Streaming Video and Podcasting Enhancements in the Post-secondary Classroom
Bibliographic record
Abstract
The use of online learning enhancements is becoming more popular in post-secondary instruction, and certainly distance education and lecture based institutions are exploring the use of elearning strategies. Unfortunately, there is very little available research in the literature that examines the use of streaming video and podcasting as enhancements to in class post-secondary instruction. This paper explores the use of short streaming video clips and audio podcasts with students enrolled in a lecture based pre-service teacher education class. Seven short video clips were developed that extended the classroom lecture material. The clips consisted of Power Point slides enhanced with voice, diagrams, and pictures. The clips were also made available to the students as audio only podcasts. Students voluntarily chose whether or not to view the video clips or listen to the audio only podcasts. A supporting study guide and workbook were also provided online. Student perspectives on the use of these online enhancements were gathered during the course feedback process. The benefits and pitfalls of using streaming video/podcasts as part of postsecondary instruction are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".