Readers Theater as a Tool to Understand Difficult Concept in Economics
Bibliographic record
Abstract
Readers Theater is one of the innovative learning in an effort to increase the understanding and value students’ learning processes that involve the activity of reading, writing, listening and speaking. In this type of learning, students read a manuscript of a certain literature and other students grasp the meaning of what was read and is shown by the reader. Readers Theaters are different from playing drama. In the drama needed costumes, setting room etc., but in this learning not required. Is the key to effective learning is clearly read the script readers, and listeners can clearly visualize from what is shown. This paper used readers theater to teach a topic of macroeconomics which is unemployment that often considered as a hard topic. We found that students are very happy with this method. Their reading, writing, listening and speaking activities are improving and the most important thing is their understanding of unemployment topic is so much better compared to teaching them with the only direct instruction method. One of the parts that should not be forgotten by teachers is debriefing to check the understanding of the students and many students want this method to be continued especially for another difficult topic so it will be easy to understand.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".