The Use of Part-of-Speech Tagging on E-Newspaper in Improving Grammar Teaching Pedagogy
Bibliographic record
Abstract
One of the components of learning English is Grammar, and the intrinsic part of it is Parts of Speech (PoS), where the majority of Malaysian students in higher institutions are still grappling to understand its use in sentences. This study aims to compare conventional method to e-learning method on its effectiveness in the teaching and learning of PoS. The application of Stanford PoS tagging has been used to analyze the PoS in every single word of the sentences extracted from the articles in The New Straits Times Online (NST Online). This quantitative research study adopted a comparative analysis in analyzing its findings. The results were statistically analyzed using The Statistical Package for the Social Science (SPSS) for statistical analysis. These findings of the research reveal a significance difference between the score from students using E-paper and the score from students not using E-paper in learning Grammar. Independent t-test was carried out to compare mean between the two groups. The result shows a significance difference (p-value = 0.007, t = -2.774) between the two groups of students’ score. The mean performance of the students using E-paper shows a higher percentage compared to those not using E-paper. As students nowadays spend most of their time with electronic gadgets, this is an innovative way to capture their interest to spend more time on quality reading materials via electronic newspaper, simultaneously learning Grammar by going to the crux of its core by identifying the PoS of each word in sentences using new pedagogical strategy of PoS tagging.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".