Effectiveness of Employing the E-mind Mapping Strategy in Scientific Courses: Adopting the Blended Learning Approach at Emirati Private Preparatory Schools
Bibliographic record
Abstract
This article explored the effectiveness of employing the e-mind mapping strategy in scientific courses in Emirati private preparatory schools in the light of adopting the blended learning approach. It explored that from the perspective of students. The researchers adopted the descriptive analytical and quantitative approaches. They used a survey that was developed by them based on several studies. Those studies were published in peer-reviewed journals. The forms of the survey were uploaded to the web through using the use of Google Form. The purposive sampling method was employed. For instance, the researchers sent the link of the survey to 400 students in five Emirati private preparatory schools. Two schools of those ones are located in Ajman, one school in Abu Dhabi, one school in Dubai and one school in Sharjah. However, 182 forms were filled. The response rate is 45.5%. The researchers used descriptive statistical methods. They found that the effectiveness of employing the e-mind mapping strategy in scientific courses in Emirati private preparatory schools in the light of adopting the blended learning approach is high. This strategy promotes innovation within students and improves their learning skills. It enables students to carry out self-learning practices. It improves students’ ability to do writing-related tasks. The researchers recommend holding courses for teachers for teaching them the way of creating e-mind maps through using various software.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".