Media Usage Survey: How Engineering Instructors and Students Use Media
Bibliographic record
Abstract
Web 2.0 has ubiquitously penetrated academia. The dissemination of online information services in higher education has led to substantial changes in faculty teaching methods as well as the learning and study behavior of students. For example, the use of online services, such as Google and Wikipedia, has become mandatory not only during teaching and learning activities but also during leisure time for students and faculty. At the same time, traditional information media such as textbooks and printed handouts still form the basic pillars of teaching and learning. This article explains the preliminary results of a survey about media usage in teaching and learning conducted with Western University faculty and students, highlighting trends for the usage of new and traditional media in higher education. Furthermore, the article intends to participate in the ongoing discussion of practices and policies that purport to advance Web 2.0 has ubiquitously penetrated academia. The dissemination of online information services in higher education has led to substantial changes in faculty teaching methods as well as the learning and study behavior of students. For example, the use of online services, such as Google and Wikipedia, has become mandatory not only during teaching and learning activities but also during leisure time for students and faculty. At the same time, traditional information media such as textbooks and printed handouts still form the basic pillars of teaching and learning. This article explains the preliminary results of a survey about media usage in teaching and learning conducted with Western University faculty and students, highlighting trends for the usage of new and traditional media in higher education. Furthermore, the article intends to participate in the ongoing discussion of practices and policies that purport to advance the effective use of media in teaching and learning.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".