Exploring Linguistic Modification Techniques Employed in Open and Distance Learning (ODL) Teachers’ Discourse
Bibliographic record
Abstract
This study aims to explore the linguistic modifications techniques employed by ODL teachers. Linguistic modification is a crucial aspect of learning and teaching as it facilitates students’ comprehension by reducing the complexity of syntactic structures and utilizing familiar language patterns. The research data were collected through observations and informal chats with the teachers. A total of 10 observations were conducted during the Second Semester of the 2022/2023 academic year, with informal chats held at the end of each session to obtain additional insights into learning dynamics. The analysis reveals that the teachers’ discourse in ODL context exhibits various linguistic modifications at phonological, syntactic and sematic level. These include a high frequency of self-repetition, exaggerated pronunciation, a slower speech rate, the avoidance of contractions, the use of concise sentences and phrases, paraphrasing, and the avoidance of idioms and unfamiliar words. These phonological, semantic, and syntactic modifications contribute to enhanced input comprehensibility, reducing confusion and fostering increased engagement and participation. This study underscores the significance of linguistic modifications in teachers’ discourse. These modifications are essential for ensuring input comprehensibility and fostering students’ engagement in the learning process. By adapting their language to meet the learners’ needs, teachers can effectively facilitate understanding and create an inclusive learning environment in ODL context.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".