Bibliographic record
Abstract
Spreadsheet use in educational environments has become widespread, likely because of the flexibility and ease of use of these tools. However, they have serious shortcomings if the teacher is to understand exactly what students or others have done. It is far too easy for students to replace a formula that gives an apparently unacceptable answer with a number that they believe to be correct. The same concern applies to recorded marks, as well as to business spreadsheets and to other reports that are used for decision-making. While intentionally misleading changes to spreadsheet files receive much attention, simple mistakes are probably more common. Some of these, such as the Trans-Alta Utilities (Globe and Mail, 2003) cut and paste error that cost the firm $24 million (US), have extreme consequences. Few are merely embarrassing. A log file or audit trail, enhanced by suitable filters, can allow both intentional and accidental changes that cause erroneous results to be caught. In order to meet these requirements, we have developed server based software tool (“TellTable”) which allows editing, version control, and auditing of spreadsheet files. Users connect to the server using a standard web browser, and are able to access and edit spreadsheet files in a Java applet in the browser window. TellTable has been used for a pilot study to maintain marks and course information for a multi-section courses with several instructions and teaching assistants. This paper describes the TellTable software and preliminary results of the pilot test.
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 | 0.007 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".