The Impact of E-mind Mapping Strategy on the Academic Achievement of Jordanian 9th Grade Students in Citizenship and Civic Education Course
Bibliographic record
Abstract
The present study aimed at identifying the impact of the e-mind mapping strategy on the academic achievement of Jordanian 9th grade students in the citizenship and civic education course. It was carried out during the second semester of the academic year 2019/2018 through adopting a quasi-experimental approach. It was carried out in Princess Sukayna bent Al-Hussain School for girls in Amman, Jordan. The sample consists from (55) female students who were chosen through the purposive sampling technique. Those students were divided into control and experimental groups. Pre-test and post-tests for measuring achievement were used. Based on the process of analyzing data, the e-mind mapping strategy can effectively improve the achievement of Jordanian 9th grade students in the citizenship and civic education course. The researcher recommends providing Jordanian citizenship and civic education teachers with special training programs about the technology-based instructional strategies. Such training programs must shed a light on e-mind mapping strategy
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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 | 0.000 |
| 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 | 0.001 |
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".