The Effects of Bubble Map and Tree Map Method in CEFR Reading Comprehension
Bibliographic record
Abstract
The effective method of teaching CEFR reading comprehension makes students to have better exposure and gain confidence in Malaysian secondary schools. The ultimate goal of this study is to investigate whether the use of Bubble Map and Tree Map methods enhances students’ learning of Multiple-Choice Questions (MCQ) in the CEFR reading comprehension. This quantitative study employed a quasi-experimental design. The sample of this study consisted of 105 Form One students (13 years old) from three different schools (school A, B and C) from Petaling Jaya, Selangor. The sample were chosen as intact groups. The Experimental Group 1 (EG1) from school A was taught using Bubble Map, Experimental Group 2 (EG2) from school B was taught using Tree Map and the Control Group (CG) from school C was taught using conventional method. The pre-test and post-test were used as instruments to collect the data for this study. The quantitative data was analyzed using SPSS program for Windows version 26. The MANCOVA test and Tukey HSD were used to analyze the data. The findings demonstrated that EG1 (using Bubble Map) significantly outperformed EG2 and CG in their CEFR reading comprehension. The results also indicated that EG2 (using Tree Map) performed significantly better than CG (using conventional method). This study has essential pedagogical implication because the Bubble Map and Tree Map methods had positive effects in enhancing students’ CEFR reading comprehension. As such, teachers can use Bubble Map and Tree Map methods as an alternative method to teach CEFR reading comprehension in the ESL classroom.
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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 | 0.000 | 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".