The Effectiveness of Teaching an Educational Unit According to the Learning Cycle-5 Es Model "Bybee" in the Development of Problem-Solving Skills and Academic Achievement in Geography Subject in Grade 9 in Jordan
Bibliographic record
Abstract
The aim of this study is to investigate the effectiveness of the learning cycle-5 Es model “Bybee ”in the developmentof problem-solving skills and academic achievement in Geography subject in the ninth graders in Jordan. The studyconsists of 60 students from the University Education Directorate in Amman in the second semester for the year2017/ 2018. The sample was distributed randomly into two groups; one of which is experimental of (30 students) andwas taught according to the Bybee model. The other was a control group, which consisted of (30) students and wastaught in the usual way. The study tools consisted of the cognitive achievement test and the capacity scale forproblem-solving.The results of the study showed that there were statistically significant differences in the average achievement of theninth-grade students in geography subject, due to the impact of the teaching strategy in favor of the experimentalgroup. Also, it showed statistically significant differences in the average achievement of ninth grade students ingeography, due to the teaching strategy of problem-solving skills and in favor of the experimental group.The researcher recommended the benefit of the learning model "Bybee" in teaching the geography subject at allstages of the upper stage.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".