Introducing Second Year Analytical Chemistry Students to Research through Experimental Design in the Undergraduate Teaching Laboratory
Bibliographic record
Abstract
Advancements in technology have led to significant changes in the workforce and created a demand for expertise in science, technology, engineering, and mathematics (STEM). In response, curricula have evolved to emphasize essential skills such as critical thinking and communication to prepare students for STEM-based careers. To this end, postsecondary institutions continue to develop and incorporate project-based learning (PBL), course-based undergraduate research experiences (CUREs), and process-oriented guided-inquiry learning (POGIL). These opportunities can be offered in junior level (1st and 2nd year) courses by designing projects that match students’ skills and knowledge. Opportunities at the junior level reach a larger student population and can increase interest in STEM-based careers. In this article, we introduce a project-based activity for the second-year analytical chemistry laboratory in which students design and conduct experiments to quantify analytes in real-life samples. Analytes selected for this project (acids or bases, ascorbic acid, beta-carotene, calcium, oxalate, reducing sugars and starch) can be quantified using techniques familiar to second year students including titration and absorbance spectroscopy. Students first designed experimental procedures and received feedback before conducting the experiments. Each experiment was performed over two laboratory periods, which allowed students to modify procedures between iterations to improve their experimental design. This experience allowed students to develop 21st century competencies including critical thinking and problem solving, innovation, creativity and entrepreneurship, self-directed learning, collaboration, and communication.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".