Analysis of Learning Achievement and Teacher–Student Interactions in Flipped and Conventional Classrooms
Bibliographic record
Abstract
This study aimed to investigate the effectiveness of two different teaching methods on learning effectiveness. OpenCourseWare was integrated into the flipped classroom model (experimental group) and distance learning (control group). Learning effectiveness encompassed learning achievement, teacher-student interactions, and learning satisfaction. The experimental method was supplemented with qualitative interviews. Overall, 181 freshmen taking a course on physics were allowed to choose their own class based on their preferred teaching method (experimental or control group). The findings indicated that learners in the experimental group scored higher for learning achievement. When selecting a teaching method, if sufficient resources are available, it is suggested that teachers provide learners with the combination of OCW and flipped classroom. Although there was no significant between-group difference in terms of teacher-student interactions and learning satisfaction, the interactions in the flipped classroom had positive effect on students’ learning achievement. The use of the flipped classroom model allows for adequate teacher-student interactions, as teachers can provide guidance and assistance to students in person, while there are greater opportunities for collaborative learning among learners. In addition, since the flipped classroom model emphasizes the process of learning rather than its outcomes, information technology tools should be used to keep detailed records and follow the learning process in order to assess various aspects of the learners’ growth. The results of this study can serve as a reference for future studies on the flipped classroom model and OpenCourseWare, as well as for teachers and researchers in related fields.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".