Assessing the Impact of Multicultural Curriculum on Student Performance in Beijing High Schools
Bibliographic record
Abstract
Considering increasing diversity and the need for culturally responsive education, this study examines the integration of technology within multicultural curricula in Chinese high schools. This study focuses on the interplay between task characteristics and technological capabilities and their impact on student performance. Grounded in the TaskTechnology Fit theory, the analysis employs structural equation modelling (SEM) to assess the relationships among these variables. This study aims to identify how aligning educational tasks with technological resources can enhance student outcomes in multicultural learning environments. The research design involved collecting data from a purposive sample of teachers and students in Beijing, and the analysis revealed significant relationships among task characteristics (TaC), technology characteristics (TeC), tasktechnology fit (TTF), and student performance (SP). The findings highlight that optimal alignment between educational tasks and technological tools is crucial for enhancing academic performance and fostering deeper engagement with the multicultural aspects of the curriculum. These results emphasise the critical need for strategic selection and integration of technology in educational settings. This study underscores the importance of developing strategies that consider both the pedagogical aspects of the curriculum and the technological tools used for its delivery. This research provides empirical insights into the effective use of technology in multicultural education and offers valuable guidance for educators and policymakers. Furthermore, it plays a role in attaining sustainable development goal 4 and 10. The findings contribute to the ongoing discourse on educational technology and multicultural education, with practical implications for enhancing teaching and learning in diverse 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".