Teaching Large Classes: What Teachers Say and Do?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Class size is often considered as one of the crucial factors that determines the effectiveness of teaching and learning in the classroom setting. In Nepal, large classes are very common in rural areas or even in urban areas. This study presents the findings of an empirical study on the challenges of teaching in large classes and how teachers are dealing with these challenges in Nepal. The main aim of this article is to explore the challenges of teaching in large classes and to find out the strategies they can be adapted to overcome these problems. The research was conducted by including 10 teachers teaching large classes, following a qualitative research design with a judgmental, non-random sampling procedure. Interviews and classroom observations were taken as the main research tools for the data collection. The research findings are divided into two categories: the challenges of teaching in large classes and how they deal with the large classes. Mainly, teachers found student participation, classroom management, disciplinary issues, and individual feedback as the main problems, and to deal with these problems, they explored various strategies like grouping students, changing seats of students, setting a code of conduct, and using alternative ways of giving feedback.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it