I ought-to learn a language, but it’s not ideal: Motivation in learners of German
Bibliographic record
Abstract
Abstract \nThis study aids in understanding language learning motivation and its interaction with multilingualism. In light of both rising levels of multilingualism in Canada and falling enrollment in language courses, I identified language learning motivation as a key factor in understanding trends in language learning. I carried out an investigation of the influence of previously learned languages on language learning motivation using Zoltán Dörnyei’s L2 Motivational Self System (L2MSS) as the theoretical foundation. Participants were students enrolled in German language courses at the University of Waterloo, Canada. Using a mixed-methods research (MMR) approach, I combined a quantitative stream using inferential statistics to examine numerical questionnaire data with a qualitative stream including both cluster analysis of questionnaire data and theme analysis of interviews with a sub-sample of participants. This MMR approach deepens understanding of motivation in the participant group. Additionally, it allows for triangulation between methods and data sources, significantly increasing reliability and generalizability of conclusions, which can be used in the development of lesson plans, course curricula, and marketing campaigns.
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 | 0.003 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".