Lasting Impact of Study Abroad Experiences: A Collaborative Autoethnography
Bibliographic record
Abstract
Researchers in the field of study abroad have focused on language, identity construction, and motivation, yet few studies have shown its lasting impact on participants. This article contains the reflections of two individuals who took part in studies abroad and remain engaged in multicultural education and in the instruction and research of second language acquisition. We review the literature in the area of study abroad, then discuss the suitability of using a collaborative autoethnography (CAE) approach, defined as "the study of self collectively" (CHANG, WAMBURA NGUNJIRI & HERNANDEZ, 2013, p.11) for this project. We analyzed our data, which are in the form of reflective narratives and archived e-mails, through open coding, based on grounded theory methodology (see CORBIN & STRAUSS, 2015). Four major themes surfaced from our data analysis: language and culture; academics; identity; and lasting impact. Finally, we compare our experiences, identify some of the lasting effects of our time abroad, and consider both the practical and theoretical implications of the research. This research has been useful for us to understand CAE and the lasting effects of study abroad experiences on students who become language teachers. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1602238
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".