The Feasibility of an Innovative Gamified Flipped Classroom Application for University Students in EFL Context: An Account of Autonomous Learning
Bibliographic record
Abstract
Aligned with the progress of technology and the availability of online resources, there is a growing inclination to incorporate game elements or gamification into educational settings, particularly in English language classrooms. This mixed methods research endeavors to examine the potential of the Gamified Flipped Classroom Application (GFCA) named “Classcraft” to enhance student’s learning ability, motivation, and autonomy. Questionnaires were employed to explore students’ attitudes towards the utilization of GFCA as an innovative learning tool within the research context. Furthermore, semi-structured interviews were conducted to gain deeper insights into students' perspectives regarding the use of Classcraft in augmenting their learning motivation and autonomy. The study was carried out with a cohort of 31 Thai EFL students enrolled in English for import and export courses at a public university in northeastern Thailand. The findings revealed that the student’s learning ability exhibited improvement, as evidenced by a higher mean score in the posttest compared to the pretest, subsequent to the implementation of the gamified flipped classroom application in the course. Additionally, a majority of the students expressed a favorable inclination towards the application due to its effectiveness in enhancing their learning motivation, and autonomy and providing an enjoyable and engaging learning experience.
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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 | 0.004 | 0.007 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".