The Epistemology of Hegel: An Underlying Approach to Learning
Bibliographic record
Abstract
In this paper we argue that research on learning in education is not a disparate set of unrelated theories and models, but rather there is an underlying unifying understanding of what learning is, based on a conception of epistemology, which can be distinguished from work done in experimental or cognitive psychology. Our argument centers on the idea that how researchers conceptualize epistemology (how one knows) determines to a large extent how they conceptualize learning and therefore teaching. We argue that many of the models and approaches in Learning Sciences are ultimately based on a Hegelian conceptualization of epistemology, whereas in experimental (and cognitive) psychology conceptualizations of epistemology are derived largely from empirical philosophies, especially those of the British Associationists. This basic difference in how the two fields conceptualize 'knowing' leads to essential differences in how we think about learning, ask questions, build models and do research.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".