Flipped Classroom: The Effectiveness of Using Pre-Lecture Assignments on Enhancing EFL Undergraduates’ Attitude, Ability, Engagement and Participation
Bibliographic record
Abstract
Having students as the core pillar of the teaching and learning process is significantly crucial in today’s classrooms. Flipped classroom along with the integration of pre-lecture assignments have proved to transform traditional teaching methods into a more engaging and effective one which stresses leaners’ role to be the centre of the teaching and learning process and increases learning gains. So, this article explores the effectiveness of implementing pre-lecture assignments on enhancing EFL undergraduates’ attitudes, ability, engagement and participation. The researcher adopts a descriptive analytical mixed approach. A questionnaire and interview are employed as data collection tools. The population of this study is level three EFL undergraduates majoring in English language; Faculty of Alsun, International University of Africa. Using a random sampling technique, the researcher administers the tools to the whole class (58 students) in which 48 students take part as sample of the study. The data is analysed using SPSS version 29. The results reveal that the pre-lecture assignments have significantly enhanced the students’ learning ability and helped them to gain a good background about the upcoming content. It also demonstrates that these assignments have increased their engagement and participation during class discussions and that class time is devoted to discussion rather than presentation. In addition, the findings show that the assignments have enhanced their attitudes towards the course. Finally, it’s recommended that EFL instructors should implement pre-lecture assignments in their classes to get more learner-centred classes and equip their students with the required terminology before coming to the class.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".