What Uses of Digital Technology Support and Enhance the Learning and the Engagement of Diverse Learners in High School Classes?
Bibliographic record
Abstract
Modern technology plays an important role in helping students from all walks of life to overcome barriers and benefit from teaching and learning. However, due to a lack of knowledge or skills with regards to educational technologies, K-12 teachers struggle to harness the full potential of digitalisation to support student learning and engagement, causing a risk of marginalisation (Chevalère et al., 2021). The gap in using ICTs is known as the digital divide. Research literature has revealed three levels of digital divide: access, usage and outcomes. In response to these research gaps, the aim of this communication is to explore which high school teaching practices cater for learners’ needs and preferences for the sake of an inclusive use of digital technology that enhances their learning and engagement. As part of a research project funded by the SSHRC, we have conducted individual semi-structured interviews with high school teachers and students aiming to obtain in-depth qualitative data mainly through open-ended questions. A thematic analysis has been employed to inductively analyse the interview data and generate possible themes and categories based on the objectives of our study. Preliminary findings will be available in spring 2023, and will be shared at OTESSA conference
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".