Visual Literacy Skills of Students in College-Level Biology: Learning Outcomes following Digital or Hand-Drawing Activities
Bibliographic record
Abstract
To test the claim that digital learning tools enhance the acquisition of visual literacy in this generation of biology students, a learning intervention was carried out with 33 students enrolled in an introductory college biology course. This study compared learning outcomes following two types of learning tools: a traditional drawing activity, or a learning activity on a computer. The sample was divided into two random groups. In the first intervention students learned how to draw and label a cell. Group 1 learned the material by computer and Group 2 learned the material by hand drawing. In the second intervention, students learned how to draw the phases of mitosis, and the two groups were inverted. After each learning activity, students were given a quiz, and were also asked to self-evaluate their performance in an attempt to measure their level of metacognition. At the end of the study, participants were asked to fill out a questionnaire that was used to measure the level of task engagement the students felt towards the two types of learning activities. The students who learned the material by drawing had a significantly higher average grade on the associated quiz compared to that of those who learned the material by computer. There were no other significant differences in learning outcomes between the two groups. This study provides evidence that drawing by hand is beneficial for learning biological images compared to learning the same material on a computer. Afin de vérifier le bien-fondé de l’assertion voulant que les outils d’apprentissage numériques renforcent l’acquisition de la littératie visuelle parmi la génération présente d’étudiants en biologie, une intervention d’apprentissage a été effectuée auprès de 33 étudiants inscrits dans un cours collégial d’introduction à la biologie. Cette étude compare les résultats d’apprentissage suite à l’utilisation de deux types d’outils d’apprentissage : une activité de dessin traditionnel ou une activité d’apprentissage par ordinateur. L’échantillon a été divisé en deux groupes formés au hasard. Au cours de la première intervention, les étudiants ont appris à dessiner et à caractériser une cellule. Le groupe 1 a appris la matière par ordinateur et le groupe 2 l’a apprise en faisant les dessins à la main. Au cours de la seconde intervention, les étudiants ont appris à dessiner les phases de la division cellulaire (mitose) et la manière dont les deux groupes ont appris cette matière a été inversée. Après chaque activité d’apprentissage, les étudiants ont subi un test de contrôle et on leur a également demandé d’auto-évaluer leurs résultats pour tenter de mesurer leur niveau de métacognition. À la conclusion de l’étude, on a demandé aux participants de remplir un questionnaire qui a été utilisé pour mesurer le niveau de participation à la tâche perçu par les étudiants par rapport aux deux types d’activités d’apprentissage. Les étudiants qui avaient appris la matière en dessinant ont obtenu une note moyenne considérablement plus élevée à leur test de contrôle que ceux qui avaient appris la matière par ordinateur. Il n’y a eu aucune autre différence significative dans les résultats d’apprentissage entre les deux groupes. Cette étude fournit la preuve que le fait de dessiner à la main est bénéfique pour apprendre des images de biologie par rapport à l’apprentissage par ordinateur de la même matière.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".