An Interactive OER Course Development at Athabasca University based on ODL Principles
Bibliographic record
Abstract
On October 16-18, I attended ICDE 25th 2013 conference hosted in Tianjin of China. At the conference I presented a paper titled: “An Interactive OER Course Development at Athabasca University based on ODL Principles to Increase Completion Rates in Calculus”, which was written by Dr. Sandra Law and me. This paper documents an Inukshuk Wireless-funded project that involved the design and development of an authoring tool (Athabasca University Tutor Authoring Tool or AUTAT) that was used to create a set of standalone learning modules intended for use by students struggling in first-year calculus courses. Introductory calculus is a popular course at universities across Canada but has one of the lowest completion rates of all courses offered at the introductory level. Interactive components of the just-in-time learning modules were designed using the AUTAT. This paper was awarded as a) The Honorable Mention of the Best Paper of ICDE25th; b) ICDE Prizes for Innovation and Best Practices of 2013; This award recognizes all of the work done by a team of AU employees (learning designers, editors, web specialists, visual designers, Flash specialists, and faculty) to move mathematics instruction into the online environment and to participate in the open education movement (by providing the learning modules and the AUTAT to the world at large through the AU OCW site http://ocw.lms.athabascau.ca/course/view.php?id=5). We would like to acknowledge the assistance of content experts and instructional designer from member institutions within the Canadian Virtual University (CVU) for their reviews of the modules. The work completed on this project has informed course design in mathematics, e.g. use of MathML (W3C recommended format for displaying mathematics online).
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".