Ethical Use of Technology in Digital Learning Environments: Graduate Student Perspectives, Volume 2
Bibliographic record
Abstract
This book is the result of a co-design project in a class in the Masters of Education program at the University of Calgary. The course, and the resulting book, focus primarily on the safe and ethical use of technology in digital learning environments, and is the second volume in the series. The course was organized according to four topics based on Farrow’s (2016) Framework for the Ethics of Open Education. Students were asked to review, analyze, and synthesize each topic from three meta-ethical theoretical positions: deontological, consequentialist, and virtue ethical (Farrow, 2016). The chapters in this open educational resource (OER) were co-designed using a participatory pedagogy with the intention to share and mobilize knowledge with a broader audience. The first section, comprised of four chapters, focuses on topics relating to well-being in technology-enabled learning environments, including the use of web cameras, eproctoring software, video games, and access to broadband connectivity. The second section focuses on privacy and autonomy of learners and citizens in a variety of contexts from schools to clinical settings. In each of the seven chapters, the authors discuss the connection to the value of technology in education, and practical possibilities of learning technologies for inclusive, participatory, democratic, and pluralistic educational paradigms. The book concludes with reflections from the course instructor gained over two iterations of teaching the course. This is a static version of the text; the live Pressbook can be accessed via https://openeducationalberta.ca/educationaltechnologyethics2/
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".