Instrument for Assessing Disposition for Contextual Learning of Science of Students in East Africa
Bibliographic record
Abstract
Attempts to make classroom science relevant to the real worlds of students can be enhanced by understanding their ways of learning and knowing. This study, which investigated East African (EA) students’ ways of knowing in science discourses, discusses the development, validation, and application of an instrument to assess EA students’ disposition for contextual learning of science. The Instrument for Assessing Disposition for Contextual Learning of Science (I-ADCLOS) is intended to assist teachers and students to assess their degree of disposition for contextual teaching and learning of science. Exploratory factor analysis of student responses to a 36-item questionnaire developed from theory yielded a validated 31-item I-ADCLOS comprising three dimensions and six subdimensions: Personal Awareness of Influences on Learning Science and Limitations of Traditional Knowledge (PA_ILS_LTK); Attitudes Towards Science Learning in Local Contexts and Nature of Traditional Knowing (AT_SLiLC_NoTK); and Orientation Towards Collateral and Personal Learning Strategies (OTC_PLS). These dimensions were further explored and determined to each bear two subdimensions—PA_ILS_LTK: (a) independence of science learning from cultural influences and (b) metacognitive learning; AT_SLiLC_NoTK (a) instrumentalist–culturalist perspectives and (b) exam-centered, textbook/teacher reliant learning; and OTC_PLS (a) personal awareness of successful learning strategies and other ways of knowing and (b) privileging science and learning by rote.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".