Embracing OneNote as an Online Pedagogy
Bibliographic record
Abstract
The COVID-19 pandemic has forced institutions worldwide shifting to fully online learning along with the campus shut down to avoid the spread of virus in year 2021. Considering the necessity of distance education during the pandemic, the Internet of Thing (IoT) and technology play an important role in keeping all connected. In this study, the usability and adoption of Microsoft OneNote class notebook via MS Teams are presented. In view of mathematics classes require a lot of annotation and demonstration of calculation solutions, OneNote class notebook via MS Teams is adopted due to its built-in content library space and collaboration space which act as the digital whiteboard for teachers and students. Besides, OneNote class notebook is able to provide a real-time synchronized space for all the mathematical works. The convenience, usefulness and real-time features of OneNote class notebook via MS Teams make it a powerful tool and as a substitute for the traditional classroom. The chat history and work done in the collaboration space during the break-out room sessions via MS Teams offer the students space to recall and retrieve what has been learned in the class. The COVID-19 crisis provides us an opportunity to blend in the component of IoT in education such as live and collaborative platforms in OneNote class notebook to complement both face-to-face and online education.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".