College Students' Motivation When Acquiring a Language Other Than English
Bibliographic record
Abstract
Foreign languages have been studied since the beginning of civilization; in the past few decades, there has been increased interest in understanding the role of motivation in learning a foreign language. Less than a quarter of studies on learning languages other than English (LOTE) have studied motivation. The purpose of this quantitative causal-comparative study was to determine if there was a difference in motivation between college students enrolled in French 100, French 200, French 300, and French 400. The study consisted of 79 college students at the freshman to senior level enrolled in French at a novice to advanced level at each university. The instrument selected to measure participants’ motivation was the Hybrid Questionnaire based on the Attitude/Motivation Test Battery and the L2 Motivation Self-System. Data were collected during French courses for face-to-face classes or at any location for online students and gathered electronically through Qualtrics. The one-way ANOVA statistical method by IBM SPSS 25, a statistics software, was used to analyze the data. The results showed no statistical difference in motivation between college students enrolled in French 100, French 200, French 300, and French 400. To augment this study, future researchers should investigate if there is a difference in motivation between college students enrolled in online and face-to-face French courses. Future researchers should also continue to augment research in the field of LOTE.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".