Teachers' Perceptions on Traditional and Non-Traditional Data Visualization for Pedagogical Decision-Making
Bibliographic record
Abstract
From 2012 until 2016, the number of US students enrolled in an online course increased 14.68%, resulting in more work for online teachers, who are responsible for planning and making pedagogical decisions to guide students. Interactions in such courses can generate data (quantity and variety), where relevant information in the educational context can be extracted, assisting teachers managing their classes. However, to present these data in spreadsheets, tables and graphics, is not enough. In this context, some authors suggest using data visualization to communicate information clearly and efficiently from the point of view of users, helping them analyze and reason about the data. However, people react differently to different types of visualization, which we categorized into two broad groups: traditional or non-traditional. We evaluated how users reacted to these types of visualizations and what users' features are associated with their preferences for one category or the other. In this paper, we surveyed 235 teachers to evaluate how these two categories of visualizations affect the way participants evaluated data from an online course. They had to check the visualizations and identify which item contributed the most, and which item contributed the least to the performance of the students. The answers (correct or incorrect) were evaluated regarding the teachers' age, gender, experience, education and perception on the usefulness of each visualization. Our ultimate purpose was to create a model to recommend visualizations according to the teachers' profile.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".