What uses of digital technology support and enhance the learning and the engagement of diverse learners in high school classes?
Bibliographic record
Abstract
Digital technologies may play 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 related to educational technologies, K-12 teachers struggle to harness the full potential of digitalisation to support student learning and engagement, causing further marginalization and exclusion that put diverse group of learners at risk of being left behind in education (Chevalère et al., 2021). In response to these research gaps, the aim of this paper is to explore which high school uses of digital technology cater for learners’ needs and preferences for the sake of a more digitally inclusive environment that supports engagement and equitable learning opportunities for all students (Stenman & Pettersson, 2020). We adopted a qualitative approach to attend to the richness and depth in various contexts. We have conducted individual semi-structured interviews with 17 high school teachers, aiming to obtain in-depth qualitative data through open-ended questions. Then, a general inductive data analysis approach was followed to generate preliminary results on the research objectives, which are presented in this study.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| 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".