The Belief of Mandarin Foreign Language Educators in Differentiated Instruction Based on Student Characteristics
Bibliographic record
Abstract
This study aims to investigate Mandarin university educators' perceptions of implementing Differentiated Instruction (DI) and the role of genders in relation to students' characteristics.The high dropout rates among students learning Mandarin have raised concerns, and this study seeks to address this issue by providing insights into how educators can adjust their curriculum, instructional methods, learning materials, and activities to meet the individual needs of their students.The study uses a purposive sampling technique to recruit 26 Mandarin University educators who completed the survey questions based on the developed instruments.The data analysis was completed using SPSS ver.25, and the normality of the data was confirmed using Shapiro-Wilks.The Mann-Whitney U test was used to ascertain the belief of the educators with respect to gender differences.This study highlights potential gender differences in Mandarin language educators' use of Differentiated Instruction (DI) and suggests that educators should consider students' characteristics in DI implementation.Tailoring teaching practices to individual students' needs can lead to increase engagement and success.It suggests informing teacher training programs to tailor teaching practices to meet the individual needs of students, leading to better student outcomes.Accurate statistical methods, such as nonparametric tests, were used in the study, which contributes to improving Mandarin language education and may inspire future research in other subject areas within educational contexts.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".