Post-Encounter Motivation During Study Abroad
Bibliographic record
Abstract
Norton (2010) called for language teachers to recognize and consider a connection between a learner’s motivation to learn a language and their changing identity. In the present study, we examined Japanese learners during study abroad (SA) in Canada and looked at the impact of intercultural contact on their motivation (Aubrey & Nowlan, 2013; Clément, 1980). More specifically, we identify post-encounter motivation (PEM) as return on the investment that students make during SA and explore the ways it enhances their desire to become active participants in target language contexts. Using focus groups and questionnaires, we collected data from 2 groups (N = 13) over the course of 2 academic years and analyzed the data using qualitative content analysis. The results demonstrate the importance of PEM, reveal its salient and observable features, and offer grounds for educators to consider PEM when preparing students for SA. Norton（2010）は、学生たちが言語を学びたいと思う気持ちと彼らのアイデンティティーの変化とのつながりについて考え、認識するよう、言語教師たちに提案した。本研究では、カナダに留学中の日本人学生たちの異文化体験が彼らのやる気に与える影響について吟味した（Aubrey & Nowlan, 2013; Clément, 1980）。更には、留学中の学生たちの努力と熱心さに対する見返りとして post-encounter motivation（PEM）という概念を提示し、これが留学先社会の一員として活躍したいという彼らの気持ちを高める過程を明らかにした。フォーカス・グループ・インタビューとアンケート調査によって、2つの集団 (n = 13)から2年にわたってデータを集め、それを質的内容分析の手法で分析した。分析の結果、PEMの重要性と特徴が明らかにされた。また、留学に向けて学生たちを指導する際、PEMについて考えることの大切さが示された。
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".