The Use of Graphics to Communicate Findings of Longitudinal Data in Design-Based Research
Bibliographic record
Abstract
Visuals and graphics have been used for communicating complex ideas since 1786 when William Playfair first invented the line graph and bar chart. Graphs and charts are useful for interpretation and making sense of data. For instance, John Snow’s scatter plot helped pinpoint the source of a cholera outbreak in London in 1854 and also changed understandings of how germs were spread. While popular in the field of information graphics, rarely are graphs beyond the bar chart found in educational research articles. When present, the graphs do not necessarily enhance the findings of the data. Nor do educational research methods textbooks promote or instruct how to create visual representations to aid with interpretation and communication of findings. This paper attempts to address this void by sharing our processes for creating meaningful visual graphs for communicating multi-dimensional statistical findings more effectively. A working hypothesis was that carefully crafted visual graphics would convey our longitudinal research findings more effectively to broader audiences than existing forms. Three visuals were constructed from survey data three-year longitudinal design based research study of teacher and student learning in a one-to-one laptop school. The study focused on learning designs that changed and improved student learning experiences and outcomes by adopting inquiry approaches to teaching that incorporate meaningful uses of technology. In field tests, our audiences found the visuals were useful for interpreting the findings. More and more frequently, academics are required to communicate their findings to broader audiences. A well-designed and well-constructed graph(ic) can provide a means for effective communication of complex, multi-dimensional statistical data. Such effective communication is beneficial for both an academic audience as well as for broader audiences. The authors presented this paper that was previously published in JITE: Research
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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 | 0.137 | 0.337 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".