Disconnected Connections of Learning Beyond Formal Schooling Through Human–Computer–Human Interactions
Bibliographic record
Abstract
Abstract This article originated from a working group on “Learning beyond formal schooling through human–computer–human interaction (HCHI)” convened at the UNESCO EDUSummIT 2023 in Kyoto (Japan). A polylogue approach was adopted by engaging eight co-authors whose diverse perspectives culminated in propositions that addressed the pivotal question: How should the connections between formal, non-formal, and informal learning be considered in a digitally mediated world? Formal learning is typically structured, organized and chronologically arranged institutional learning, whereas informal learning is associated with everyday learning across contexts throughout one’s life, and non-formal learning is a hybrid of these forms of learning. Considering the growing prominence of informal and non-formal learning in a digitally mediated world, the evolving learning ecosystem calls for a recalibration of the emphasis on formal learning. In this regard, HCHI has the potential to mediate human–human interactions, thereby bridging formal and informal learning. Our articulated position is to preserve the distinct boundaries and inherent complexities of each type of learning while creating opportunities or ‘bridges’ to authentically draw on the processes of each through meaningful actions. The polylogue yielded three propositions to bridge the connections between formal, non-formal and informal learning spaces in a digitally mediated world: (a) formal education institutions can establish strategic alliances and collaborations with learning organizations beyond the formal educational system, (b) the creation of digital learning communities within formal and non-formal spaces exemplify a paradigm of operation in an unregulated, student-centric cohesive space where the educator is a co-participant, (c) the recognition of the value of informal learning experiences by formal education institutions is critical, with emphasis on the learning process rather than the product.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".