Peel District Elementary Teachers using "Bring Your Own Device" to Enhance Digital Literacy and Student-Centered Learning
Bibliographic record
Abstract
The use of technology in education is not recent and has been implemented in classrooms to support student learning over time. Furthermore, its value has been well documented in the literature. With the power to revolutionize technology as new advancements are made, recent trends in educational technology such as Bring Your Own Device are made possible. Bring Your Own Device allows students to bring in personally owned devices into the classroom to support learning objectives. The purpose of this qualitative research study is to describe Peel District elementary teachers’ perceptions on whether or how, consistent use of the BYOD program in teaching digital literacy creates meaningful learning experiences for students. A comprehensive literature review on the topic of technological integration was completed followed by three semi-structured interviews with experienced educators employing the program. Data analysis yielded common themes among the participants which include the importance of students being digitally literate, the learning environment conducive to BYOD, student interest as a guide for instruction, and the supports and challenges associated with implementing the program. These themes and the implications of the study will be discussed in greater detail.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".