A Proposed Theory for the Pedagogic Practices in Alternative Learning System (ALS)
Bibliographic record
Abstract
This study explored the pedagogic practices on modalities in facilitating instruction and assessing learning employed for the PDLs, IPs, and Non-Literate Adults as group of learners in the ALS and consequently propose a theory along these dimensions. Data were obtained from 18 purposively chosen programs-implementers through semi-structured, observations of class activities, and field notes. These were analyzed through Charmaz’ framework on grounded theory data analyses. Results of the findings showed that educational goods and services are delivered through flexible modalities specifically face-to-face aided with modern technologies and home visitation aided with traditional technologies. Assessment in learning, on the other hand, is realized in multi-modal fashion specifically trough performance-based, module-based, and portfolio assessment formats. These findings point towards a theoretical conception that the delivery of educational good and services is unique and dependent on the circumstance, condition, and situation of the target learners and assessing the learners’ learning is actualized in a multi-modal fashion in response and recognition of the learners’ diversity, diverse learning needs and teaching and learning environment. With these theoretical conceptions, the Granular Learning Theory is proposed. The basic tenet of the theory is that instructional delivery and assessment in learning are case-dependent.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".