TECHNOLOGY, INCLUSIVITY AND CULTURALLY RELEVANT PEDAGOGY: HOW EDUCATORS USE MOBILE APPLICATIONS IN SOCIO-CULTURALLY DIVERSE CLASSROOMS
Bibliographic record
Abstract
Today’s unprecedented growth of the mobile applications industry and its accessibility for educators and students presents exceptional challenges and benefits to the education system. This study is an account of how seven K-12 educators from Canada, United States, Saudi Arabia and Thailand evaluate, select and use mobile apps in their classrooms, taking into account relevant criteria including: cultural, social, and developmental/age appropriateness of each app. It also addresses a gap in the literature around how and why K-12 educators are using mobile apps. Lastly, the study highlights the perceived benefits and challenges faced by educators in their efforts to ensure digital apps are relevant and inclusive for diverse student needs.This study is an account of how seven K-12 educators from Canada, United States, Saudi Arabia and Thailand evaluate, select and use mobile apps in their classrooms. The study looks at how these educators take into consideration relevant criteria including: cultural, social, and developmental/age appropriateness of each app. The study also reports on some of the benefits, challenges and areas for further research, contributing to ways in which educators can continue to be critically conscious with regard to the use of new and emerging digital technologies in diverse classrooms. Drawing on Ertmer’s (1999, 2012) and Tsai’s and Chai’s (2012) technological integration frameworks, this study examines the use of mobile apps in physical education (PE) classrooms in particular. Key questions that lead the study were: how are physical educators using mobile apps in their practice? What are the cultural, social, and developmental considerations physical educators take into account when using technology in their classrooms?This is a mixed-methods study driven by the motivation to better understand how educators select and evaluate apps based on their pedagogical considerations for maintaining inclusive classrooms, ensuring representation of all students, as well as the correct age and developmental range of tools for the PE classroom. Participants for the study included seven male and female K-12 educators in either publicly funded or private schools across the United States, Canada, Saudi Arabia and Thailand. Criterion sampling (Patton, 2001) was used to select participants that met three predetermined criterion of importance. The criteria were: educators’ current use of mobile applications in pedagogy and practice; demonstration of leadership in technology integration in PE; certification and current practice as an elementary teacher with no less than five years of experience. All participants had access to WiFi in their schools and used either tablets or iPads in their classes.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".