The Effects of Instructional Design on Student Engagement with Video Lectures at Cyber Universities
Bibliographic record
Abstract
Aim/Purpose: The number of students enrolled in online courses that use video lectures is on the rise. However, research shows that the number of students watching video lectures is low, and the number watching videos to completion is even lower. Background: This paper seeks to understand this problem by looking for correlations between instructional design and student engagement with video lectures. Methodology: Students at a cyber-university in South Korea (n=1801) were surveyed on their perception of the instructional design used in the courses they took and their engagement with online video lectures. Contribution: This paper contributes to the body of knowledge by demonstrating positive correlations between instructional design, watching, and finishing video lectures. Findings: While most other research has found low levels of online lecture viewership, this paper found significantly higher numbers watching and finishing videos. Other major findings of the paper are that five key elements of instructional design for online learning environments (designing methods, setting the curriculum, establishing time parameters, establishing netiquette, and utilizing the medium effectively) all correlated positively with students watching and finishing video lectures. Recommendations for Practitioners : Based on findings in this paper, it is recommended that practitioners consider taking actions when designing their instruction for online courses. These include batching their video lectures together by topic, devoting greater resources to helping students utilize the medium, and communicate time parameters in a way that encourages students to view video lectures in a timely manner. Recommendation for Researchers: As the watching of video lectures in this study was mandatory for learners, an interesting area of further research would be to examine whether that decision led to higher numbers of students watching them. Future Research: It is important for researchers to conduct further research into the interplay between ways instructors can design their instruction in order to encourage learners to better experience online 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 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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".