The effect of multiple internal representations on context-rich instruction
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it