Responsive eLearning exercises to enhance student interaction with metabolic pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it