Effectiveness of visual communication and collaboration tools for online GIS teaching: using Padlet and Conceptboard
Bibliographic record
Abstract
Geographic information systems (GIS) have become more suitable for online delivery. But teaching GIS online is challenging because, without enough interactions with the instructor or among themselves, students may not understand processes, use critical thinking, and collaborate effectively on a team project. This study aims to evaluate two online visual collaboration tools, Padlet and Conceptboard, in enhancing learner engagement, collaboration, and visual communication in an online GIS course. We analyze 1) usage patterns and their relationship with student performance, 2) students’ opinions on the added values of the tools compared with face-to-face delivery, and 3) students’ qualitative feedback. Student findings show that those tools, particularly Conceptboard, help students get timely help from other students and the instructors, be motivated by participating in discussions and seeing others’ progress, organize and brainstorm project ideas, and summarize and present the final products. The combination of Conceptboard and an online video conference tool is a particularly effective alternative to the face-to-face learning environment. On the other hand, we also found significant challenges with remote teamwork in GIS. The course design incorporating online collaboration tools can provide practical strategies to instructors who teach GIS or similar software online.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".