The Effect of Computerized Educational Software on the Achievement of the Third Grade Students in Learning Arabic in Jordan
Bibliographic record
Abstract
This study aimed to reveal the impact of computerized educational software on the achievement of third grade students in learning Arabic language in Jordan. To achieve the objective of this study, the researcher built a computerized educational software, and an achievement test that measures reading and writing skills of third grade students. The validity and reliability of the study tools have been verified. The study members consisted of (50) male and female students of the third grade students in the first semester 2018/2019, were distributed into two groups, one of which is an experimental group of (25) male and the other officer is composed of (25) male and female students. He divided the sample into two groups: a control officer studied in the usual way, and an experimental study using computerized educational software. The results showed that there were statistically significant differences at the level of significance (α = 0.05) in the achievement of the third grade students in the Arabic language (reading and writing) in favor of teaching method using computerized educational software. The study recommended: generalizing the experience of the use of computerized educational software that was applied to the students of Arabic language on different subjects, taking advantage of the positive impact of the use of computerized educational software in the achievement of students, conducting new studies with different designs and measurement tools to examine the impact of the use of computerized educational software in materials. A variety of different levels of study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".