Studies on visualisation in science classrooms: a systematic literature review
Bibliographic record
Abstract
Visualisation has been a critical means of scientific reasoning, knowledge development, and communication. In science classrooms, visualisation plays significant roles for teaching and learning. To better understand the landscape of the existing research on visualisation for supporting student science learning in K to 12 classroom contexts, we undertook a systematic review to investigate what research areas and contexts of visualisation have been examined by science educators and researchers. In particular, this review explored the domains of science subject areas, authors, types and purposes of visualisation in science education research published in peer-reviewed journals between 2011 and 2021 and indexed in the ERIC and Academic Search Complete databases. Our systematic search and subsequent screening process yielded 33 studies for analysis. The findings suggest (a) research on visualisation for student science learning spans across the main science domains (i.e. life science, physical science, chemistry, earth and space science), and grade levels (from Grades 1−12); (b) compared with students’ visualisation in social interactive contexts, more studies focused on individual students’ visualisation; and (c) the majority of the research focused on students’ concept learning through visualisation, rather than visualisation for scientific reasoning and communication. Based on these findings, suggestions for further research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".