An Exploration of Impact Factors Influencing Students’ Reading Literacy in Singapore with Machine Learning Approaches
Bibliographic record
Abstract
This study identified the contextual factors which differentiated 15-year-old students with high- and low-achieving reading literacy in Singapore based on Program for International Student Assessment (PISA) 2015. 4,015 students from Singapore were collected from the public dataset of PISA 2015, with 2,646 high-achieving students and 1,369 low-achieving students in PISA reading literacy test. The impact of the overall 49 contextual factors on reading literacy was analyzed in three levels: student level, family level and school level. Support vector machine (SVM), a machine learning approach, was applied to analyze these contextual features. It indicated that SVM could effectively distinguish these two cohorts of readers with an accuracy score of 0.78. SVM-based recursive feature elimination (SVM-RFE), another machine learning approach, was then applied to rank these selected features. These features were outputted in descending order with regard to the degree of their significance to the differentiation. At last, an optimal set with 15 contextual factors was selected by RFE-CV (cross validation), which collectively affected the differentiation of students with high- and low-level of reading literacy. Based on the analysis, implications to further improving students’ reading literacy can be achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".