Stratégies d'apprentissage des langues secondes dans un environnement informatisé : une méta-analyse qualitative de l'utilisation du courrier électronique
Bibliographic record
Abstract
This thesis examines the application of Information and Communication Technologies (ICT) to Second-language (L2) learning. It analyzes the use of electronic mail as a cognitive tool and aims at providing a better understanding of the learning process in a computerized environment. In this meta-analysis, qualitative data were drawn from independent studies (n=29) published between 2000 and 2010. \nThe thesis briefly reviews historical and theoretical perspectives on Computer-Assisted Language Learning (CALL) and language learning strategies. Then, with reference to Oxford’s (1990) typology, it investigates the use of learning strategies in email exchanges or projects of L2 learners. The identification of five categories of learning strategies (cognitive, social, meta-cognitive, compensatory and affective) constitutes the ground work to defining the paradigms of L2 learning associated with the use of electronic mail.\nThe study draws parallels between this electronic learning environment and Jonassen et al.’s (1999, 2008) five principles of meaningful learning, namely active, constructive, intentional, authentic and cooperative learning. Furthermore, a (non-exhaustive) list of five variables associated with successful L2 learning via email interaction (sustained communication, proficiency level in L2, audience interaction, structure of the language-related task and the topics of email correspondence) is presented.\nAs demonstrated in this research, this ICT’s ability to provide a favorable L2 learning environment is threefold. First, the use of electronic mail, as a cognitive tool, fosters learners’ activation of learning strategies. Second, patterns reflecting the principles of self-appropriated learning in the electronic environment suggest its role in the development of transversal skills. Finally, attitude changes towards L2 culture and stereotypes and towards L2 learning, among others, indicate modifications to learners’ behavior.\nThis study also provides updates to Oxford’s (1990) typology of learning strategies in the five categories identified, based on data from the 29 studies.\nThe pedagogical implications, discussed in the conclusion, draw attention to the qualitative and non-linguistic learning outcomes, as well as to the social, affective and cultural dimensions related to the use of email interaction in L2 learning.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".