EduCHI 2020: 2nd Annual Symposium on HCI Education
Bibliographic record
Abstract
The past few years has seen steady growth for the HCI Education Community of Practice (CoP), driven primarily by the "HCI Living Curriculum" workshop at CHI 2018 and the inaugural EduCHI symposium at CHI 2019. In discussions among HCI educators over the past two years, two themes have 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 EduCHI 2020: The 2nd Annual Symposium on HCI Education. Similar to last year's symposium, EduCHI 2020 will again feature paper presentations about HCI education trends, curricula, pedagogies, teaching practices, and diverse and inclusive HCI education. In addition, we will also be adding more opportunities for discussions among and between members of the HCI education community, particularly around solving current and future challenges facing HCI educators.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.150 | 0.089 |
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".