perceived usefulness of the AI assistant significantly mediated the relationship between the programming approach and student outcomes, highlighting the importance of positive perceptions in leveraging AI tools for educational benefits. No significant differences emerged between the two AI models employed, indicating that both GPT-3.5 Turbo and Claude 3 Opus provided similar benefits. Conclusion While AI-assisted pair programming enhances motivation, reduces anxiety, and improves performance, it does not fully match the collaborative depth and social presence achieved through human-human pairing. These findings highlight the complementary streng

关键词： AI-assisted pair programming Intrinsic motivation programming anxiety Collaborative learning programming performance

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Effects of Intuition and Sensing in programming performance using MBTI personality model 18

Effects of Intuition and Sensing in Programming Performance ...

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2nd International Conference on Advances in Image Processing (ICAIP) / 2nd International Conference on Software Engineering and Development (ICSED

作者： Li, Xuechao Shih, Po-Chou Daniel, Yundt Concordia Univ Chicago Dept Comp Sci 7400 Augusta River Forest Chicago IL USA Natl Taipei Univ Technol Grad Inst Ind & Business Management Taipei Taiwan Concordia Univ Chicago Dept Comp Sci River Forest IL USA

ISBN: (纸本)9781450364607

In this paper, we describe an empirical study carried out to investigate the effects of Intuition and Sensing personality types in programming performance using Myer-Briggs Type Indicator (MBTI) model. This formal experiment was conducted at Auburn University and participants were first-year students in an introductory programming course. Our results showed: (1) Intuition and Sensing personality traits did affect students' programming performance;(2) students with the Intuition type significantly wrote more efficient code that students with Sensing type;and (3) a significant linear correlation existed between Intuition and programming performance.

关键词： Myers-Briggs Type Indicator programming performance Intuition Sensing Empirical Study

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The Effect of Software Programmers' Personality on programming performance

The Effect of Software Programmers' Personality on Programmi...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Li, Xuechao Shih, Po-Chou David, Evans Concordia Univ Chicago Dept Math & Comp Sci River Forest IL 60305 USA Natl Taipei Univ Technol Grad Inst Ind & Business Management Taipei 10608 Taiwan

ISBN: (纸本)9781538669877

The performance in computer science area has been researched for decades in both academia and industry. Generally, various optimization methods and proposed framework were employed to improve original system performance. But few researchers focus on influences of human factors on the programming performance from the prospective of programmers. In this paper, we empirically conducted a formal experiment to explore the relationship between Judging and Perceiving using Mayers-Briggs Type Indicator personality model and the programming performance. With three statistical analysis: Independent T-test, Pearson Correlation and Regression analysis, our findings showed that personality traits did affect the programming performance.

关键词： programming performance myers-briggs type indicator personality judging perceiving

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Analysis of programming performance Based on 2-grams of Keystrokes and Mouse Operations 21

Analysis of Programming Performance Based on 2-grams of Keys...

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21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023

作者： Matsumoto, Kazuki Nishiura, Kinari Sasakura, Mariko Monden, Akito Okayama University Graduate School of Natural Science and Technology Okayama Japan

ISBN: (纸本)9798350345889

Individual differences in programming performance are very large. For objectivity in programming performance, there is a strong need to quantitatively evaluate programming performance of individual developer. In this study, we conduct an experiment to measure programmers' behaviors including keystrokes and mouse operations of fourteen subjects, and analyze correlations between them and programming performance. The experimental results show that those who check a lot of program operation and those who use shortcut keys frequently had higher programming performance. In addition, subjects who spent a lot of time searching on the web, compiled a lot, and rewrote a lot of code had lower programming performance. © 2023 IEEE.

关键词： 2-gram analysis keystroke mouse click programming performance

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Classification and prediction-based machine learning algorithms to predict students' low and high programming performance

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2024年第1期32卷 e22679-e22679页

作者： Durak, Aykut Bulut, Vahide Katip Celebi Univ Dept Comp Engn Software Engn TR-35000 Izmir Turkiye

The current study aims to determine the programming performance (low and high) of the students between the ages of 12-24 who receive programming education, by descriptive analysis, to determine the current situation according to various variables and to redict them with machine learning algorithms. Thus, the change in programming performance, computational identity, computational thinking perspective, programming empowerment, and programming anxiety according to various variables was examined. The performances of different algorithms were compared in estimating the low and high programming performance of these variables. The research involved 763 students who were between the ages of 12 and 24 and had received programming education. Different scales were used to collect the opinions of students who received programming education. Descriptive analyses, one-way analysis of variance (ANOVA), and machine learning algorithms were used in the analysis of the data set. Analyzes were made in Statistical Package for the Social Sciences (SPSS) and Knime software. Decision trees, k-nearest neighbors, support vector machines, random forest, Naive Bayes classifiers, and logistic regression were used in the study. Students' programming performance, computational identity, computational thinking perspective, and programming empowerment mean scores differ in terms of gender and educational level variables. Research variables do not differ statistically in terms of academic success level variables. According to the programming performance level, students' computational identity, computational thinking perspective, and programming empowerment scores differ. No significant difference was found between programming anxiety scores. Decision trees of the algorithm with the highest accuracy result according to low and high programming performance conditions are [(0.966), (0.966)], respectively. The fact that these obtained scores are above 90% can be interpreted as sufficient estimation pe

关键词： computational identity computational thinking perspective descriptives machine learning algorithms programming anxiety programming empowerment programming performance

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Factors Affecting programming performance of First Year Students at a University in Botswana

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Journal of the Southern African Association for Research in Mathematics, Science and Technology Education 2018年第3期22卷 363-373页

One of the challenges encountered in programming courses offered at first year level is high failure rate, which is a serious concern to lecturers and university management. To address the issue of high failure rate, it is important to have a clear understanding of the factors that may affect performance. This study investigates the effect of gender and prior academic experience on performance in an introductory programming course. These factors have special significance related to the societal and educational context in which the study is conducted. Moreover, prior academic experience is commonly used as an admission criterion and hence its validity needs to be assessed. Data were collected from 146 first year computing students who took an introductory programming course during semester 1 of the 2016/2017 academic year. The study found a statistically significant correlation between high school performance in mathematics and physics (albeit moderate and weak correlations respectively) and performance in an introductory programming course. Surprisingly, the study found no statistically significant correlation between performance in high school computer studies (where programming is taught) and performance in the introductory programming course. A discussion of the potential reasons for this discrepancy and its implications is provided.

关键词： Success factors programming performance computing education failure rate

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EXPLORATORY EXPERIMENTAL STUDIES COMPARING ONLINE AND OFFLINE programming performance

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COMMUNICATIONS OF THE ACM 1968年第1期11卷 3-&页

作者： SACKMAN, H ERIKSON, WJ GRANT, EE System Development Corp. Santa Monica CA System Development Corp. Santa Monica CA System Development Corp. Santa Monica CA

Examines the debugging performance of programmers working under conditions of online and offline access to a computer. Indication of faster debugging under online conditions; Individual differences in programmer perfo... 详细信息

关键词： analysis of variance basic programming knowledge test debugging effectiveness experienced programmer study factor analysis application online vs. offline performance programmer individual differences programmer trainee performance programmer/computer communication programming cost effectiveness programming experimental-empirical studies programming performance time sharing vs. batch processing

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An investigation into keystroke latency metrics as an indicator of programming performance 05

An investigation into keystroke latency metrics as an indica...

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Conference on Australasian Computing Education

作者： Richard C. Thomas Amela Karahasanovic Gregor E. Kennedy The University of Western Australia Western Australia Simula Research Laboratory Lysaker Norway The University of Melbourne Parkville Vic Australia

ISBN: (纸本)9781920682248

Typing has long been studied in psychology and HCI, and strong cognitive models for transcription typing exist. The goal of the present research was to test if there is any correlation between students' keystroking speed and performance while they are programming. We present the results from two studies with computer science students conducted in different contexts. Keystroke timings were recorded while they worked on Java and Ada source code. Quality of their programming work was measured mainly in terms of completeness. In the controlled experiment that lasted six hours, 39 students undertook three change tasks on a 6000 LOC Java application. In the field study, data was collected over 6 weeks from 141 students while they worked unsupervised on Ada programming in first year laboratories. In both cases there were highly significant (P=0.001), moderately strong, negative correlations between speed and coding performance. With additional development, these techniques may have promise for user modelling and assessment as well as in educational diagnostics.

关键词： programming performance program performance programming Students Typing user modelling ADA (programming LANGUAGE) field studies computer human interaction Psychology

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Effects of Intuition and Sensing in programming performance using MBTI personality model

Effects of Intuition and Sensing in Programming Performance ...

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作者： Xuechao Li Po-Chou Shih Yundt Daniel Department of Computer Science Concordia University Graduate Institute of Industrial and Business Management Taipei University of Technology

In this paper,we describe an empirical study carried out to investigate the effects of Intuition and Sensing personality types in programming performance using Myer-Briggs Type Indicator(MBTI) *** formal experiment was conducted at Auburn University and participants were first-year students in an introductory programming *** results showed:(1) Intuition and Sensing personality traits did affect students' programming performance;(2) students with the Intuition type significantly wrote more efficient code that students with Sensing type;and(3) a significant linear correlation existed between Intuition and programming performance.

关键词： Myers-Briggs Type Indicator programming performance Intuition Sensing Empirical Study

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Predicting Low and High Student performance in programming Education Using PLS-SEM Algorithms

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TECHNOLOGY KNOWLEDGE AND LEARNING 2024年 1-18页

作者： Durak, Aykut Bulut, Vahide Necmettin Erbakan Univ Konya Eregli Kemal Akman Vocat Sch Dept Comp Technol Konya Turkiye Assoc Prof Izmir Katip Celebi Univ Dept Comp Engn Software Engn Izmir Turkiye

The study uses the partial least squares-structural equation modeling (PLS-SEM) algorithm to predict the factors affecting the programming performance (PPE) (low, high) of the students receiving computer programming education. The participants of the study consist of 763 students who received programming education. In the analysis of the data, the PLS-SEM method was used with the help of the SmartPLS 4 program. In addition, multigroup SEM was used to examine the differentiation of models between groups with low and high PPE. According to the research results, the percentage of explanation of the model is relatively high in the group with high PPE compared to the group with low performance. According to the findings of the study, age, education level, general academic achievement, and PPE scores were found to be related. In addition, programming experience, attitude, and programming empowerment are related to PPE. The most important of some of the limitations of this study is that the data collected from the participants are based on their self-reports. The results of this study may have important contributions to the differentiation of approaches toward low and high-performing students in supporting programming education. This type of research can help design relevant interventions for students experiencing failures in programming education.

关键词： programming training programming performance Computational identity Computational thinking Empowerment Anxiety

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