Developing a Framework for Understanding Lecturer-Student Interaction in English-Medium Undergraduate Lectures in Sri Lanka: First Step Towards Dialogic Teaching
Bibliographic record
Abstract
This study focuses on developing a framework to identify dialogic interaction in English-medium science lectures in a small faculty of a Sri Lankan university. In Sri Lanka, English-medium instruction was introduced with an objective of developing language proficiency of students along with the content delivery. It is asserted that teacher-student interaction in ESL content classes would help develop language proficiency of students. However, generally, lectures in English-medium undergraduate courses in Sri Lanka tend to be monologic, leaving the language development a question. The lecture delivery style, along with other reasons, affects students’ language development in English-medium classes. Although increased dialogic interaction could help change this situation, few studies have examined the occurrence of dialogic interaction in tertiary-level ESL science classes. The main objective of this study is to develop a framework by analysing the lectures given at the faculty in a method that contextually suits the lecture delivery style in the Asian countries. Data were collected from transcribed recordings of 12 hours of lectures, involving four lecturers. The interactional episodes in the lectures were the basis of developing the analytical framework, which refines and extends the MICASE corpus interactivity rating in a contextually-focused way, was especially designed to categorise the lecture discourse along a monologic-interactive/dialogic continuum. This paper also suggests how this framework could be adopted to analyse the lecture deliveries from a practitioner’s point of view. Within the scope of this paper it is explained how this framework was designed focusing attention to interactional episodes. It can be envisaged that the proposed framework can make a concrete contribution to teaching and learning in higher education, mainly to the concept of developing language through dialogic lecture delivery at tertiary level ESL content classes.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".