Psychological Aspects of Pair Programming
- URL: http://arxiv.org/abs/2306.07421v1
- Date: Mon, 12 Jun 2023 20:57:50 GMT
- Title: Psychological Aspects of Pair Programming
- Authors: Marcel Valov\'y
- Abstract summary: This study aims to gain quantitative and qualitative insights into pair programming.
The research's goal is to use the findings to design further studies on pairing with artificial intelligence.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: With the recent advent of artificially intelligent pairing partners in
software engineering, it is interesting to renew the study of the psychology of
pairing. Pair programming provides an attractive way of teaching software
engineering to university students. Its study can also lead to a better
understanding of the needs of professional software engineers in various
programming roles and for the improvement of the concurrent pairing software.
Objective: This preliminary study aimed to gain quantitative and qualitative
insights into pair programming, especially students' attitudes towards its
specific roles and what they require from the pairing partners. The research's
goal is to use the findings to design further studies on pairing with
artificial intelligence. Method: Using a mixed-methods and experimental
approach, we distinguished the effects of the pilot, navigator, and solo roles
on (N = 35) students' intrinsic motivation. Four experimental sessions produced
a rich data corpus in two software engineering university classrooms. It was
quantitatively investigated using the Shapiro-Wilk normality test and one-way
analysis of variance (ANOVA) to confirm the relations and significance of
variations in mean intrinsic motivation in different roles. Consequently, seven
semi-structured interviews were conducted with the experiment's participants.
The qualitative data excerpts were subjected to the thematic analysis method in
an essentialist way. Results: The systematic coding interview transcripts
elucidated the research topic by producing seven themes for understanding the
psychological aspects of pair programming and for its improvement in university
classrooms. Statistical analysis of 612 self-reported intrinsic motivation
inventories confirmed that students find programming in pilot-navigator roles
more interesting and enjoyable than programming simultaneously.
Related papers
- Code Interviews: Design and Evaluation of a More Authentic Assessment for Introductory Programming Assignments [15.295438618760164]
We describe code interviews as a more authentic assessment method for take-home programming assignments.
Code interviews pushed students to discuss their work, motivating more nuanced but sometimes repetitive insights.
We conclude by discussing the different decisions about the design of code interviews with implications for student experience, academic integrity, and teaching workload.
arXiv Detail & Related papers (2024-10-01T19:01:41Z) - Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review.
A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods.
We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
arXiv Detail & Related papers (2024-06-26T05:30:21Z) - Evaluating Contextually Personalized Programming Exercises Created with Generative AI [4.046163999707179]
This article reports on a user study conducted in an elective programming course that included contextually personalized programming exercises created with GPT-4.
The results demonstrate that the quality of exercises generated with GPT-4 was generally high.
This suggests that AI-generated programming problems can be a worthwhile addition to introductory programming courses.
arXiv Detail & Related papers (2024-06-11T12:59:52Z) - AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in
CS Education [8.898863361318817]
We develop, implement, and evaluate a novel learning environment enriched with four distinct chatbots.
These roles cater to the three innate psychological needs of learners - competence, autonomy, and relatedness.
The system embraces an inquiry-based learning paradigm, encouraging students to ask questions, seek solutions, and explore their curiosities.
arXiv Detail & Related papers (2023-08-08T02:13:44Z) - Investigating Fairness Disparities in Peer Review: A Language Model
Enhanced Approach [77.61131357420201]
We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs)
We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date.
We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
arXiv Detail & Related papers (2022-11-07T16:19:42Z) - Theme and Topic: How Qualitative Research and Topic Modeling Can Be
Brought Together [5.862480696321741]
Probabilistic topic modelling is a machine learning approach that is also based around the analysis of text.
We use this analogy as the basis for our Theme and Topic system.
This is an example of a more general approach to the design of interactive machine learning systems.
arXiv Detail & Related papers (2022-10-03T04:21:08Z) - The Impact of Remote Pair Programming in an Upper-Level CS Course [0.0]
Pair programming has been highlighted as an active learning technique with several benefits to students.
This work analyzes the effect of pair programming in an upper-level computer science course.
arXiv Detail & Related papers (2022-04-06T20:01:01Z) - Visualizing the Relationship Between Encoded Linguistic Information and
Task Performance [53.223789395577796]
We study the dynamic relationship between the encoded linguistic information and task performance from the viewpoint of Pareto Optimality.
We conduct experiments on two popular NLP tasks, i.e., machine translation and language modeling, and investigate the relationship between several kinds of linguistic information and task performances.
Our empirical findings suggest that some syntactic information is helpful for NLP tasks whereas encoding more syntactic information does not necessarily lead to better performance.
arXiv Detail & Related papers (2022-03-29T19:03:10Z) - Enforcing Consistency in Weakly Supervised Semantic Parsing [68.2211621631765]
We explore the use of consistency between the output programs for related inputs to reduce the impact of spurious programs.
We find that a more consistent formalism leads to improved model performance even without consistency-based training.
arXiv Detail & Related papers (2021-07-13T03:48:04Z) - BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration [72.88493072196094]
We present a new synthesis approach that leverages learning to guide a bottom-up search over programs.
In particular, we train a model to prioritize compositions of intermediate values during search conditioned on a set of input-output examples.
We show that the combination of learning and bottom-up search is remarkably effective, even with simple supervised learning approaches.
arXiv Detail & Related papers (2020-07-28T17:46:18Z) - Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
We present an in-depth analysis of existing deep learning-based methods for modelling social interactions.
We propose two knowledge-based data-driven methods to effectively capture these social interactions.
We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
arXiv Detail & Related papers (2020-07-07T17:19:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.