The "T3 Support Centre" (Teaching, Technology and Testing) - Not just another help desk
Bibliographic record
Abstract
Many faculty members embrace the challenge of responding to rising student demands for more technically advanced course supports by offering their courseware through a variety of media. However, it is often difficult for them to find the time required to become proficient in the use of the software packages, course management systems and web technologies at their disposal. These new realities of teaching point to the need for support systems for faculty members that go beyond the traditional computer services "help desk" with a more comprehensive support service that actually becomes involved in the development and modification of technology-based course materials and computerized test marking and analysis. Increasing demand for these types of services at Carleton University resulted in the establishment of the T3 (Teaching...Technology...Testing) Support Centre. The service offers faculty members extended-hour phone-in and walk-in support as well as a variety of resources such as Scantron and Item Analysis service for multiple choice exams, the use of scanners and colour printers, as well as a variety of teaching publications and contacts. This paper details the planning, administration, and services offered of the T3 Service, including advice those attempting to establish a similar service. Usage statistics from the first year of operations will be delineated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.195 | 0.100 |
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".