The Reality of Using E-learning Strategies to Improving the Learning of Mathematics for Undergraduate Students
Bibliographic record
Abstract
This research aims to know the reality of using e-learning strategy in improving the learning of mathematics in university education, and to know the moral differences between the variables, the researcher used the descriptive and analytical method with the questionnaire tool consisting of (30) paragraphs distributed in the fields of education, learning and evaluation, addressed to a random sample of (110) students and the Mathematics teaching staff of (34) members of Prince Sattam bin Abd Al-Aziz University in Wadi Al-Dawasir at tow Colleges (Arts, Sciences and Engineering) for the first semester of the academic year 2021.After collecting and analyzing data statistically, the researcher came up with the following findings: The most important ones are: the existence of good efficiency in using e-learning strategies in teaching Mathematics , learning and evaluation process, especially in the focus of education, the presence of obstacles in the way of the use of e-learning strategies in learning the university student, especially weak internet, and the absence of statistically significant differences in the efficiency of using strategies of e-learning in learning Mathematics for gender , and for the cumulative academic rate, there is an urgent need for training faculty members and students to improve the use of e-learning strategies in teaching and learning Mathematics, especially when it comes to developing.The research came up with a set of recommendations the most important of which is increasing community awareness to calling for: action to spread the culture of using e-learning in teaching in universities, addressing the obstacles facing the use of e-learning strategies, draw attention to training students and members of Mathematics teaching staff on what is new in the field of educational technologies.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".