Using Constructivism in Technology-Mediated Learning: Constructing Order out of the Chaos in the Literature
Bibliographic record
Abstract
There are a variety of epistemological positions underlying constructivism \nlearning theory in the literature. The purpose of this paper is to identify \nand categorize the positions of constructivism learning theories, their \nrelationships to each other, and the implications for instructional practice for \neach position. This paper clarifies these positions by differentiating the \nmajor forms of constructivism along two dimensions. The first dimension \ndefines the constructivist position along a continuum between an understanding \nof reality as being objective at one end, and a view of reality that is \ndefined subjectively at the other end. The second dimension defines each \nposition on a continuum where knowledge is either socially constructed at the \none end, or individually constructed at the other end.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.084 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".