The Trends in Authentic Learning Studies and the Role of Authentic Learning in Geography Education
Bibliographic record
Abstract
Schools are the basic environments where learning takes place. The quality of these environments and acquirement of the knowledge and skills expected by societies have caused the disconnection between real life and the school. One of the main subjects of education circles in the 21st century has been what to do in order to ensure the connection between real life and education. In this context, the subject of the study is the authentic learning strategy which aims to bring real life subjects and students together. Although this strategy was first introduced in studies conducted in the United States of America in the 1990s, its philosophical roots go back to the 19th century. According to authentic learning, students’ encounter with real life situations or subjects in learning will be effective in raising effective citizens and increasing the quality of learning. The study focuses on how authentic learning is shaped in the literature and what place it takes in the Geography Curriculum of the Ministry of National Education (MoNE, 2018). The main features of authentic learning were determined, and a template was developed by means of the document review method and descriptive content analysis technique. The suitability of this template for the features was examined by reviewing the acquirements with the application principles of the GC 2018 and the explanations in the introduction section. In conclusion, it was determined that all the features required for authentic learning are present in the GC.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".