Case Study on Planning and Designing Social Innovation Projects: Insights into Students’ Learning Experiences, Challenges, and Aspirations through Reflective Practice
Bibliographic record
Abstract
Service learning or social innovation projects in higher education institutions (HEIs) have become more mainstream and are no longer optional endeavors done by students only when they have time to do so. Such initiatives have become a critical part of learning and character development for young people keen to lead change and find meaning in what they do. As HEIs strive to offer more relevant and authentic learning opportunities for their students, service learning or social innovation projects have become common in HEI curricula. Other than the opportunity to work with others on projects that aim to address social issues and challenges, students get to carry out self-retrospection and introspection on what they experienced and learned. This paper presents excerpts from written reflection entries authored by fifteen students who participated in a social innovation project module at an applied learning university in Singapore. Their written reflective practice provided rich insights into their personal development, team interactions and dynamics, challenges faced, perceptions of the social impact of their work, and areas for improvement based on what the students experienced. The paper concludes with suggestions for implementing similar modules or initiatives related to social innovation or social impact in HEI curricula.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".