Introducing Multimedia Presentations and a Course Website to an Introductory Sociology Course: How Technology Affects Student Perceptions of Teaching Effectiveness
Bibliographic record
Abstract
I use a quasi-experiment and follow-up questionnaire to ascertain the effects of PowerPoint multimedia presentations and a Blackboard course website on the course grades and perceptions of teaching effectiveness of introductory sociology students. Results of t-tests showed no statistically significant difference in course grades between experimental and control groups. However, students' responses to standardized teaching evaluations were considerably more favorable in the experimental group; all measured dimensions of perceived teaching effectiveness yielded statistically significant increases, with substantial increases in perceptions of instructor rapport and grading. I use the ideas of George Herbert Mead to interpret the results and increase sociological understanding of the relationship between the introduction of instructional technology and student perceptions of teaching effectiveness. In Mead's terms, the introduction of technology is not merely a self-involved act performed by the instructor that changes the modality of course presentation but a social process involving both instructor and students. Within this process the introduction of technology is both a nonsignificant gesture, which elicits from students an unconscious or “instinctively” favorable impression of the course, and a significant symbol, which calls forth behavioral responses from students, conscious actions that substantially alter their perceptions of the course. Students not only reacted positively to the instructor's use of technology but through their own use of the technology increased their involvement in the course and came to perceive its teaching more favorably.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".