The Effectiveness of Andragogically Oriented Teaching Method to Improve the Male Students’ Achievement of Teaching Practice
Bibliographic record
Abstract
Students at universities are still frequently found to have low independency in learning. Besides, lecturers also still have tendency to treat students as if they were young learners, or in other words, the lecturers still use pedagogically oriented teaching methods (POTM); although they claimed themselves to have applied methods of teaching orienting to the characteristics of adult learners or andragogically oriented teaching methods (AOTM). This also happens to the students at English Education Department of Muria Kudus University (EED MKU), and affects their achievement of language leaning, in particular theirs of teaching practice. Some schools in which the students conduct teaching internship consider that the students’ achievement of teaching is still less satisfactory, and they tend to be less creative and less independent in preparing the teaching, particularly related to the use of instructional skills of teachnig, such the use of classroom language and the use of things neeeded in teaching as media of teaching. This is a quantitative study involving 87 students joining Speaking for Instructional Purposes (SIP) classes at EED MKU. The instruments used are pre and post tests of teaching practice in SIP classes.This study aims to compare the effectiveness of implementing AOTM and POTM to improve the female and the male students’ achievement of teaching practice. The results reveal that AOTM is more effectively implemented to improve the male students than the female ones.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".