Text-Based Video: The Effectiveness of Learning Math in Higher Education Through Videos and Texts
Bibliographic record
Abstract
The Text-Based Video (TBV) model is a particular case of the more general Video-Based Learning (VBL) model in which an instructor’s curriculum is fully covered by high-quality videos and texts. The aim of this study is to test the effectiveness of the TBV model by examining and comparing its two main components: Videos and texts. The model is based on the creation of high-quality texts which form the basis for high-quality video clips. It is designed to improve learning in quantitative courses in higher education. The research was based on a sample of students who enrolled in the course Mathematics for Business Administration at the Neri Bloomfield School of Design and Education, Haifa, Israel that was based on the TBV model. The course was given during the five academic years 2016-2021 using different teaching formats: face-to-face learning, distance learning and blended learning. Learners were asked to answer an online questionnaire that assessed the characteristics and advantages/disadvantages of TBV. The findings show that although students preferred watching videos based on texts over reading those texts alone, students opined that the combination of video and text was by far the most effective instructional method. All results were identical regardless of whether face-to-face, distance or blended learning was used.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".