Beyond Summer Reading: Enabling Covert Student Learning Through a Cross-Campus Connecting Theme
Bibliographic record
Abstract
In the spring of 2022, a formerly vibrant summer reading program had become moribund due to retirements and staff changes; roughly 1600 first year students were about to miss out on a longstanding and successful tradition. In response, a small group of faculty sought a way to link first-year courses between disciplines in order to boost student engagement. This group latched upon a fortuitous circumstance: the 101st anniversary of Karel Capek's play R. U.R., which gave the world the term “robot.” Thus was born Robot 101, an integrative experience connecting instructional content between courses and with a wide range of academic and artistic events outside the classroom, all centered around the multifaceted theme of “robot.” This paper details how a common theme can inform and enliven first-semester courses in engineering, composition, and computer science, as well as engage the greater university community. Our primary objective in establishing these overt connections between courses and other activities was to give students the opportunity to form their own covert connections, thereby deepening their appreciation of the interdisciplinary nature of big ideas in engineering, technology, and society. The program also provided opportunities for more advanced students to participate and make their own interdisciplinary connections. This work provides initial data and reflections on the effect of this program on student engagement and connectedness in first semester courses. We include recommendations for a scalable and sustainable program that continues thematic integration on an ongoing basis.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".