Collaborative mind mapping to support online discussion in teacher education
Bibliographic record
Abstract
Mind maps that combine text, images, colour and layout elements, have been widely used in classroom teaching to improve retention, knowledge organization and conceptual understanding. Furthermore, studies have shown the advantages of using mind-maps to facilitate collaborative learning. However, there are gaps in the literature regarding the use and study of collaborative mind-mapping in online learning settings. This integrated-article dissertation explores the implementation of online collaborative mind mapping activities in a mathematics teacher education program at a Canadian university. The studies were developed with participants enrolled in three different courses where at least two of the online activities used collaborative mind mapping for knowledge construction. Rather than prove the efficacy of a visual tool, as other studies have, this research provides an understanding of how the learning and knowledge construction process occurs when student interact with one another using a mind mapping tool. The set of articles contained in this dissertation answers to the questions: (1) What are the roles that collaborative mind mapping plays in the participants’ education as mathematics teachers? (2) What are the differences between student interaction in threaded forums and mind-maps? (3) How does online collaborative mind mapping enhance the aspects of engagement, representation, and expression in teacher education? (4) How can grounded theory methods be developed with sources of online multimodal data such as online mind mapping? (5) How do students interact and construct knowledge when they engage in online collaborative mind mapping? The research view is qualitative and uses a variety of descriptive case study, content analysis, and constructivist grounded theory methods. This dissertation provides insights into online collaborative knowledge construction when using collaborative mind-mapping and adds to the existing literature on online learning, especially concerning the use of visual, collaborative tools. It contains guidelines and suggestions to implement this type of learning experiences in other courses and/or other education levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".