Understanding The Nature of Out-of-School-In-School Technological Divide in Uganda
Bibliographic record
Abstract
Abstract Currently, there seem to be a big gap between students’ experiences with Information Communication Technology (ICT) In-school and Out-of- school in Uganda. Ironically there is more exposure and use of digital technologies in Out-of-school context than is In-school settings in Uganda and in majority of sub-Saharan African countries. The question that arises is, do teachers use these out-of school acquired experiences when designing learning opportunities to harness this great knowledge capital? And, What flexibilities are embedded in the current curriculum in Uganda to allow teachers to explore these rich technological proficiency that students bring to their classrooms to create more meaningful and relevant learning experiences? This poster presentation will explore the existing ICT resources available in the Out-of-school environment and further, examine the possible challenges teachers might encounter and possible strategies available to provide rich vibrant technologically enriched learning environments. Invariably, students whose daily interaction with ICTs in out-of school context find In-school learning more meaningful and relevant. Teachers in such learning environment require strong technological Content Knowledge (TPACK) yet not all teachers in Uganda might have this rich background in technology knowledge. Keywords: ICT, In-School, Out-of-School, Technological Pedagogical Content Knowledge (TPACK)
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".