The Impact of the Use of YouTube and Facebook on Students’ Academic Achievement in Geography Course at the University of Jordan for the Bachelor's Degree
Bibliographic record
Abstract
The aim of this study is to investigate the impact of the use of YouTube and Facebook on students’ academic achievement in geography course at the University of Jordan for the bachelor's degree, and the effect of the variable of the cumulative average. The study was conducted in the first semester of the academic year 2017/2018. The study consists of two groups: the first was taught by using YouTube and Facebook and the number of its members is (43) students, and the second group which is the control group was (34) students.A quasi-experimental approach was used and the study’s tools were the educational material designed in a manner consistent with the methods of YouTube and Facebook, and a 25-point achievement test to measure the students' achievement in geography course. The validity and reliability of the study tools were verified by known scientific methods.The results showed that there was a statistically significant effect on the achievement of the students of the University of Jordan in the geography course due to the variable of method of teaching and to the two experimental groups that were taught using the methods of YouTube and Facebook. There are too statistically significant differences (α = 0.05) due to the cumulative average and the significance was in favor of those with good, very good and excellent assessments.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".