Project-Based Learning Experience That Uses Portable Air Sensors to Characterize Indoor and Outdoor Air Quality
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Understanding how to interpret and manipulate large data sets is increasingly important today; however, this experience has been slow to trickle down to the typical undergraduate student. Here, we describe the implementation of a project-based learning experience that uses portable air sensors for the real-time measurement of carbon dioxide, ozone, and particulate matter, providing students with data sets that include thousands of measurements. These projects allow students to design their own research question and then independently carry out relevant air sampling. Data visualization was used as a tool to identify trends and relationships among analytes and was emphasized as a way to effectively present these findings to an audience. We have implemented the projects over two academic years with diverse student populations, from high school summer research students to senior undergraduate and graduate students in an environmental analytical chemistry course. The extent of mentoring, and the students’ competencies in atmosphere chemistry and spreadsheet and graphing software, were not equivalent between these groups, but all were able to execute successful projects. The projects often focused on the indoor environment as concentrations are not well characterized and tend to vary with human activity, which lend themselves to the development of testable research questions. The paucity of data on indoor concentrations means that, in addition to a valuable experiential learning opportunity, students were engaged in a legitimate citizen science exercise as they set about characterizing a diverse set of indoor environments.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".