Bibliographic record
Abstract
AbstractThe growing number of ELLs (English language learners) makes the search for new effectiveand efficient instructional methods a priority. While several teaching methods and tools areused to help ELLs succeed in becoming proficient English speakers, technology has gainedsubstantial attention due to the abundance of new technology tools, which are helping us,achieve more in less time and also due to our increasingly connected world. Tablets and appsare changing the nature of English language instruction. The purpose of this study is toinvestigate how technology tools helped ELLs become more proficient in English. Studiesreviews and summaries of research published on the topic of technology tools and Englishlanguage acquisition, specially focusing on the efficiency and effectiveness of technologytools in helping ELLs acquire English language will be scrutinized. Theories of secondlanguage acquisition will be used to better understand how Krashen’s (1982) comprehensiveinput theory delivered using new technology provides learners with comprehensible materialsleading to acquiring faster the language. Overall, from the current review of literature, Iconclude that technology is an effective and efficient tool in helping ELLs become proficientin English. While there are several benefits, of using technology to enhance ELLs learningskills, quantitative data from various studies shows that factors such as costs and trainings areof great importance in assessing how efficient technology tools are. Thus, I will also explorerelevant challenges to using technology for English language instruction.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".