Hackathon as an Effective Learning and Assessment Tool: An Analysis of Student Proficiency against Bloom's Taxonomy
Bibliographic record
Abstract
In recent years, several learning strategies have been adopted to boost students’ learning and performance. Hackathon as a collaborative learning method, gives students the opportunity to investigate the practical usage of concepts by solving a real-world project in a limited time. Many researchers have investigated the effect of hackathons on students’ engagement, team work and learning motivation. In this paper, we integrate a hackathon component in a software development and architecture course curriculum to evaluate the effect of working on a real-world web development project in a hackathon setting on deepening the theoretical concepts learnt in lectures. The data is collected through two surveys which were accessible to students before and after the hackathon and students code commits on GitHub. By comparing the students’ code quality as well as their answers to survey questions before and after the hackathon against the Bloom’s taxonomy, we understand their knowledge state in each step and possible improvements in each one of the areas. The research findings show the importance of hackathon participation on students’ performance and state of knowledge.
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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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".