Exploring the Use of Faded Worked Examples as a Problem Solving Approach for Underprepared Students
Bibliographic record
Abstract
It is not uncommon for students to find themselves underprepared when entering a post secondary institution. In additional to lower levels of academic achievement, underprepared students may not be aware that they lack the skills needed to be successful and effectively acquire and process information. Because of this, students that enter post-secondary institutions underprepared often require more support in and out of the college classroom. In computational based classes, such as math, engineering, chemistry or physics, this support often includes an introduction to effective problem solving strategies. This study introduced faded worked examples as a problem solving approach to students identified as mathematically underprepared in a college chemistry course. Faded worked examples are similar to worked examples but fade out steps for students to complete, allowing support within the problem solving approach as learning improves. The goal of this study was to explore students’ perceptions of this problem solving approach and their belief in its potential to enhance their learning, particularly with students identified as academically underprepared. Overall, students reported that faded worked examples enhanced their overall learning and problem solving abilities in chemistry and the step by step process allowed for a better understanding of the course material.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".