The Impact of a Digital Educational Story Based on Sports on Developing Alphabetical Learning Skills among Jordanian Pre-School Kids in Light of the Covid-19 Pandemic
Bibliographic record
Abstract
This study aimed to identify the impact of a digital educational story based on sports on developing alphabetical learning skills among Jordanian pre-school kids in light of the Covid-19 pandemic. The study sample consisted of (29) pre-school kids from the leaders' international schools, "The International Curriculum," which is affiliated to Al-Qweismeh District's Directorate in Amman. The study used an Experimental approach; the study sample chosen by intention randomly was divided into two groups: experimental group (14) kids: (7) girls and (7) boys, and control group (15) kids: (7) are boys, and (8) are girls. To achieve the study's goal, the researchers built a guide for the teacher to implement the experiment; they also built a pre-and post-test consisting of (16) items and provided the Arabic language teacher with digital educational stories based on sport learning the alphabet. The stories include the alphabet lessons: (letter al-ba, letter al-da, letter al-ha, letter al-ain), from the "curriculum in the Arabic language” for the academic year 2020/2021. The results of the study showed that there were statistically significant differences in favor of students of the experimental group, which studied the digital educational story in each Alphabet learning skills: Pronunciation, Abstraction, and writing, in the combined skills, and the absence of statistically significant differences attributed to gender, interaction in testing the skills of learning the alphabet together, and in each of the skills of learning the alphabets: (pronunciation, abstraction, writing). In light of the findings mentioned above, the researchers proposed a set of recommendations that include Arabic language curricula guides in digital educational programs based on sports to be used in learning and teaching processes in teaching Arabic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".