The use of digital technology to enhance language and literacy skills for Indigenous people: A systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Indigenous people have experienced negative inter-generational impacts of colonization and socioeconomic stress, which has led to persistent subpar academic performance compared to non-Indigenous populations. This has prevented Indigenous people from graduating high school and pursuing post-secondary education and professional opportunities. One of their most critical challenges is obtaining adequate language and literacy skills required for success in school and at work. Thus, by a systematic review of 25 empirical studies, this article examines the evidence for the efficacy of using digital technologies to support Indigenous people's learning of language and literacy skills. This research synthesis provides a profile of the studies’ comprehensive attributes and responds to five research questions that focus on the effects of, and Indigenous people and educators’ perspectives on digital technology use for Indigenous people's learning of language and literacy skills. This article provides insights for teaching practice, and also identifies gaps for future research, instructional designs and implementations that are urgently needed to support Indigenous people, particularly the language and literacy development of Indigenous school children and youth.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it