Seeing innovation from different prisms: university students’ and instructors’ perspectives on flipping the Spanish language classroom
Bibliographic record
Abstract
Abstract This article focuses on the implementation of a flipped classroom approach in two different levels of Spanish foreign By foreign language (FL), the authors mean modern language (ML), as strongly suggested by the European Commission. language university courses for beginner and intermediate learners. The flipped classroom approach delivers course content that prioritizes both digital technology and active learning. Despite its potential advantages in the language classroom, empirical research in this area remains limited. The present study addresses the gap by investigating the effects of the flipped classroom approach on Spanish as a foreign language by comparing student attitudes in flipped and traditional classrooms in beginner and intermediate Spanish courses at a university level. Specifically, this research explores the use of the flipped classroom approach in a second language classroom as a way to present grammar content prior to the in-class lesson and compares the results to those of a traditional, lecture-like delivery of the same grammar content. Drawing on data elicited from students and instructors in these course levels, this study investigates student and instructor perceptions, as well as student autonomy, engagement, and achievement through a qualitative lens.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".