Learning Cycle Model For Transformation Pedagogic Biology Education Students: A Hypothetical Model To Enhance Professionalism Prospective Teachers
Bibliographic record
Abstract
Abstract Learning Content Microbiology at the high school curriculum that emphasizes scientific inquiry in 2013 there are obstacles, which include many microbes (viruses, bacteria, fungi) are pathogenic or infectious for humans, and high school biology lab infrastructure there are limitations associated with safety or biosafety. On the other hand biology teachers still encountered problems in connection with the pedagogical curriculum in 2013 and the technology is developing very rapidly, including ICT, but there are limitations in the implementation by biology teachers. Solution by the government, among others, with the Inservice training for teachers. LS performed phases Plan-Do-See (Saito, 2005 in Ibrahim, 2010), whereas according to Lewis and Hurd (2011): i) Build a lesson study group, ii) Focus the group's inquiry, iii) Study the topic and plan the research lesson, iv) Conduct and discuss the research lesson, v) Reflect and plan the next steps, and vi) Undertaking stusy lesson is important work to build our profession. While models for scientific Inquiry by Alberta (2004) carried out in phases: i) Planning, ii) Retrieving, iii) Processing, iv) Creating, v) Sharing, and vi) Evaluating. Alternative merger LS with inquiry is offered Learning Cycle Model for Transforming Pedagogy, with stages in the cycle: 1. Need assessment 2. Planning 3. Implementation & Monitoring 4. Evaluation At each stage of the cycle is done immersion and integrated ICT.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".