What’s app? Negotiating the good, bad, and ugly of apps for (English and other) language learning.
Bibliographic record
Abstract
While schools attempt to merge new technologies and digital literacies in curricular instruction, the social permeation of mobile digital devices, uptake of social media, and utilization of apps is far more evident in social practice. Mobile digital technologies enabling novel learning designs hold much promise for both classroom (Mahruf et al, 2010; McCombs et al, 2006), and self-access learning (Kukulska‐Hulme, 2009). However, a 2014 survey of Canadian teachers’ use of educational technologies (Mindsharelearning, 2014), confirms that the use of mobile devices in the classroom lags behind their ubiquity in social spheres. The ubiquitous app—a third party computer program designed for mobile devices, and available at minimal or no cost to the user—is less than a decade old. In 2007, Apple produced a game-changing smart phone, which was a powerful portable computer capable of wireless Internet connection (Sanford, 2015), and enabling the user to communicate interactively in context. This presentation draws data from two studies on mobile language learning apps to disentangle the growing profusion of apps for language learning, examine the theory and pedagogy behind popular language teaching apps and consider how the affordances of mobile technologies might be imaginatively utilized for effective teaching and learning.
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 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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".