AN ANALYSIS OF FREDI’S TEACHING STRATEGY IN SPARE PARTS MOVIE
Bibliographic record
Abstract
In educating a student, a teacher needs a teaching strategy that has a purpose to overcome the difficulties of the student to achieve the learning purpose. The teaching strategy itself has the meaning of the plan of teaching the students to make them comprehend the material in the teaching process. A proper teaching strategy is very essential because it can help the teacher easily in explaining the material to the student and stimulate the student learning interest. This research aimed to find out an appropriate teaching strategy that suitable for the student interest and can generate a good quality student. Therefore, to gain the research purpose, this research will answer the question regarding the teaching strategy implemented by Fredie in Spare Parts movie. \nIn this research, the researcher used descriptive qualitative research. The object of this research was taken from an educational movie titled Spare Parts (2015). This research was using document analysis. The researcher analyzed the teaching strategy used by the teacher in the movie through the dialogues and events of the movie. Then, the researcher becomes the key research instrument in this research and supported by data validity, and procedure inside the data collection of document analysis. \nThe finding exhibits several teaching strategies employed by Fredie in the movie which are comprised of four out of five teaching strategies under the theory of teaching strategy by Saskatchewan Educational as cited in Majid (2013), namely Direct Strategy, Indirect Strategy, Interactive Strategy, and Experiential Learning Strategy. Then, Fredie also used three out of four teaching strategies under the theory of teaching strategy by Brown (2014), namely Cognitive Strategy, Affective Strategy, and Sociocultural-Interactive Strategy.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".