Student perspectives on learning through developing software for the real world
Bibliographic record
Abstract
From a student's perspective, the standard computer science curriculum can effectively develop fundamental software design principles and techniques, but may struggle to fully prepare students for professional practice. Real-world projects require many skills that are challenging to foster in the classroom, including the ability to implement large applications, interact professionally with others, and independently learn new concepts. Undergraduate programs have attempted to develop these abilities through capstone classes and by encouraging participation in co-ops and internships. At Ohio State University, nearly a dozen students have attempted to foster these abilities by doing long-term, real-world, large-scale, commercial-grade software development projects. The first such project recently released an iPhone-based, stadium-centric infotainment application to end-users in time for the 2010 football season. This paper, whose first author is an undergraduate computer science student, captures, from a student's perspective, the educational benefits of ongoing and real-world projects over the more traditional approaches. Following an examination of the educational impacts of these projects relative to the impacts of co-ops, internships and capstone classes, results suggest that long-term, real world projects are a valuable and synergistic component of an undergraduate education in computer science.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".