Chunking, Elaborating, and Mapping Strategies in Teaching Reading Comprehension Using Content Area Materials
Bibliographic record
Abstract
This paper reports on an experimental study of the application of chunking, elaborating, and mapping strategies inteaching reading comprehension using content area reading materials. The research method employed apretest-posttest control group design. The purpose was intended to answer the research problem related to the effectof the treatment on the students’ English reading achievement. The hypothesis proposed was that there was nodifference in reading achievement scores of the two groups before and after the treatment. The subjects of theresearch were the first year students at the Economics Faculty, Bandung Islamic University, Indonesia. The researchinstruments were reading comprehension tests covering micro processes, integrative processes, macro processes, andelaborative processes. The data obtained through pretest and posttest, were statistically analyzed using t-test. Thestudy showed that the treatment had a significant effect on the students’ reading achievement. In addition, thestudents in both groups were asked to fill in questionnaires to identify their perception on the trained readingstrategies and teaching materials. The study indicated that their perception was mostly positive. In brief, this studysuggests that chunking, elaborating, mapping, and summarizing strategies facilitate students’ reading comprehensionin expository texts in the Indonesian context. However, further research utilizing different reading strategies shouldbe conducted to explore other outcomes that might be more effective in EAP classrooms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".