Understanding the Information Needs of Students Conducting Multidisciplinary Capstone Projects in Engineering Education
Bibliographic record
Abstract
SettingThis article describes my implementation of a study designed to analyze the learning goals and perspectives of engineering students conducting capstone projects.In principle, a capstone project serves as a culminating experience in the final year of engineering programs, where students apply knowledge and skills across disciplines, conducting projects sponsored by industry, government agencies or nongovernmental organizations (NGOs).The results in the study guided the selection of library resources to enhance access to multidisciplinary information and to provide opportunities for collaborations with faculty and students.I initiated this study because I transitioned from being a faculty member at a private technical university for 20 years, supervising engineering capstone projects sponsored by private industry, to the role of engineering librarian at Ohio State University Libraries.With campuses, research facilities, organizations, and partners throughout the state, Ohio State University (OSU) is a public research institution, including its main campus in Columbus and several regional campuses.The academic offerings at OSU include 200+ majors with more than 12,000 courses from 18 colleges/schools, and 200 research centers/institutes.The total student enrollment at OSU increased to 66,901, up 2.3% from 2023.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".