Project-Based Learning for Graduate Students in Digital Humanities
Bibliographic record
Abstract
This essay reports on a five-year summer internship sponsored by the Graduate School of Arts and Sciences at New York University that sought to apply computer science pedagogy in project-based learning [PBL] to the digital humanities training of graduate students from diverse humanities disciplines and programs. While much student training in the field tends to occur in academic courses or workshops devoted to particular tools and methods, this program used the iterative process of project development to design an inclusive, efficient context for graduate students with limited experience with technology to learn digital humanities skills appropriate to their professional and scholarly objectives. Describing the framework of PBL computer science pedagogy, the essay considers the technology learning objectives of a broad variety of projects undertaken by 50 MA and PhD students from disciplines ranging from English and History to Fine Arts and Linguistics. Emphasizing the role of peer learning and cultural differences between STEM and humanities learning contexts, the essay draws on the program coordinators' teaching experience and student commentary to assess the learning outcomes of a PBL approach for the professional and scholarly development of humanities graduate students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".