Language students and their technologies: Charting the evolution 2006–2011
Bibliographic record
Abstract
Abstract This paper has two key objectives. Firstly, it seeks to record the technologies in current use by learners of a range of languages at an Australian university in 2011. Data was collected via a large-scale survey of 587 foreign language students across ten languages at The University of Queensland, Brisbane, Australia. Notably the data differentiates between those technologies that students used inside and outside of formal classrooms as well as recording particular technologies and applications that students perceived as beneficial to their language learning. Secondly, this study aims to compare and contrast its findings with those from two previous studies that collected data on students’ use of technologies five years earlier, in 2006, in the UK and Canada. The intention is to chart major developments and changes that have occurred during the intervening five-year period, between 2006 and 2011. The data reported in two studies, one by Conole (2008) and one by Peters, Weinberg and Sarma (2008) are used as points of reference for the comparison with the present study. The findings of the current study point to the autonomy and independence of the language learners in this cohort and the re-emergence of CALL tools, both for in-class and out-of-class learning activities. According to this data set, learners appear to have become more autonomous and independent and much more able to shape and resource their personal language learning experience in a blended learning setting. The students also demonstrate a measure of sophistication in their use of online tools, such that they are able to work around known limitations and constraints. In other words, the students have a keen awareness of the affordances of the technologies they are using.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".