Assessing Down Syndrome EFL Learner’s Language Ability: Incorporating Learners-Teachers’ Perspectives
Bibliographic record
Abstract
This study aimed to explore Down syndrome EFL learners and their teachers’ perceptions of language ability assessment and considering their perceptions in order to develop an appropriate test format which can enable them to present their best of language ability. To achieve this purpose, 35 individuals with Down syndrome (both genders), their teachers, and counselors participated in this research. 21 individuals with Down syndrome were at the basic level of second language development, 14 individuals were elementary. It is noteworthy that four instruments were used in this study: Observation, Interview, and Questionnaire and Down Syndrome EFL learners’ language proficiency test. Down syndrome individuals’ English classes were also observed to achieve information on the strengths and weaknesses in developing second language. Then, an interview was conducted among Down syndrome individuals, their teachers, and counselors for the purpose of qualitative data required to make a researchers-made questionnaire in order to elicit their assessment perceptions. The data obtained from the study revealed that the Down Syndrome learners preferred to be tested that most of students with Down Syndrome prefer to be assessed only through some especial test items which including the multiple-choice, matching, true-false, short-answer questions, fill-in-the-blanks tests, conversation with patterns and oral assessments. This study can provide teachers and material developers with the knowledge to develop and provide assessment models to help Down Syndrome EFL learners improve their learning quality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".