A Festschrift to Honour Jim Greer: Guest Editors’ Introduction
Bibliographic record
Abstract
issue of IJAIED as an honour to Jim emerged over the next few months.The goal was to create a "Festschrift", a collection of papers by researchers who felt they had been inspired by Jim and/or influenced by his research insights.The Festschrift special issue was formally approved by the IJAIED editorial board in 2019, with the two of us appointed as "guest" editors.We created an open Call for Papers that was distributed widely to the AIED, UMAP, and related communities during the (northern) summer of 2019.The Call requested that "…each [submitted] paper should be a contribution to the AIED field presenting a novel research project or result, an integrative summary of an ongoing research program, or an insightful reflection or survey of a field of research that overlaps one or more of Jim's various interests.To ensure that the special issue is a true tribute to Jim, we ask that each paper explicitly communicate Jim's influence on the research or researcher."Papers started to arrive in late 2019 and continued to flow in over the next many months.By June of 2021, after full reviewing (often with several reviewing cycles) 24 papers had ultimately been accepted for the special issue.It is our pleasure to present this Festschrift.We believe it is a fitting tribute to Jim Greer's influence on the researchers and generally on the fields of AIED and UMAP.Jim Greer was a longtime Professor of Computer Science at the University of Saskatchewan.Throughout his life Jim had an unwavering commitment to teaching and learning, earning many degrees himself, teaching at the high school and university levels, taking leadership roles in improving the teaching and learning environment at the University, and exploring deep issues in advanced learning technology in his research career.For all his efforts, Jim was awarded the Province of Saskatchewan Centennial Medal in 2006, which "recognizes individuals who have made significant contributions to society and honours outstanding achievements".Jim grew up on a farm near Weyburn, Saskatchewan, completed high school at St. Peter's College in Muenster, Saskatchewan, and came to the University of Saskatchewan (UofS) in 1970 where he completed his B.Sc. in Mathematics in 1973 and his B.Ed. in Mathematics Education in 1976.He taught high school in Saskatoon, Saskatchewan, from 1974 to 1976, and then in Lethbridge, Alberta, until 1982.In 1982, in a life-transforming move, he and his wife Marianne, with their young family in tow (son Phil and daughter Erin), upped stakes and headed off for further education and new careers.The first stop was back at UofS, where Jim completed an M.Ed. in Mathematics Education in 1984 and an Advanced Certificate in Computational Science.After that they were off to the University of Texas at Austin, where Jim earned both an M.S. in Computer Science and a Ph.D. in Computer Science Education.In 1987 Jim and Marianne came back to UofS again, where Jim joined the Department of Computational Science (now Computer Science) as a postdoctoral fellow, working with Gord McCalla.Together, Jim and Gord established the Laboratory for Advanced Research in Intelligent Educational Systems (ARIES), a lab that went on to carry out world class research in advanced
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.028 | 0.025 |
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".