Difficulties Saudi, Undergraduate, Male Students Encountered in Topic-Sentence Writing and Bloom’s Cognitive Competencies They Relate to: A Case Study of (PMU)
Bibliographic record
Abstract
This case study attempted to explore the difficulties Prince Mohammad Bin Fahd University (PMU) Saudi, undergraduate, male students encountered in writing topic sentences for the traditional, argument essays they wrote for a core writing course (COMM 1311) they took in the fall semester of 2017/2018. The data collection technique used for this study was the document review, which included thirty traditional, argument essays. MAXQDA 2020 was utilized for the data analysis. The analysis resulted in five themes of difficulties: lack of precision, lack of concision, lack of orientation, mechanics difficulties, and grammatical difficulties. The writing difficulties in each of the five themes were found to relate to the following cognitive competencies: understanding, analysis, and evaluation. Only the difficulties in lack of orientation and mechanics themes, however, were found to relate to the remembering cognitive competence, whereas the difficulties in lack of concision, mechanics, and grammar themes were found to relate to application.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".