Mediation and the Action-Oriented Approach in Language Education: The Learner Experience
Bibliographic record
Abstract
The teaching of an additional language in a communicative educational context in Canada is traditionally approached through the development of four linguistic skills: listening, speaking reading, writing (Fox, Cheng, & Zumbo, 2014). The seminal Common European Framework of Reference for Languages: Learning, teaching, assessment (CEFR) (Council of Europe, 2001) for language learning and teaching initiated a significant action-oriented paradigm shift from skills to modes of communication, namely: reception, interaction, production, and mediation. Since its initial timid yet pivotal presence in the CEFR 2001, mediation in particular has been attracting the interest of researchers, as this mode was further conceptualized to reveal it complex and rich nature (Coste & Cavalli, 2015; North & Piccardo, 2016; Zarate, 2003), and this led to the development of illustrative descriptors for it (North & Piccardo, 2016; North & Panthier, 2016), which informed the new updated version of the CEFR, the so-called CEFR Companion Volume (CEFRCV) (Council of Europe, 2020). Despite the growing interest in the subject, there are very few studies that focus on the implication and the learners’ experience on mediation-based tasks within an Action-oriented Approach in the language classroom. My research recruited a group of 12 adults (aged 19-45) intermediate level learners (CEFR B1) enrolled in an English for Academic Preparation program in an Ontario post-secondary institution. The participants completed 5 researcher-designed mediation-based, Action-Oriented tasks in small groups, and within the different learning scenarios they were required to collaborate and mediate text, concepts, communication, as well as to use mediation strategies, to explain new ideas, to adapt a text, and to create a final artifact for each task. These activities took place online via the ZOOM platform, and qualitative data were collected via transcription of video recordings of task completion, all 12 participants’ individual interviews, participants’ self assessments as well as researcher and peer-researchers’ task observations. These data were analyzed using theme identification, synthesis, and pattern identification via primarily inductive but also deductive coding. Findings reinforce that mediation and the Action-oriented Approach motivate learners, and also uncovers an increase in social agency with strong promise for language teaching and 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".