Aligning the quantum perspective of learning to instructional design: Exploring the seven definitive questions
Bibliographic record
Abstract
TThis paper builds upon a foundational paper (under review) which explores the rudiments of the quantum perspective of learning. The quantum perspective of learning uses the principles of exchange theory or borrowed theory from the field of quantum holism pioneered by quantum physicist David Bohm (1971, 1973) to understand learning in a new way. Bohm proposes that everything exists as wholes, rather than as parts, and that everything is connected. Similarly, the quantum perspective of learning proposes that individuals learn in holistic ways as they interact with temporal and in infinitely extending virtual worlds. Further, according to the quantum perspective of learning, learners have infinite potential. In this paper, the quantum perspective of learning is examined utilizing a combination of Schunk’s (1991) and Ertmer and Newby’s (1993) definitive questions for aligning learning theory with instructional design. These seven definitive questions focus on how learning happens, influential factors in learning, the role of memory, transfer of knowledge, modalities of learning that can best explain the quantum perspective of learning, applicable assumptions, and a discussion of how instruction can be organized to optimize learning. Examples of strategies that facilitate the quantum perspective of learning are provided.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.029 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| 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".