A Quick Fix to Encourage Connecting Classroom Learning with the “Real World”
Bibliographic record
Abstract
Connecting course learning to other experiences in one’s life helps learning take root, yet, without explicit encouragement to do so, we don’t always pause to consider such connections. To help our students make the connections between their course and the world around them, we introduced a small assignment called Physics in My Life, which involves sharing a photo and analyzing some aspect of it based on course learning. We were pleased with the results of this small addition, seeing many submissions related to our students’ favorite sports and others where the author thought more deeply about a mundane experience. This little project was meant to be a quick fix to help our students connect course material to the world around them, but we also found it to be a quick fix in helping us connect with our students as individuals in a large class.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.103 | 0.083 |
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".