Causes of 8th Grade Students Low Achievement in TIMSS Study-2015 from Science Teachers and Educational Supervisors
Bibliographic record
Abstract
The aim of the study was to examine causes of low achievement among Jordanian 8th grade students in TIMSS study- 2015 from science teachers and educational supervisor. Another objective of the study was to examine the role of position (teacher, supervisor), gender, experience, educational directorate on their perceptions. To achieve this aim, a (51) items questionnaire was administrated to the science male and female teacher whose schools have reported under average level in TIMSS study- 2015; totaling (100) teachers and (114) educational supervisors. The results of the study showed that causes of lower achievement among Jordanian 8th grade students were at high levels from science teachers and educational supervisors perceptions as school administration related causes ranked first. The results found no statistically significant differences due to gender in causes for low achievement in TIMSS, while differences were found in school administration related causes due to educational directorate, in favor of middle region, due to experience, in favor of (6-10) years of experience. In light of results, the study recommended the need for organizing conferences with ministry of education officials and science teachers to discuss the how to overcome the causes behind low achievement level in TIMSS study. Finally, future research is needed to compare the results reported in high achievement countries in TIMSS study with the ones reported in Jordan.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".