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.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".