RESOLVING ARABIC-LANGUAGE TEXT READING ERRORS AMONG UNIVERSITY STUDENTS THROUGH PROJECT-BASED LEARNING (PBL)
Bibliographic record
Abstract
Foreign language learning is certainly accentuating on the mastery of basic skills including reading skills that need to be emphasized by every student in the early stages. Similarly, the same goes for Arabic language learning which is the third language or also recognized as a foreign language in the Malaysian community. One of the skills focused on this study is the focus on reading Arabic texts. The objective of this study is to identify the common reading mistakes that students often make while reading Arabic texts and to determine how well the Project-Based Learning (PBL) method can overcome and reduce their reading errors. This study utilizes a qualitative approach with the method of observation performed on students when they were reading texts during pre and post-reading tests. The reduction of these mistakes was identified from before and after the PBL procedure was implemented. The study sample consisted of 69 students who registered for the 3rd level Arabic course in the 2019/2020 study session at Universiti Malaysia Sabah Labuan International Campus (UMS-LIC). The instrument used was a set of pre and post-reading tests based on the KSSR (Primary School Curriculum Standards) guidelines set out by the MOE (Ministry of Education, 2015).
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".