Aligning the quantum perspective of learning to instructional design: Exploring the seven definitive questions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>TThis paper builds upon a foundational paper (under review) which explores the rudiments of the <em>quantum perspective of learning</em>. 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.<br /><br /></p>
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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 | 0.023 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it