A Suggested Approach to Overcoming Obstacles of Learning Chemistry for Ninth Grader Students in The Syrian Arab Republic-Idlib Suburb Refugee Camps as Perceived by Teachers and Students
Bibliographic record
Abstract
This study aims to identify the obstacles of learning chemistry for ninth grade students in Syrian Arab Republic-Idlib Suburb Refugee camps and suggest solutions to overcome these obstacles. The study followed an analytical descriptive approach using two questionnaires of (58) items of questions for teachers and (18) for students.The population of the study included (27) teachers of chemistry and (91) ninth grade in schools in students in Idlib Refugee camps between 2016 and 2018. The SPSS program adopted for the analysis of data and the statistical study showed the following results: the obstacles identified by teachers included tools, materials, and structures of laboratories as the most significant, as well as the situation of schools and the education environment in the camps. Students also identified obstacles related to tools, materials, and the structure of laboratories, then difficulties with teachers, availability and content of chemistry textbooks, and finally personal or interpersonal difficulties. The results showed there are no statistically significant differences at the level (0.05) between the estimates of chemistry teachers or students regarding obstacles related to gender. Similarly there were no statistically significant differences at the level (0.05) between the estimates of chemistry teachers regarding the variable number of years of experience or degree of university qualification or variable number of schools in which teachers teach, and statistically significant difference at the level (0.01) due to variable number of schools in which teachers teach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".