The Influence of Flipped Learning on Geographic Concept Acquisition among Al-Balqa' Applied University Undergraduate Students in the Environment and Society Course
Bibliographic record
Abstract
This study aimed to determine the influence of flipped classroom techniques on the acquisition of geographical concepts within an undergraduate Environment and Society course. Using an experimental design, 50 students at Al-Balqa Applied University were randomly divided into a flipped learning intervention group and a traditional learning control group. Quantitative pre- and post-achievement tests and a questionnaire gauged changes in academic performance and perceptions. The flipped approach incorporated pre-class educational videos, homework tasks, and active in-class learning. Analyses revealed the experimental group attained statistically significantly higher post-test scores versus the control, with a large effect size demonstrating 41.9% of the variance in achievement explained by flipped methods. Triangulation from the questionnaire further showed strong agreement that flipped learning enhanced motivation and self-directed education. While supporting earlier research that flipping classrooms has positive effects across all subjects, this study adds to our knowledge of how effective it is in helping Jordanian undergraduates master human geographical concepts. Recommendations include broader implementation of flipped techniques aligned to course objectives, faculty training in instructional technologies, enhanced university digital infrastructure, and additional research tracking long-term knowledge retention. Overall, carefully gathered data showed significant gains in both the quantity and quality of learning, supporting ideas about the benefits of incorporating flipped learning into higher education with care. According to the main findings, the critical recommendation is to implement flipped learning in the Environment and Society course at Al-Balqa Applied University, involving a task force to design a tailored framework incorporating pre-recorded lectures and online materials via the LMS.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".