Effects of Electronic Reading Environments’ Structure on L2 Reading Comprehension
Bibliographic record
Abstract
This study examines the effects of an electronic reading environment’s structure on second language (L2) reading comprehension. In particular, this study explores whether clarifying the underlying structure of an electronic text, along with the ways in which its units or nodes are organized and interrelated results in better comprehension as well as whether L2 reading proficiency affects the comprehension of electronic text. In this study, 40 English as a second language (ESL) learners, grouped into proficient and less-proficient groups based on their scores on the Test of English as a Foreign Language’s (TOEFL’s) Reading section, were asked to read two electronic texts using computerized programs classified as either “well structured” or “less structured.” To assess the efficacy of each type of reading environment, two tests—a multiple-choice test and a mapping of main ideas and details (MOMID) test—were developed and administered to the participants after they read each text. The results of these tests were analyzed using a paired-samples t-test and a two-way (proficiency level by computerized reading program) mixed-model analysis of variance (ANOVA). The findings revealed that well-structured electronic texts can aid ESL readers in developing a more coherent mental representation of the electronic texts’ content, thereby increasing their reading comprehension. Furthermore, well-structured electronic texts are more helpful for less-proficient readers than for more-proficient readers. These findings have significant pedagogical and technological implications for L2 reading instructors and instructional designers.
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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".