Lecturers’ Method in Teaching Speaking at the University of Iqra Buru
Bibliographic record
Abstract
This research aimed to reveal; (1) what method was applied by lecturers in teaching speaking; (2) how was the method applied in the classroom; and (3) how was the students’ perception toward the implementation of the method. This research employed qualitative research. The respondent of the current research were two non-native English lecturers who taught at the University of Iqra Buru in Ambon, Maluku. To collect the data, three kinds of the instrument were used; observation, interview and documents examination. The data were analyzed using Miles & Huberman technique who proposed three concurrent flows of action: a) data reduction; b) data display; and c) conclusion drawing/verification. The researchers found that; 1) The most of the method used in teaching speaking were grammar translation method, task-based, and lexical approach; 2) The implementing method in teaching speaking at the university consisted of material presentation and classroom discussion; 3) The students have bad perception toward the implementation of lecturers’ method and learning activities in teaching speaking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".