Comparison of the Effectiveness of Distance Learning for Software Courses in Higher Education: Videos vs. Texts
Bibliographic record
Abstract
This study investigates the effectiveness of video-based learning compared to traditional text-based methods in distance education for software courses. The research was based on two samples of students (n1=32, n2=30)  enrolled in the "Fundamentals of PSPP" distance course at the N.B. School of Design and Education (PSPP is a free alternative to SPSS). Students were asked to fill in a questionnaire at the end of the year indicating their views on the two methods of learning. Results indicate that students who utilized video content exhibited higher levels of understanding and satisfaction. Videos combining visual and auditory elements were found to significantly enhance learning outcomes by reducing cognitive load and providing clearer demonstrations of software procedures. The study highlights the advantages of video-based learning in fostering a sense of connection between instructors and students, which enhances motivation and engagement in asynchronous learning environments. The study concludes that video-based learning is a superior method compared to text-based learning for teaching complex software skills in distance education, promoting higher student achievement and engagement.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".