Can videos affect learning outcomes? Evidence from an actual learning environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine the effect of an innovation in an educational context, a class of 500 + first-year economics students at a well-known Australian university. We study whether introducing content in the form of a multimedia presentation has a detectable effect on specific categories of student knowledge. The multimedia presentation has a narrator presenting concepts with images, words, and worked examples. Our key outcome measure is the probability of answering questions correctly on a mid-term test. A quasi-experimental design is followed to offer a causal interpretation of the results. We find that the multimedia presentation markedly increases students' academic outcomes on the test compared to those that did not view the presentation, especially in regards to procedural and evaluative knowledge. An additional survey reveals gains in students' metacognitive knowledge. These findings suggest that multimedia presentations contribute to improved student learning outcomes and offer valuable options at a time of increased online course delivery. The findings also highlight the relevance of investing in education and resources to develop the necessary design skills among academics and staff. Supplementary Information: The online version of this article contains supplementary material available 10.1007/s11423-022-10147-3.
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.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it