Changing Practice and Enabling Development: The Impact of Technology on Teaching and Language Teacher Education in UAE Federal Institutions
Bibliographic record
Abstract
The global trend of increased technology use for information access, communication and entertainment is extending into educational settings, prompting educators to consider the role of technology and review more traditional teaching and learning methodologies. Whether or not we agree with the growing opinion that “Traditional teaching and learning methods are becoming less effective at engaging students and motivating them to achieve” (Gitsaki et al., 2013: 1), the use of technology in English language teaching and learning is increasing. Technology is moving from being a supplementary resource (e.g. language labs, Computer Assisted Language Learning) to a means of language instruction and practice, made increasingly easier by personal and mobile devices. However, it is well recognized that the successful integration of new technologies in education is dependent on teachers (Mumtaz, 2000; Albrini, 2004; Judson, 2006; Keengwe et al., 2008; Rossing et al., 2012). Their personal beliefs, assumptions and attitudes to technology will influence the acceptance, use, effectiveness and success of new initiatives; therefore, teachers who are required to implement change need sufficient time, support and training, without which they are unlikely to see the value and affordances of new technology. It is important, then, that teachers in this environment are effectively prepared for potential changes in classroom practice (Ess, 2009) and supported in ongoing learning (Abadiano & Turner, 2004; Borko, 2004).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".