Why Instructional Explanations Often Do Not Work: A Framework for Understanding the Effectiveness of Instructional Explanations
Bibliographic record
Abstract
Although explanations are a common means of instruction, research shows that they often do not contribute to learning. To unravel the factors giving rise to the ineffectiveness of instructional explanations, we propose a framework that brings together empirical work on instructional explanations from a variety of research fields, including classroom instruction, tutoring, cooperative learning, cognitive skill acquisition, learning from texts, computer-supported learning, and multimedia learning. In our framework, we identify the distinctive characteristics of instructional explanations, present general guidelines for designing instructional explanations, and describe factors influencing both the generation and use of instructional explanations. It is argued that future research should uncover in more detail the interrelations between the different aspects of providing and using instructional explanations and their specific effects on 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 | 0.029 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".