Students Attitudes toward Blended Learning among students of the University of Jordan
Bibliographic record
Abstract
Information and communication Technology and the use of internet has added many values to the educational process. Many approaches has been developed for this purpose including e-learning, distant learning and recently blended learning. Many universities are adopting these approaches for teaching students. Therefore this research aims to examine attitudes of University of Jordan students toward blended learning. A sample of 250 students were selected to complete online questionnaire who have taken blended coursest hat contains aspects of knowledge about and attitude toward blended learning and a high level of preferring toward blended learning. The results of the study indicated that blended learning is useful to Students, and that most students fully understand the goals of e-learning through blended learning. In general the students have shown positive attitude towered blended learning. Furthermore, they have shown adequate information about the field of blended learning and to decide when and how to use the resources provided to them through blended learning. it also indicated that blended learning is more effective than traditional way of teaching to develop and improve the knowledge and skills. The results also indicated that electronic resources provide through blended learning is an effective way that help students to get knowledge and improve their skills, and it has tremendous effect on students life to take responsibility for their own learning process.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".