Video Self-Modeling Technique that Can Be Used in Improving the Abilities of Fluent Reading and Fluent Speaking
Bibliographic record
Abstract
The use of technology in the field of education makes the educational process more efficient and motivating. Technological tools are used for developing the communication skills of students and teachers in the learning process increasing the participation, supporting the peer, the realization of collaborative learning. The use of technology is increasingly widespread in the language teaching as well as in all areas of the education. The use of technology in the classroom language teaching activities allows students to be more active in the learning process than other techniques, learn at their own pace and give them a chance to repeat the activities they want to do. Computers, videos, tablets and other technological products such as mobile phones are increasingly feel the importance in language teaching and learning in recent years. In fact teaching methods and techniques built on the use of technology have been developed. Video self-modeling is one of these methods. Video self-modeling is an application with evidence basis, defined as watching and taking as a model the target behavior exhibited by the person on the videotape. The aim of this study is to draw attention of researchers and practitioners to the video self-modeling method which is determined to be ignored and provides information about the use of methods of language teaching in fluent reading and fluent speaking. These are thought as the contribution of this study to the field of teaching Turkish.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.016 | 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".