Learner differences and learning outcomes in an introductory biochemistry class: Attitude toward images, visual cognitive skills, and learning approach
Bibliographic record
Abstract
The practice of using images in teaching is widespread, and in science education images are used so extensively that some have argued they are now the "main vehicle of communication" (C. Ferreira, A. Arroio Problems Educ. 21st Century 2009, 16, 48-53). Although this phenomenon is especially notable in the field of biochemistry, we know little about the role and importance of images in communicating concepts to students in the classroom. This study reports the development of a scale to assess students' attitude toward biochemical images, particularly their willingness and ability to use the images to support their learning. In addition, because it is argued that images are central in the communication of biochemical concepts, we investigated three "learner differences" which might impact learning outcomes in this kind of classroom environment: attitude toward images, visual cognitive skills, and learning approach. Overall, the students reported a positive attitude toward the images, the majority agreeing that they liked images and considered them useful. However, the participants also reported that verbal explanations were more important than images in helping them to understand the concepts. In keeping with this we found that there was no relationship between learning outcomes and the students' self-reported attitude toward images or visual cognitive skills. In contrast, learning outcomes were significantly correlated with the students' self-reported approach to learning. These findings suggest that images are not necessarily the main vehicle of communication in a biochemistry classroom and that verbal explanations and encouragement of a deep learning approach are important considerations in improving our pedagogical approach.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".