Responsive eLearning exercises to enhance student interaction with metabolic pathways
Bibliographic record
Abstract
Successful learning of biochemistry requires students to engage with the material. In the past this often involved students writing out pathways by hand, and more recently directing students to online resources such as videos, songs, and animated slide presentations. However, even these latter resources do not really provide students an opportunity to engage with the material in an active fashion. As part of an online introductory metabolism course that was developed at our university, we created a series of twelve online interactive activities using Adobe Captivate 9. These activities targeted glycolysis, gluconeogenesis, the pentose phosphate pathway, glycogen metabolism, the citric acid cycle, and fatty acid oxidation. The interactive exercises consisted of two types. One involved dragging objects such as names of enzymes or allosteric modifiers to their correct drop locations such as a particular point in a metabolic pathway, a specific enzyme, and so forth. A second type involved clicking on objects, locations within a pathway, and so forth, in response to a particular question. In both types of exercises, students received feedback on their decisions in order to enhance learning. The student feedback received on these activities was very positive, and indicated that they found them to increase their confidence in the material and that they had learned the key principles of each pathway. © 2018 by The International Union of Biochemistry and Molecular Biology, 46(3):223-229, 2018.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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".