“The effectiveness of a program based on educational games in developing the efficiency of cognitive representation of information and academic achievement motivation among students with academic learning difficulties”
Bibliographic record
Abstract
The current research aims to identify the effectiveness of aprogram based on educational games for developing the efficiency of cognitive representation of information and the motivation for academic achievement among students with difficulties in academic learning. The sample consisted of (8) students were from the third and fourth grades of primary school who attended the Canadian International School - Erbil for the academic year (2021-2021). The researchers used the semi-experimental approach, and to achieve the hypotheses of the research, the researchers built a program based of (16) sessions, The researchers also adopted the scale (Ghanim, 2011) to measure the efficiency of cognitive representation of information, and the scale (Rabaya, 2018) to measure the motivation of academic achievement after extracting their validity and reliability. The data was analyzed and processed statistically using the Statistical Package for Social Sciences (SPSS). The research found: There are statistically significant differences at the level of significance (0.05) between the score ranks of the experimental group members in the efficiency of cognitive representation of information and the motivation of academic achievement between the pre and posttest and in favor of the post test. And the effectiveness of the program based on educational games.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".