Language Teachers and Their Trajectories Across Technology-Enhanced Language Teaching: Needs and Beliefs of ESL/EFL Teachers
Bibliographic record
Abstract
In this article, we present the initial results of the first phase of our international Research Network, which sets out to revisit the current needs of language teachers in terms of training to achieve the integration of technology within their educational contexts. We focus on the type of needs and their order of priority from the viewpoint of English as a Second/Foreign Language (ESL/EFL) teachers and suggest some recommendations for training programs. These data, collected via an online questionnaire distributed in several countries, were analyzed quantitatively and qualitatively in relation to participants’ perceptions of needs regarding language education technology, as well as participants’ perceptions of the “ideal” teacher, and their relation to the use of technology. Results indicate that despite overall satisfaction regarding training received, many teachers mention their need for a posttraining follow-up. They also express interest in developing “learning task design” skills and in exploring course management platforms such as Moodle. Furthermore, while most participants fully or partially agree with the relationship between technology and excellence in language teaching, their definition of the “ideal” language teacher rarely includes the use of technology. A shift in “ideal” teacher beliefs seems, therefore, necessary for better adoption and use of technology in language education. Dans cet article, nous présentons les premiers résultats de la phase initiale d’une étude effectuée par notre Réseau international de recherche, qui vise à réexaminer les besoins actuels des professeurs de langues en termes de formation sur l'intégration de la technologie dans leur propre contexte éducatif. Nous mettons l’accent sur les types de besoins et leur ordre de priorité du point de vue des professeurs d’anglais langue seconde/langue étrangère (ESL/EFL) en proposant plusieurs recommandations pour les programmes de formation. Les données ont été collectées par le biais d’un questionnaire en ligne distribué dans plusieurs pays, et ont fait l'objet d'une analyse quantitative et qualitativement par rapport aux perceptions des participants sur les besoins en matière de technologie de l'enseignement des langues. Le questionnaire interroge aussi les participants sur leur perception de l'enseignant « idéal », et leur relation avec l'utilisation de la technologie. Les résultats indiquent que malgré la satisfaction générale concernant la formation reçue, de nombreux enseignants mentionnent leur besoin d'avoir un suivi. Ils expriment également leur intérêt à développer des compétences en conception de tâches d’apprentissage et à explorer des plates-formes de gestion des cours comme Moodle. En outre, bien que la plupart des participants reconnaissent un lien positif entre la technologie et l'excellence dans l'enseignement des langues, leur définition du professeur de langues « idéal » inclut rarement l'utilisation de la technologie. Il semble donc nécessaire que les croyances relatives à la définition du professeur de langue « idéal » évoluent afin de favoriser l’adoption et l’usage de la technologie dans l’enseignement de langues.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".