Bibliographic record
Abstract
The creation of Inclusive and Accessible Open Educational Resources (IA-OERs) is a challenge for teachers because they have to invest time and effort to create learning contents considering students' learning needs and preferences. An IA-OER is characterized by its alignment with the Universal Design Learning (UDL) principles, the quality on its contents and the web accessibility as a way to address the diversity of students. Creating an IA-OER with these characteristics is not a straightforward task, especially when teachers do not have enough information/feedback to make decisions on how to improve the learning contents. In this paper we introduce ATCE - an Analytics Tool to Trace the Creation and Evaluation of IA-OERs. This tool focuses in particular on the accessibility and quality of the IA-OERs. ATCE was developed as a module within the ATutor Learning Management System (LMS). An analytics dashboard with visualizations related to the teachers' competences in the creation and evaluation of IA-OERs was included as part of the tool. This paper also presents a use case of the visualizations obtained from the creation and evaluation of one IA-OER after using our analytics tool.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".