Development of Foreign Language Lesson Satisfaction Scale (FLSS): Validity and Reliability Study
Bibliographic record
Abstract
In this research it is aimed to develop an instrument that could be used to measure university students' satisfactionwith foreign language lessons in a valid and reliable manner. The research was conducted on three separate studygroups consisting of 460 students in the spring semester of the 2017-2018 academic year. In the research, firstly, anexpert opinion was applied for the content and face validity of the scale prepared in accordance with literature review,student and expert opinions. In the final form, exploratory factor analysis (EFA) and confirmatory factor analysis(CFA) were applied. The EFA yielded a construct that consisted of 28 items and three factors that explained the59.97% of the total variance. These factors were named as follows: the Curriculum, Teaching Staff and PhysicalConditions. CFA was performed in the final application phase and the fit indexes were determined to be acceptable.For the criterion validity, the correlations of the factors with the total points of the scale were calculated and a highlevel of meaningful correlation was observed between these values. Internal consistency and split half test values forthe whole scale show that the scale is highly reliable. Based on these findings, it can be said that this scale is a validand reliable measurement tool for university students to measure their satisfaction with foreign language lessons.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".