Examining Student and Educator use of Digital Technology in an Online World
Bibliographic record
Abstract
Over the past thirty years, institutions of higher learning across the world have increasingly embraced digital technology for teaching and learning. Many institutions have begun to offer mobile, hybrid, and online courses and programs for enhanced relevance and accessibility. Universities and colleges employ digital technology through learning management systems for maintaining and processing educational information/records, offering blended/hybrid learning using asynchronous online student/instructor interaction and collaboration, and web conferencing software for synchronous and asynchronous virtual classroom functionality. Thus, it is critical for us to gain a better understanding the nature of these technological changes and the factors affecting the online realities of 21st Century teaching and learning. The study reported here involved students and instructors at the University of Ontario Institute of Technology (UOIT) in Oshawa, Canada using the General Technology Competency and Use (GTCU) Survey, in which they assessed the purpose and frequency for which they used a variety of digital technologies, and the confidence they had in using various digital technologies. Preliminary results indicated high scores in both confidence and frequency of use for computers/laptops and smartphones, and low scores for frequency of use and confidence with newer technologies, such as “wearables” and the “Internet of Things”.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".