Towards Emotionally Intelligent Software Engineers: Understanding Students' Self-Perceptions After a Cooperative Learning Experience

• URL: http://arxiv.org/abs/2502.05108v1
• Date: Fri, 07 Feb 2025 17:29:08 GMT
• Title: Towards Emotionally Intelligent Software Engineers: Understanding Students' Self-Perceptions After a Cooperative Learning Experience
• Authors: Allysson Allex Araújo, Marcos Kalinowski, Matheus Paixao, Daniel Graziotin,
• Abstract summary: Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project.
• Score: 3.001523463519739
• License:
• Abstract: [Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

Related papers

• Gender Influence on Student Teams' Online Communication in Software Engineering Education [8.65285948382426]
  This study examines an eight-week project involving 39 SE students across eight teams contributing to GitHub projects. Using a mixed-methods approach, we analysed Slack communications to identify gender differences. We found higher help-seeking and leadership behaviours in the all-woman team, while men responded more slowly.
  arXiv  Detail & Related papers  (2025-02-20T15:43:54Z)
• Embracing Experiential Learning: Hackathons as an Educational Strategy for Shaping Soft Skills in Software Engineering [3.02243271391691]
  We report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.
  arXiv  Detail & Related papers  (2025-02-11T20:58:33Z)
• Code Collaborate: Dissecting Team Dynamics in First-Semester Programming Students [3.0294711465150006]
  The study highlights the collaboration trends that emerge as first-semester students develop a 2D game project. Results indicate that students often slightly overestimate their contributions, with more engaged individuals more likely to acknowledge mistakes. Team performance shows no significant variation based on nationality or gender composition, though teams that disbanded frequently consisted of lone wolves.
  arXiv  Detail & Related papers  (2024-10-28T11:42:05Z)
• Mutual Theory of Mind in Human-AI Collaboration: An Empirical Study with LLM-driven AI Agents in a Real-time Shared Workspace Task [56.92961847155029]
  Theory of Mind (ToM) significantly impacts human collaboration and communication as a crucial capability to understand others. Mutual Theory of Mind (MToM) arises when AI agents with ToM capability collaborate with humans. We find that the agent's ToM capability does not significantly impact team performance but enhances human understanding of the agent.
  arXiv  Detail & Related papers  (2024-09-13T13:19:48Z)
• EmoBench: Evaluating the Emotional Intelligence of Large Language Models [73.60839120040887]
  EmoBench is a benchmark that draws upon established psychological theories and proposes a comprehensive definition for machine Emotional Intelligence (EI) EmoBench includes a set of 400 hand-crafted questions in English and Chinese, which are meticulously designed to require thorough reasoning and understanding. Our findings reveal a considerable gap between the EI of existing Large Language Models and the average human, highlighting a promising direction for future research.
  arXiv  Detail & Related papers  (2024-02-19T11:48:09Z)
• Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought [50.13429055093534]
  Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks. We propose the Emotional Chain-of-Thought (ECoT) to enhance the performance of LLMs on various emotional generation tasks.
  arXiv  Detail & Related papers  (2024-01-12T16:42:10Z)
• Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
  We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs) By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
  arXiv  Detail & Related papers  (2023-08-17T10:49:18Z)
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli [53.53886609012119]
  We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli. Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
  arXiv  Detail & Related papers  (2023-07-14T00:57:12Z)
• Understanding Self-Efficacy in the Context of Software Engineering: A Qualitative Study in the Industry [2.268415020650315]
  Self-efficacy is a concept researched in various areas of knowledge that impacts various factors such as performance, satisfaction, and motivation. This study aims to understand the impact on the software development context with a focus on understanding the behavioral signs of self-efficacy.
  arXiv  Detail & Related papers  (2023-05-26T17:16:37Z)
• Who does what? Work division and allocation strategies of computer science student teams [5.863264019032882]
  The aim of this research is to gain insight into the work division and allocation strategies applied by computer science students during group assignments. Motivated primarily by grade and efficiency factors, students choose and allocate tasks based on their prior expertise and preferences. Based on our findings, we argue that the setup of group assignments can limit student motivation for practicing new software engineering skills.
  arXiv  Detail & Related papers  (2021-03-04T12:27:07Z)
• Knowledge Bridging for Empathetic Dialogue Generation [52.39868458154947]
  Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. We propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation.
  arXiv  Detail & Related papers  (2020-09-21T09:21:52Z)

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.