A modular activity to support knowledge retention, application, and metacognition in undergraduate immunology
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
Learning in undergraduate immunology requires students to be able to retain knowledge, to apply information to new contexts, and to self-assess their understanding of concepts. These core competencies strengthen students' critical thinking and metacognitive skills which, in turn, enhances their ability to identify knowledge gaps and strategies to support future learning. Retrieval practice and feedback-driven metacognition are evidence-based teaching strategies that have been demonstrated to enhance knowledge retention and metacognition in a range of academic disciplines and levels of education, although their implementation and impact on learning in undergraduate immunology remain largely unexplored. To this end, I designed a modular "practice session" activity for a 12-week, upper-level, undergraduate immunology course that incorporates periodic retrieval practice and feedback-driven metacognition to support students' knowledge retention, application of information, and metacognitive skills. Near the end of the course, a survey was conducted to assess student perceptions on whether the activity supported learning and metacognition in immunology. Instructional resources are provided to facilitate easy adaptation of this modular activity to courses of diverse science disciplines and levels of study in higher education.
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 | 0.003 | 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