What Are the Learning Approaches Applied by Undergraduate Students in English Process Writing Based on Gender?
Bibliographic record
Abstract
The purpose of this study is to determine gender differences and type of learning approaches among Universiti Utara Malaysia (UUM) undergraduate students in English writing performance. The study involved 241 (32.8% male & 67.2% female) undergraduate students of UUM who were taking the Process Writing course. This study uses a Two-Factor Study Process Questionnaire (R-SPQ-2F) by Biggs, Kember, and Leung (2001). This instrument assesses how students in higher learning institutions approach learning. In addition, data was also obtained from students’ overall performance in the Process Writing course. The overall score for the Process Writing course was 67.58% in which female scores were above the average score while the scores for males were below the average. Overall, the vast majority of UUM undergraduate students apply the surface approach compared to the deep approach. For the surface approach learning, more students chose the surface strategy when compared to the surface motive. For the deep learning strategy, most students chose the deep strategy compared to the deep motive. Very few students used a combination of both approaches. In the context of English language writing, students need to have an intrinsic interest in what is being discussed for their writing activities. An intrinsic interest will help to make learning meaningful as in the deep learning approach. However, the findings provide evidence that most female students who applied the surface approach managed to score well in their overall performance in Process Writing.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".