Engaging ESP University Students in Flipped Classrooms for Developing Functional Writing Skills, HOTs, and Eliminating Writer’s Block
Bibliographic record
Abstract
The current study aims to investigate the impact of using flipped classroom approach on improving functional writing skills of business majors. Also, it aims to enhance some Higher Order Thinking (HOTs) skills including analysis, evaluation, and creation. Additionally, the study may help in eliminating writer’s block of the study sample. A standardized functional writing skill Pre and Posttest, Higher Order Thinking (HOTs) skills test and writer’s block questionnaire have been used to assess the target gains of students at the end of the study. The Quasi-experimental research design was used to investigate progress achieved by the sample of the study which included (51) business majors; (26) business students for the experimental group and (25) for the control group. The findings revealed large gains in functional writing skills, HOTs in favor of experimental group compared with the control group with minimized writer’s block based on the T-test differences in scores. Also ANOVA statistics among the quizzes targeted individual skills during the experiment showed on-going progress in both targeted skills and reduced writer’s block. It is recommended that flipped learning approach should be used in language learning practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".