Exploring Teachers Practices of Classroom Assessment in Secondary Science Classes in Bangladesh
Bibliographic record
Abstract
The study investigates teachers’ classroom assessment practices of secondary schools in Bangladesh. The study is mainly quantitative with some integration of qualitative approach. Secondary science teachers and their science classrooms were main data source of the study, which were selected randomly. Data sources were secondary science teachers and their science classrooms. The study used a lesson observation protocol to understand their classroom assessment practice, and pre-lesson and post-lesson observation interview protocols as main sources of data collection. Qualitative data from interview were used to triangulate the quantitative data from observation. A total of thirty teachers (twenty male and ten female) were chosen randomly from six secondary schools in Dhaka. The study explored that teachers’ current practice of classroom assessment was to only assess students learning achievement and they followed traditional methods to assess students. The dominated assessment activity was oral questioning and very few students take part in the assessment activities by answering the questions. The classroom questions are basically focused very specific responses and encouraged rote learning; even students’ didn’t get enough time for thinking and answering the questions. Therefore the study suggests changing current practices by using different assessment strategies like self and peer assessment and focus on assessment for learning to ensure effective teaching-learning and quality education. These findings can inform the classroom teachers as well as o relevant stakeholders in making necessary changes in the present classroom assessment practices in Bangladesh.
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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 | 0.003 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".