The Effect of Collaborative Literacy Intervention Project (CLIP) Training as a Professional Development Tool on Enhancing High School Teacher’s Efficiency in Teaching English
Bibliographic record
Abstract
Over the past several years, research on school improvement and professional development has identified a consistent message in regard to the most effective ways to improve student achievement. Collaborative Literacy Intervention Program (CLIP) can be regarded as a more effective way to reach students who were having difficulty in reading. By examining average classroom test scores, this study provides some quantitative evidence regarding the use of CLIP as a professional development tool. The quantitative analysis includes data from two classroom teachers in Tehran, Iran. First, teachers self-selected to be involved in the CLIP training and each teacher had an equal opportunity to participate. Then, four classes were selected as the participants. In general, 65 students in all four classes taught by the two teachers selected for the purpose of this study. Based on PET proficiency test, those placed between one standard deviation above and below the mean were selected. Finally, 38 students were selected as the main participants, 20 for the experimental group taught based on CLIP and 18 for the control group. The TOEFL proficiency test was used as the pretest and posttest for all selected participants. Then, average classroom test scores of students in the class taught based on CLIP training were compared with the class taught without using CLIP training. The results of data analysis indicate that there was a statistically significant difference between the students’ performance in experimental and control group in the results of pretest and posttest. In other words, CLIP training played a significant role in developing the students’ performance in reading, grammar and vocabulary. In conclusion, this study showed that training teachers through CLIP is a suitable pedagogical approach to promote language skills.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".