Secondary School Students’ Understanding of Learning Contexts in Science Education: Perspectives from Ethiopia
Bibliographic record
Abstract
There is increasing recognition of the importance of learning contexts in science education. However, learning contexts are rarely articulated in terms of what they constitute and are often framed by educators and textbooks with limited inputs from students. Unless properly articulated, contexts become anything and everything, thereby losing their relevance for practical aspects of science teaching and learning. In this study, we examined secondary school students’ perceptions of learning contexts as they relate to their science learning and determined aspects of contextualising science education. We recruited 418 secondary school students (Grades 10 and 11) from eight schools in Bahir Dar, Ethiopia, to respond to a survey that has both closed and open-ended items. We used exploratory factor analysis to analyse the quantitative element of the survey and determine aspects of contextualising science teaching and learning. We also used open-coding and constant comparison to analyse students’ qualitative descriptions of learning contexts with the goal of determining students’ understanding of the concept as it relates to their science learning. Results from the factor analysis revealed four factors—agency and discourse, inquiry and processes, everyday life and community engagement and customising as aspects of contextualisation from students’ perspective. The results from the qualitative element reinforced those quantitative findings. Overall, results underscore the importance of contextualising as well as questioning the purpose of science education, especially, but not limited to, in low- and middle-income countries. The study has implications for learning design and fostering learner agency in science education.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".