Youth English Language Learners’ Learning Outcomes and Experiences of Digital Technology-Based Writing Instruction: A Literature Review of Key Empirical Evidence
Bibliographic record
Abstract
A growing body of research has revealed that the use of digital technology, including digital media, for writing instruction positively impacts English language learners’(ELLs’) learning and engagement; however, little is known about how this instruction impacts the development of ELLs’ writing skills. This scoping literature review is comprised of empirical evidence from 32 studies published between 2010 and 2020 that reported on the impact of digital technology-supported writing instruction on youth ELL’s writing skills. Although the types of digital technology media that were used varied across the studies, the results revealed that all 32 studies found a positive or perceived positive impact of digital technology-supported writing instruction on ELLs’ writing skills in areas of grammar, language mechanics, metalinguistic awareness, organization, sentence/paragraph structure, and/or word choice/language use. Specifically, 29 articles reported positive outcomes or perceived positive outcomes, while three showed mixed results in which certain areas improved but not others. Pedagogical implications, and recommendations are provided to language educators and youth ELLs.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".