Mar 2014· PeerJ· Vol 2, pp. e289· 216 citations· ⚡ 13 influential· 102 references
MedicineComputer Science
TL;DR
A study with 42 participants investigates the relationship between the affective states, creativity, and analytical problem-solving skills of software developers and offers support for the claim that happy developers are indeed better problem solvers in terms of their analytical abilities.
Abstract
For more than thirty years, it has been claimed that a way to improve software developers’ productivity and software quality is to focus on people and to provide incentives to make developers satisfied and happy. This claim has rarely been verified in software engineering research, which faces an additional challenge in comparison to more traditional engineering fields: software development is an intellectual activity and is dominated by often-neglected human factors (called human aspects in software engineering research). Among the many skills required for software development, developers must possess high analytical problem-solving skills and creativity for the software construction process. According to psychology research, affective states—emotions and moods—deeply influence the cognitive processing abilities and performance of workers, including creativity and analytical problem solving. Nonetheless, little research has investigated the correlation between the affective states, creativity, and analytical problem-solving performance of programmers. This article echoes the call to employ psychological measurements in software engineering research. We report a study with 42 participants to investigate the relationship between the affective states, creativity, and analytical problem-solving skills of software developers. The results offer support for the claim that happy developers are indeed better problem solvers in terms of their analytical abilities. The following contributions are made by this study: (1) providing a better understanding of the impact of affective states on the creativity and analytical problem-solving capacities of developers, (2) introducing and validating psychological measurements, theories, and concepts of affective states, creativity, and analytical-problem-solving skills in empirical software engineering, and (3) raising the need for studying the human factors of software engineering by employing a multidisciplinary viewpoint.
The growing use of generative artificial intelligence (GAI) in engineering education has created a need for measures that capture students’ creativity with these tools in engineering contexts. This research developed and validated the GAI-Assisted Engineering Creativity Scale (GECS) across six studies involving university engineering students. Study 1 generated and refined the initial item pool through theory-informed content validation. Study 2 identified a four-dimensional structure comprising Intentionality, Authenticity, Human–AI Collaboration, and Critical Integration. Study 3 confirmed this structure, compared alternative measurement models, and examined measurement invariance across gender. Study 4 evaluated short-term test–retest reliability and informed the final item refinement. Study 5 examined convergent and discriminant validity in relation to established measures of AI-assisted creativity and general creative attributes and behaviors. Study 6 evaluated associations with creative thinking, critical thinking, creative self-concept, and general creativity scale. The final 16-item GECS demonstrated satisfactory internal consistency, a replicable four-factor structure, moderate short-term temporal stability, and meaningful relationships with theoretically relevant constructs. The findings support the GECS as a multidimensional measure of students’ creativity assisted by GAI in engineering work.
Collaborative software development has become an essential component of computing education, where students are expected to work effectively in teams while completing complex academic projects. This study assessed the level of teamwork competencies among computing students engaged in collaborative software engineering activities. A survey dataset consisting of 631 respondents and 39 variables was analyzed to evaluate five key teamwork dimensions: task responsibility, communication, coordination, quality assurance, and technical collaboration. Descriptive statistics and reliability analysis were employed to determine the overall competency level and internal consistency of the measurement instrument. Results revealed that students demonstrated a high level of teamwork competency (M = 4.00, SD = 0.56) across all dimensions. Among the measured factors, quality assurance (M = 4.12) and communication (M = 4.10) obtained the highest ratings, while technical collaboration (M = 3.86) received the lowest mean score. The instrument showed excellent reliability with an overall Cronbach’s alpha of 0.938, confirming its suitability for evaluating teamwork behaviors in collaborative academic environments. Findings provide baseline insights for future studies exploring predictive models of teamwork performance using machine learning and learning analytics approaches.
J. Bungay, Joevelyn W. Fajardo, Arjo Ladia· IEEE International Conferenc...· 0 citations
This study examines the effectiveness of a project-based learning approach designed to foster engineering students’ creativity, innovation, and design skills. The pedagogical approach involves teams of Mid-studies (third-year) civil engineering students who must design, simulate, and validate a technical solution to an open-ended problem. The first semester centres on problem definition, exploration of conceptual solutions, numerical modelling and planning for prototype development. The second semester shifts to prototype fabrication and experimental validation in the laboratory. The study employs a mixed-method research design combining quantitative and qualitative evidence. Quantitative data are gathered through structured evaluation tools aligned with targeted competency indicators, while qualitative insights are obtained through student reflections and open-ended questionnaire responses. Several data sources were used to assess student performance and competency development: rubrics, design performance metrics, design logs, prototypes, simulation reports and post-project questionnaire. The findings suggest that the project-based learning approach significantly enhances students’ creativity, innovation capacity, and design optimization skills.
G. Poitras, E. Poitras, S. Desjardins· Proceedings of the Canadian...· 0 citations
Software engineering depends on the ability of developers to understand code, yet predicting how they do so remains an open challenge despite decades of research. Existing approaches rely either on simplified proxy measures that limit accuracy or on non-trivial measurements requiring elaborate experimental setups that are difficult to scale and apply in practice. In contrast, recent work in psychology suggests an alternative perspective: Instead of modeling task-specific phenomena directly, human behavior can be captured through cognitive regularities learned from large-scale behavioral data. This idea treats complex human behavior as the observable outcome of underlying cognitive processes that manifest consistently across tasks and domains. In this paper, we explore this perspective in the context of program comprehension. We evaluate Centaur, a foundation model trained on 160 general psychological experiments, on 9 previously published program-comprehension studies. We assess how well its predicted response distributions align with human response data and compare Centaur's performance to its base model, Llama 3.1. To better understand the source of its performance, we conduct ablation studies to isolate the contribution of different sources of information, such as the code artifacts, task-related context, and prior trials and participant responses. In a nutshell, we find that Centaur more closely aligns with human response patterns than its base model, is significantly less reliant on information from prior trials and responses, and benefits more from task-related information. These findings suggest that behavioral patterns learned from general psychological data can transfer to complex software engineering tasks such as program comprehension. More broadly, they point toward foundation models of human cognition as a basis for modeling developer behavior in software engineering.
This article clarifies the concept definitions and evaluation criteria of understanding in cognitive psychology by combining classic theories and experimental evidence, and uses these criteria as the analytical framework for the performance of “similar understanding” in contemporary artificial intelligence systems.
Ruo Qin· Journal of Language, Culture...· 0 citations
Generative artificial intelligence has become part of the daily academic works for the university students, whether it develops research competence or simply improves the quality and speed of immediate problem solving and response. This integrative literature review examines that question through five related dimensions of undergraduate research competence: information literacy, critical thinking and problem-solving, academic writing, self-regulated learning and research independence, and research ethics. The review synthesizes recent systematic reviews, meta-analyses, empirical studies, and established educational frameworks. However, GenAI looks most useful when it functions as an assistive tool: students use it to possibility generation, feedback receiving, alternatives comparing, or revise their work while retaining responsibility for source evaluation, reasoning, and final decisions. By contrast, substitutive use where the system performs substantial portions of the cognitive or research process creates problems of cognitive offloading, weak source studying, overconfidence, wrong references, and reduced human research independence. The review therefore argues that the central educational issue is not whether the generative artificial intelligence is beneficial or harmful in itself, but under what conditions AI-assisted performance becomes genuine, transferable research competence. Three recurring moderators are especially important: instructional directions, assessment design, and the limit of responsibility respected by the student. The review concludes with a conceptual framework distinguishing assistive from substitutive usage and proposes a research agenda centered on new designs, objective skill assessment, discipline-sensitive studies, and assessments that require students to demonstrate their own reasoning.
Mohamad Rami Al Jundi, Bashar Abdulkareem Alali, Rama Alothman et al.· Research Journal of Maaref U...· 0 citations
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