Computer mediated communication and task-based learning for adolescent learners of Chinese as a foreign Language in Ireland: An eBook task design under the adaption of Bridge 21 technology-mediated learning model
Bibliographic record
Abstract
The Bridge 21 learning model emphasizes teamwork and technology mediation in the process of activity implementations. With the introduction of Chinese as a Leaving Certificate specification in the Irish secondary education system in 2020, there is a growing interest in Chinese language among schools, parents, as well as students. There are three types of Chinese language courses run through Irish secondary school settings: Junior Cycle (JC) Chinese Short Course, Transition Year (TY) Chinese and Leaving Certificate Mandarin Chinese. However, compared to other Anglosphere countries (e.g., UK, Australia), Ireland is in the early development stages of Chinese as a Foreign Language (CFL) learning (Osborne et al., 2019). Despite this, the open and flexible principle of Chinese course syllabi in JC and TY not only provides Chinese language teachers with the freedom of selecting contents, but also makes it possible to adapt Bridge 21 model into teaching practice. However, Chinese as a curriculum specification at JC in Ireland has not been explored in depth or been combined with the Bridge 21 model. Therefore, this paper aims to elaborate on the design and implementation of an eBook activity which aligns to the Bridge 21 model in a JC Chinese course. The preliminary findings of participants’ reflections suggest that the majority of participants had a positive experience in this activity and identified language development, especially recognition and production of Chinese characters, while one group of participants highlighted that they felt challenged working as a team. This may suggest there is a need for training of both technological tools as well as teamwork prior to conducting Bridge 21 learning activities in the future.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".