Interactivity as a Retention Factor in Learning Biology Through the Protégé Effect
Bibliographic record
Abstract
This study investigated the role of interactivity on the protégé effect, and explored how biology teachers can utilize it in their classrooms to reduce rote learning and facilitate long-term retention. This investigation utilized the generative learning theory, and adopted a non-equivalent quasi- experimental research design involving 60 students. The instruments used for this study include a stimulus instrument titled, Teachers’ Instructional Guide on Ecology of Population (TIGEP), which was used as guide for teaching ecology with the protégé effect, and three response instruments. The first, the Population Ecology Requirement Test (PERT), was used to show the required knowledge for the respondents on the protégé effect, while the second and third, the Population Ecology Achievement Tests (PEATs; version 1 and 2), helped to assess the learners’ performances. Results, obtained using analysis of covariance and Bonferroni post-hoc analysis, indicated that the protégé effect significantly influenced the performances of students on immediate tests (Fcal = F(3,55) = 24.47 > Ftab = 8.57, p < 0.001) and on the long-term retention of Biology concepts (Fcal = F(3,55) = 16.25 > Ftab = 8.57, p < 0.001). This study showed that interactivity, via the protégé effect, provides a strong indication for improving academic performance and retention of learned concepts in biology, as it assists in consolidating and integrating learned concepts.
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.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".