Incorporating Whiteboard Voice-Over Video Technology into the Accounting Curriculum
Bibliographic record
Abstract
ABSTRACT This article discusses how accounting instructors can adopt whiteboard voice-over (WBVO) video technology as a supplemental resource in traditional classroom designs or as an integral resource in a flipped or online classroom design. WBVO technology can facilitate a blended learning classroom design by allowing instructors and/or students to create short videos that can be posted in a learning management system or public domain. The benefits of utilizing WBVO technology are analyzed through the lens of variation theory, and include (1) providing students with additional instructional design materials to increase learning opportunities, (2) aiding instructors in focusing on the “process of learning” as opposed to the “product of knowledge” in order to make it easier for students to learn, (3) developing instructional design resources that are unique to the classroom learning environment to reduce the unintended consequences of adopting third-party materials that may have been designed for different learning objectives, (4) freeing up class time for active learning activities that focus on higher-order cognitive skills, and (5) reinforcing a student-centered learning environment. Observations from the classroom provide some preliminary empirical evidence to support the efficacy of utilizing WBVO technology to create instructional design materials. JEL Classifications: A220.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".