The Effectiveness of a Digital Literacy-Integrated Syllabus for Arabic-speaking Courses in Teacher Education Universities in China
Bibliographic record
Abstract
As digital literacy becomes crucial in modern education, its integration into language education is necessary to prepare preservice teachers to meet 21st-century challenges. This study evaluates the impact of a digital literacy-integrated syllabus for Arabic-speaking courses on the digital literacy and Arabic language acquisition of Chinese preservice teachers in China. This paper presents a Phase 3 evaluation of the Design and Developmental Research (DDR) approach. A one-group pretest and posttest design was employed, with 32 Chinese preservice teachers pursuing Arabic language education at three teacher education universities in Yunnan, Ningxia, and Gansu provinces in China. The participants engaged in 2-week online lessons based on a developed syllabus. This syllabus, designed during Phase 2 design and development of DDR, explicitly included digital literacy objectives and teacher-student interaction strategies and learning activities. It was adapted from Yunnan Normal University's syllabus for an Arabic-speaking course. Qualitative data were collected through individual online interviews with five randomly selected participants after completing the 2-week lessons. Quantitative results showed significant improvements in digital literacy across technical, cognitive, attitudinal, and social-emotional domains. Additionally, the findings indicated that the syllabus enhanced preservice teachers' engagement and interest in learning to speak and understand Arabic. However, addressing challenges in the implementation is important to maximize its benefits. These findings contribute to the growing field of technology-enhanced Arabic language education for preservice teachers in China.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".