Exploring English as Second Language (ESL) Learners' Reading Experiences: Attitudes and Challenges with Implementation of Graphic Organizer Instruction (GOI)
Bibliographic record
Abstract
Despite the increasing recognition of Graphic Organizers for Instruction (GOI) as a potentially effective tool for enhancing reading comprehension among ESL readers, there remains a significant gap in understanding how learners perceive and engage with this approach. The primary objective of this study is to investigate the reading experiences and attitudes of ESL learners towards the utilization of GOI in their reading practices. By doing so, this research aims to expand our current understanding of the perspectives of ESL learners regarding the implementation of GOI and the obstacles they may encounter. Ultimately, this study seeks to enrich the existing literature by offering valuable insights into the reading experiences and challenges associated with the use of GOI in ESL reading instruction. To achieve the research objectives, this research employed focus group interviews with 12 purposively selected participants and three sessions of classroom observations. Key findings indicated that GOI positively influenced reading engagement and comprehension among ESL learners, fostering interaction and enhancing their understanding of instructional materials. The study's conclusions highlight the potential benefits of GOI in ESL reading instruction and suggest implications for pedagogical practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".