Making a Connection between Computational Modeling and Educational Research
Bibliographic record
Abstract
Bruner, Goodnow, and Austin's (1956) research on concept development is re-examined from a connectionist perspective. A neural network was constructed which associates positive and negative instances of a concept with their corresponding attribute values. Two methods were used to help preserve the ecological validity of the input: 1) closely mapping the input to the actual visual stimuli; and 2) structuring the output layer based on Gagne's (1962, 1985) work on human concept learning. This resulted in the addition of output units referred to as attribute context constraints. These units required the network to demonstrate the identification of attributes both relevant and irrelevant to the task of classification. Results suggest that the simultaneous learning of attributes guided the network in constructing a faster and more generalizable representation than when attribute constraints were absent. Results are discussed with respect to the advantages of computational approaches to studying learning.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.019 |
| Scholarly communication | 0.009 | 0.027 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".