Undergraduates Attitudes towards Adopting a Flipped Learning Approach in Jordanian Universities: Empirical Study
Bibliographic record
Abstract
The present study investigated the undergraduates’ attitudes towards adopting a flipped learning approach in Jordanian universities. An analytical descriptive approach was adopted. The purposive sampling technique was used to choose a sample. This sample consists from 392 BA students. The researchers selected those students from the faculty of education at the University of Jordan. The forms of the questionnaire were distributed by hand. They were retrieved, but 4 ones have been excluded. The final sample consists from 388 BA students. The researchers concluded that the students show positive attitudes towards adopting this learning approach. They found that the flipped learning approach enables students to learn at their own pace and motivates them to learn. It was found that the flipped learning approach facilitates the process of taking notes and improves the students’ comprehension of information. The researchers recommend enacting policies for encouraging faculty members at Jordanian universities to adopt a flipped instructional approach. They recommend using social media by faculty members at Jordanian universities when adopting a flipped instructional approach. That shall motivate students to learn.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".