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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".