Exploring Biochemical Reactions of Proteins, Carbohydrates, and Lipids through a Milk-Based Demonstration and an Inquiry-Based Worksheet: A COVID-19 Laboratory Experience
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has placed pressure on institutions, and especially instructors, to deliver course content in innovative ways, often with limited resources. In face-to-face learning, many chemistry instructors value inquiry-based learning with real-world applications. These types of learning activities cultivate student interest and, thus, motivate students to achieve their best. However, inquiry-based learning can be more challenging in an online environment, especially given limited resources and preparation time. This paper describes an inquiry-based laboratory demonstration that is inexpensive and easy to conduct outside of the traditional laboratory setting and yet provides a valuable learning experience for second-semester organic chemistry students. In this activity, students apply their knowledge of chemistry to the proteins, carbohydrates, and lipids in bovine milk, studying the casein protein, lactose, fatty acids, and more. Students complete a pre-lab assignment individually, watch prerecorded demonstrations via the Zoom video-conferencing platform, and complete a guided inquiry worksheet in groups, via breakout rooms. These activities help students review concepts such as solubility, hydrogen bonding, and acidity and invite them to apply new material on carbonyl chemistry, hydrolysis, and the relationship between protein structure and function. Because this learning activity challenges students to explore real-world implications, it deepens their understanding of the intricate interplay between organic chemistry, biochemistry, and microbiology and thus prepares them for further scientific inquiry in these areas.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".