A sustainability framework for mobile technology integration in schools: The case of resource-constrained environments in South Africa
Bibliographic record
Abstract
The application of mobile technology integration in schools has been widely researched. However, only a few studies have extensively examined the sustainability of mobile technology integration in resource-constrained environments. Diverse contexts and devices complicate the construction of a consolidated view of how to sustain the pedagogical practice of learning with mobile devices in these environments. The purpose of this article is to indicate how feedback from teachers and district officials informed the development of a sustainability framework for mobile technology integration in schools (SFMTIS), which originated following a literature review. Employing design science research as methodology, a sustainability framework was synthesized from the existing literature. Teachers’ views were obtained regarding the integration of mobile technology in their schools and were subsequently processed to inform the further development of the framework. Teachers who had previously participated in an initiative which introduced mobile tablet use, trained those teachers, and provided information and communication technology infrastructure to their schools, were purposively selected for the study. Department of Basic Education officials based at district offices were also interviewed for their views on sustainable integration. The findings form the basis for the proposed SFMTIS in resource-constrained environments in South Africa. Besides the refined sustainability framework, the research contributes novel insights into the differing perspectives of the teachers and the district officials, and how those can impact the sustainability of mobile technology integration in resource-constrained environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| 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".