The Kinetic-Vectorial Design of Internet-Mediated L2 Teaching Practices: The Case of BBC Learning English Website
Bibliographic record
Abstract
This study offers new insights into the educational-linguistic visual semiotics of online teaching as a digital practice with kinetic-vectorial design. Towards this end, a twofold social semiotic approach is utilized as a synthetic methodology. First, Van Leeuwen’s (2016) kinetic design model is employed with a view to revealing the movement types of Internet-mediated L2 teaching practices. Second, Kress and Van Leeuwen’s (2021) model of ideational vector analysis is used in a way that uncovers the subtle pedagogical practices of the same type of teaching as directional and transactional acts addressed to educational-website networked L2 learners worldwide. The data sets targeted for analysis comprise screenshot-styled images from the BBC Learning English Website available for public purposes of L2 teaching. A total number of functionally related five images have been selected with an eye to the BBC’s pedagogic content online on the techno-semiotic levels of design kinetics and vectoriality. The current study has reached three findings (with emerging relevant implications). First, the synthetic kinetic-vectorial method of analysis has proved to be empirically effective in investigating the visual semiotics of design mediated by educational websites such as the BBC Website. Second, there emerged kinetically motivated ‘pedagogic’ and ‘digital’ vectors, respectively controlled by the BBC instructors and the design features of the website itself. Third, and last, the kinetic-vectorial analysis of the BBC Website revealed a sort of spatiotemporal compression of pedagogic content; further, the visual aspects of spatial and temporal movements (mobility and movability) appeared to have occurred across different semiotic modes, verbal and visual.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".