The effect of multiple internal representations on context-rich instruction
Bibliographic record
Abstract
We discuss n-coding, a theoretical model of multiple internal mental representations. The n-coding construct is developed from a review of cognitive and imaging data that demonstrates the independence of information processed along different modalities such as verbal, visual, kinesthetic, logico-mathematic, and social modalities. A study testing the effectiveness of the n-coding construct in classrooms is presented. Four sections differing in the level of n-coding opportunities were compared. Besides a traditional-instruction section used as a control group, each of the remaining three sections were given context-rich problems, which differed by the level of n-coding opportunities designed into their laboratory environment. To measure the effectiveness of the construct, problem-solving skills were assessed as conceptual learning using the force concept inventory. We also developed several new measures that take students’ confidence in concepts into account. Our results show that the n-coding construct is useful in designing context-rich environments and can be used to increase learning gains in problem solving, conceptual knowledge, and concept confidence. Specifically, when using props in designing context-rich problems, we find n-coding to be a useful construct in guiding which additional dimensions need to be attended to.
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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 | 0.003 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".