Bibliographic record
Abstract
Scores of studies have established that when learning online, students must be equipped with different sets of strategies and skills than in a physical classroom setting (Anderson, 2003; Broadbent & Poon, 2015; Coiro, 2007; Leu et al., 2007; Michinov, Brunot, Le Bohec, Juhel, & Delaval, 2011; Salmon, 2013). The present study, by virtue of exploring foreign language learners’ online reading experience, aimed to identify the reading strategies that learners would use when engaged in online reading activities in the target foreign languages. Thirty-two foreign language learners whose native language was English participated in the study. The Online Survey of Reading Strategies (OSORS) designed by Anderson (2003) was administered to investigate the following four research questions: (1) What are the strategies that language learners would or would not use when reading online in foreign languages? (2) Would foreign language learners use some of the online reading strategies more frequently than other strategies? (3) Would different levels of foreign language proficiencies influence language learners’ use of the strategies? (4) What could foreign language teachers do in their instruction to help students acquire and broaden their repertoire of online reading strategies? Data analysis demonstrated the most and least frequently used strategies of the foreign language learners and uncovered a significant difference in the frequency of use among the strategies. However, there was no significant difference found between the use of online reading strategies and learners’ foreign language proficiencies. Implications and suggestions for future research and practice were proposed accordingly.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".