Proceedings of the 21st Annual Conference on Information Technology Education
Bibliographic record
Abstract
It gives us immense pleasure to welcome you to the 21st Annual Conference of the Special Interest Group in IT Education (SIGITE 2020) being held virtually during a unique time in our lives. It has been quite a year already. The world around us continues to face an unprecedented time with the pandemic and years of structural racism. We greatly appreciate your continuing understanding and flexibility as the situation demanded. We also wanted to extend our sincere gratitude to all the authors and the more than one hundred reviewers for engaging with SIGITE 2020 despite the stresses of the pandemic in everyone's lives. We are thrilled to inform you that the conference received 118 high quality submissions from authors representing 95 different universities from around the world including USA (90%), India, Italy, Thailand, Austria, Canada, Chile, China, Hungary, Indonesia, Nigeria, Philippines, Romania, Tunisia, UAE, Ireland, UK, and Mexico. We are also excited to have three outstanding keynote speakers for the conference - Mr. Steve Kaniewski, President and CEO of Valmont Industries, who used to be the SVP/CIO of the same company; Dr Maria Telleria, Canvas Co-Founder and CTO; and Dr. Lecia Barker, NCWIT Senior Research Scientist and Associate Professor at University of Colorado Boulder. The final conference program has 57 completed research papers, 21 posters/extended abstracts, 5 big idea talks, 6 Work in Progress research papers, 3 panels, 4 workshops and 4 teacher experience track talks. All in all a truly representative collection of work in the IT Education and affiliated domains. Putting together SIGITE2020 was a team effort primarily led by the conference and program cochairs and included a number of graduate student volunteers helping with the logistics of the conference. We thank the authors for providing the content of the program. We appreciate our employer, the University of Nebraska at Omaha and in particular the College of Information Science & Technology for supporting us and providing in-kind material and people support for the conference. We express our sincere gratitude to our generous local supporters who agreed to continue supporting the conference even after Covid-19 budget cuts in their organizations. We encourage you to thank these supporters/exhibitors since their contribution allowed a dramatic reduction in our registration rates for all delegates to the virtual conference. Specifically: platinum supporters: Nebraska Tech Collaborative and Union Pacific Railroad; Silver supporters: Conagra Foods and Blue Cross Blue Shield of Nebraska; Academic Supporter Heider School of Business at Creighton University; and other supporters include First National Bank of Omaha, Metropolitan Community College and Prospect Press. Finally we would extend our thanks to the ACM staff for giving us rapid support as the situation changed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.285 | 0.195 |
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".