Effectiveness of Hackathons in Software Engineering Education
Bibliographic record
Abstract
This Research Full Paper presents quantitative and qualitative data on the effectiveness of hackathons in Software Engineering Education. Hackathons have become a growing part of Software Engineering (SE) education in the past decade. Although the academic environment develops technical foundations, a hackathon can develop key competencies to hone lifelong learning. Improving interpersonal, entrepreneurial, and technical skills better prepare SE students for a career after graduation and reinforces engineering-relevant skills such as problem-solving, teamwork, and management. This study examines students’ abilities to transfer relevant course skills into the hackathon environment, specifically those related to SE best practices such as software design, SOLID principles (Single-responsibility, Open-Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion Principle), and Object-Oriented Programming (OOP). Furthermore, through faculty-led hackathon prep sessions and workshops, students are trained to follow an adapted design-thinking process known as the Hackathon Design Thinking Process (HDTP). This study directly addresses the following sub-questions: to what extent do participants feel that hackathons have a positive impact on their SE education, what are the participants’ perceptions of SOLID principles and OOP skill development during the hackathon, and how effectively are participants able to apple SOLID principles and OOP practices during the hackathon? The data is aggregated from a second-year SE undergraduate and a first-year SE masters cohort from participant perception surveys, judges, and project submissions from two hackathons. We utilize Natural Language Processing (NLP) with Google’s Sentiment Analysis, conduct Kendall’s Tau-b Test using IBM’s SPSS Statistics Tool, and compare Class Diagrams with student-submitted code. Results confirm that participants believe hackathons positively impact their education and tend to sacrifice planning time to implement solutions, often disobeying SE design principles and best practices due to the fast-paced nature of hackathons.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".