Morphological Derivations: Learning Difficulties Encountered by Public Secondary School Students in Amman/Jordan
Bibliographic record
Abstract
This study aims at investigating the difficulties encountered by public school students in Amman/ Jordan. The study raises the following questions: What are the obstacles that students may encounter in relation to the derivations? What are the causes of such obstacles? To achieve the aims of the study, the researchers manipulate two methods: A quantitative approach in which students of public secondary schools are tested and pre-tested in order to fulfil the reliability and validity of the results and a qualitative approach using interviews with teachers at the same secondary schools and one supervisor in Amman Third Educational Directorate (AL-Qwesmeh). The main results the study reaches can be summed up as follows: students are so poor not only in derivations and derivational suffixes but also in other linguistic topics. In other words, the problem of committing mistakes in derivational suffixes can obviously be regarded as being accumulative problem resulting from other problems which students are encountering in relation to, for example, parts of speech, word order or sentence patterns. Derivations and derivational suffixes should be taught in early stages such as the 8th grade or 9th grade rather than in the last ones like 11th grade or 12th grade.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".