The Effectiveness of Electronic Mind Maps in Developing Academic Achievement and the Attitude towards Learning English among Primary School Students
Bibliographic record
Abstract
The present study aimed to identify the effect of using electronic Mind Maps on the academic achievement of the fifth-grade primary female students in the English language curriculum compared to the traditional teaching method adopted in the teacher’s guide. It also aimed to indicate the attitudes of the fifth-grade female students towards the use of electronic Mind Maps in understanding the study unit adopted in this study. The study utilized the quasi-experimental method applied to two groups: experimental and control. The population of the study consisted of the fifth-grade of primary school female students, who studied in Ashbeelya Private School in Riyadh for the academic year 2016/2017, and the participants’ ages ranged from 10 to 12 years. The sample of the study is consisted of 30 fifth-grade female students, divided into experimental group (15 students) and control group (15 students). The study resulted that there were statistically significant differences between the mean scores of the experimental group and the control one in the post achievement test scale in favor of the experimental group. The effect size of using Mind Maps was high. There were statistically significant differences between the mean scores of the experimental and control group scores in the post achievement test of the attitude towards learning English in favor of the experimental group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".