Bibliographic record
Abstract
At CHI 2018, a workshop on developing a community of practice to support global HCI education was held, building on six years of research and collaboration in the area of HCI education. Many themes emerged from the workshop activities and discussions. Two particularly stood out: creating channels for discussions related to HCI education and providing a platform for sharing HCI curricula and teaching experiences. To that end, we are organizing a CHI 2019 symposium dedicated exclusively to HCI education: EduCHI 2019: Global Perspectives on HCI Education. The symposium will focus on the canons of HCI education in 2019 and beyond. It will offer a venue for HCI educators across disciplinary and geographical borders to discuss, dissect, and debate HCI teaching and learning. Through keynote addresses, paper presentations, and a panel discussion, we aim to discuss current and future HCI education trends, curricula, pedagogies, teaching practices, and diverse and inclusive HCI education. Post-symposium initiatives will aim to document and publish the discussions from the symposium.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.205 | 0.104 |
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".