Small Group Discussion Method to Increase Learning Activity: its Implementation in Education
Bibliographic record
Abstract
The aims of this study is: 1) Knowing the use of the Small Group Discussion method in fiqh learning for class X MA tanbihul Ghofilin Bawang students; 2) Knowing the difference between learning activities using the Small Group discussion method and those who do not use the small group discussion method in class X MA Tanbihul Ghofilin students; 3) Knowing the increase in learning activities using the small group discussion method for class X MA Tanbihul Ghofilin Bawang students. The research according to experimental methods using non-equivalent control group design. The subjects in this study were students of class X Agama 3 and X Agama 4 MA Tanbihul Ghofilin which totaled 60 students who were divided into two groups, namely class X Agama 3 as the Experimental class and Class X Religious 4 as the control class. Learning begins with providing pretest questions to find out the extent of student learning activities. Experimental students were given learning using the small group discussion method while the control group used conventional learning methods. The experimental group and the control group were given the final test in the form of posttest questions in writing. Then the results were processed, analyzed, and compared using a t-test and a scor gain test to determine the differences and activities between the two groups to be studied. The results showed that there were differences in learning activities between the experimental class and the control class with evidenced by the t-test and the control class with evidenced by the t-test calculation showing tcount of count 2,0001 > ttable 10.6 with a significance level 0f 5 % and degress of freedom 58. It was proveb by the calculation of N-gain of 0,76 with category.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".