Discourse Competence as an Essential Variable in Developing Grade 11 English First Additional Language Learners’ Writing Skills
Bibliographic record
Abstract
Discourse competence, which entails the interrelatedness of concepts in sentences in spoken and written language, is essential in the development of learners' receptive and productive English skills. Learners with excellent discourse competency skills can better grasp spoken and written texts on a local and global level. The main objective of this paper was to investigate the impact of discourse competence in grade 11 English First Additional Language (EFAL) learners’ writing skills. Halliday and Hassan’s Model of Evaluation framework, which advocates that the primary means of linking texts in discourse is through lexical cohesion, underpinned this study. This paper adopted an interpretivist paradigm. A qualitative approach was employed and a case study design was used to gather data from 40 purposely selected grade 11 learners. Document analysis was used. Findings indicated that (i) restricted knowledge of lexicon, (ii) inadequate knowledge about reiteration and collocation, and (iii) insufficient knowledge about appropriate use of cohesive and coherent devices, were among the established reasons for learners’ writing deficiencies. This paper recommends that essay writing skills can best be achieved through the implementation of the proposed recent language teaching methods such as the Text-based Approach, which uses texts to teach language structures and writing skills. The Department of Education should monitor the development of writing skills from the learners’ earliest years of schooling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".