Creative Approach in Teaching CEFR Reading Comprehension Using Bubble Map and Tree Map Method
Bibliographic record
Abstract
In Malaysian secondary schools, the dynamic tool of teaching CEFR reading comprehension is to give students a greater exposure and increase their motivation in conducive learning environment. The topmost objective of this study is to investigate whether the use of Bubble Map and Tree Map method improves students’ learning of open ended and cloze test questions in the CEFR reading comprehension framework. For this purpose, the researcher employed a quasi-experimental design with a sample of 105 Form One students (13 years old) from three different schools (school A, B and C) from Petaling Jaya, Selangor. Three groups of students were taken as intact groups to fulfill the requirement of this study. All the three groups were comprised with equal participants (35 students in each group). 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. In order to collect the data, the researcher administered pre-test and post-test (instruments for this study). First, the quantitative data was analyzed using MANCOVA test and Tukey HSD (SPSS program for Windows version 26). The findings from the MANCOVA test demonstrated that EG1 (using Bubble Map) significantly outperformed EG2 and CG in answering open ended comprehension questions and the cloze test questions. The results of Tukey HSD also indicated that EG2 (using Tree Map) performed significantly better than CG (using conventional method) in answering the open ended and the close test comprehension questions. This study has crucial pedagogical implication because it revealed that utilizing Bubble Map and Tree Map methods can enhance students CEFR reading comprehension. As such, teachers can use Bubble Map and Tree Map method as an alternative method to teach CEFR reading comprehension in the ESL classroom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".