Argument Visualization with DMaps: Cases from Postsecondary Learning
Bibliographic record
Abstract
The Dialectical Map (DMap) is an open-source, web-based argument visualization tool developed and used at a Canadian University to scaffold argument construction. To illustrate the ways that argument mapping can be used in undergraduate courses, this article presents five cases selected from courses in biology, psychology, computing science, and English as a foreign language offered at three post-secondary institutions. Each case explains how argument mapping with DMaps (DMapping) was implemented and assessed in a course. Students responded to a questionnaire that gathered their attitudes toward DMapping as a learning activity. In each course, students were also interviewed about their DMapping experiences. The interview and questionnaire data indicated that students believed DMapping was an effective way to meet the knowledge objectives of their course and to learn about argumentation. The authors explain how DMap assignments added value to their courses by helping students think critically about course topics while developing their argumentation ability and information literacy. Finally, we summarize the lessons learned across the cases and discuss ways of maximizing the benefits of argument mapping activities for postsecondary learning.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".