Queries and Cues: Textual Stimuli for Reflective Thinking in Digital Mind-Mapping
Bibliographic record
Abstract
Abstract Mind-mapping is useful for externalizing ideas and their relationships surrounding a central problem. However, balancing between the exploration of different aspects (breadth) of the problem with respect to the detailed exploration of each of its aspects (depth) can be challenging, especially for novices. The goal of this paper is to investigate the notion of “reflection-in-design” through a novel interactive digital mind-mapping workflow that we call “QCue.” The idea behind this workflow is to incorporate the notion of reflective thinking through two mechanisms: (1) offering suggestions to promote depth exploration through user’s queries (Q) and (2) asking questions (Cue) to promote reflection for breadth exploration. This paper is an extension of our prior work (Chen et al., 2020, “Qcue: Queries and Cues for Computer-Facilitated Mind-Mapping,” Proceedings of Graphics Interface 2020, GI 2020, Canadian Human-Computer Communications Society/Société canadienne du Dialogue Human-Machine, pp. 125–136) where our focus was mainly on the algorithmic development and implementation of a cognitive support mechanism behind QCue enabled by ConceptNet (a graph-based rich ontology with “commonsense” knowledge). In this extended work, we first present a detailed summary of how QCue facilitated the breadth-depth balance in a mind-mapping task. Second, we present a comparison between QCue and conventional digital mind-mapping, i.e., without our algorithm through a between-subjects user study. Third, we present new detailed analysis on the usage of different cognitive mechanisms provided by QCue. We further consolidate our prior quantitative analysis and build a connection with our observational analysis. Finally, we discuss in detail the different cognitive mechanisms provided by QCue to stimulate reflection in design.
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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 | 0.003 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".