Academic Rigour and Video Technology: A Case Study on Digital Storytelling in Graduate-level Assignments
Bibliographic record
Abstract
While some may perceive technology as disruptive in higher education, this chapter makes a case that video technology can be used to increase collaboration and engagement in learning and teaching. It is argued that digital storytelling can be integrated as part of the assessment in graduate-level courses without compromising expectations related to academic rigor. Rather, digital storytelling advances multimedia literacy for the individual and supports the generation of bounded learning communities, specifically in online and blended programmes. Covering social presence, teaching presence and cognitive presence, the chapter draws on two examples of digital storytelling used in the MA in Conflict Analysis and Management and the MA in Global Leadership at Royal Roads University, Canada. Overall, the chapter makes a contribution to the conversation of how assessment formats can be updated to match the shift from traditional, lecture formats and brick-and-mortar institutions to applied, collaborative programmes that are often delivered in blended and online formats. Thus, as the field of higher education continues to evolve and adapt alongside technological innovations, the chapter suggests that digital storytelling can be one way to complement and update assessment formats to match the evolution of the twenty-first century.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".