Strategies in Teaching Academic Essay Writing, Level of Effectiveness, and Instructional Barriers: The Case of Filipino Learners
Bibliographic record
Abstract
This study about academic essay writing strategies was conducted in order to propose teachers' lesson guide based on the effective strategies that were ascertained after the investigation. The study used the descriptive-quantitative method of research. The University-approved questionnaire was used to identify the frequency of use of the strategies utilized in students' essay writing activities. There were 126 students and 20 English teachers in Cebu City, Philippines, used as the respondents of the study. It was discovered that the three academic essay writing strategies investigated in the study were always used in both argumentative and informative essay writing, as perceived by the respondents. The first two strategies, traffic light color coding, and planning using informal outline, were found to be very effective in both writing the argumentative and informative essays, while the third strategy, framed paragraph, was also effective to use in both writing the two types of essays. The strategies used, and the students' performance showed a significant relationship. The top barriers in teaching academic essay writing were as follows: teaching essay writing to second language learners, lack of time for explicit instruction, no strategies in place for the part of the students, lack of parental support, and lack of essay structures on the part of the teachers. It was concluded that there were various effective pedagogical strategies that teachers could utilize in teaching academic essay writing. Based on the findings, this study further presents its recommendations.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".