Developing Model Assesement for Learning (AFL) to Improve Quality and Evaluation in Pragmatic Course in IAIN Surakarta
Bibliographic record
Abstract
The research objective is to develop a model of Assessment for Learning (AFL) in Pragmatic course in IAIN Surakarta. The research problems are as follows: How did the lecturer develop a model of AFL? What was the form of assessment information used as the model of AFL? How was the results of the implementation of the model of assessment. The method used in this study is Research, Development and Diffusion. There were three steps activities in this model. The first step, the researcher done the activities included doing the basic scientific inquiry, investigation issues of education, data collection and designing the operational research planning. The second step, the researcher was composing AFL modeling, data validation from the experts and practitioners, compossing readability test; included trial operation models to find solutions to the problems, planning an educational programs, testing, and evaluating the programs. The third step was diffusion, the reseacher informing the target system, demonstrations programs, training to use the target system and program solutions, servicing and maintaining. The population of this study were 150 students from fives classes. From the data analysis shown than the application of AFL model for Pragmatic course could be improved in understanding the materials and English performing. The average score of Pragmatic course was 3.18 from 5 parallel classes, while the average scores of Vocabulary course is 2.40 from 5 parallel classes. The data analyzis shown that AFL method was more suitable to teach English Pragmatic course than English Vocabulary course.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".