Proceedings of the Twelfth Australasian Conference on Computing Education - Volume 103
Bibliographic record
Abstract
Welcome to the Twelfth Australasian Computing Education Conference (ACE2010). This year, the ACE2010 conference, which is part of the Australasian Computer Science Week, is being held in Brisbane Australia from 18-21 January, 2010. We again see a strong international presence at the conference with 51 authors coming from Canada, Finland, New Zealand, Scotland, Sweden, United States and Australia. The Chairs would like to thank the Program Committee for their excellent efforts in the double-blind reviewing process which resulted in the selection of 14 full papers from the 30 papers submitted, giving an acceptance rate of 47%. The keynote speakers this year are hosted by the other ACSW conferences, in line with the policy that the plenary sessions will rotate between the ACSW conferences each year. We are lucky though to have the services of three international invited speakers for this year's ACE conference. Nell Dale from Texas will talk on the topic of CS textbooks, and as a prolific textbook author herself we are lucky to have her share her insights. Arnold Pears from Sweden will address the topic of Quality Assurance in Computing Education and some current conundrums. Tim Bell from New Zealand will profile some promising new developments in the high school computing curriculum in New Zealand. We have a very diverse set of topics this year. Papers and presentations include collaboration technologies and Web 2.0, models and pedagogical frameworks for computing education, studies of novice programming students, student motivations and perspectives, the use of technology in computing education, course content, curriculum structure, methods of assessment, web development, online learning, and work-integrated learning through to graduate attributes. The high quality papers this year continue to push the frontiers of opportunities for research and innovation in computing education, and this conference will enable these educators to meet and share their experiences in a new forum. We will be holding a Panel on Internationalisation in Computing Education, profiling institutional developments, a major ALTC funded study into international students in Australian Universities and a critical perspective on Internationalisation and the 'Export Education' industry.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.269 | 0.136 |
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".