Educational Technology Integration Strategies in Colleges of Teacher Education in Ethiopia
Bibliographic record
Abstract
Research on educational technology (EdTech) integration has extensively explored determinants; however, strategies remain underexamined. Existing models predominantly focus on identifying the determinants of technology adoption yet fail to offer systemic frameworks for sustainable EdTech integration. This study bridges that gap by investigating strategies proposed by stakeholders in a college of teacher education, culminating in a theoretical framework. The research was conducted across four Ethiopian colleges of teacher education by employing a constructivist grounded theory. Data were collected through semi-structured interviews and document analysis, involving 23 participants selected through purposive and theoretical sampling. Data analysis was performed using MAXQDA (Version 2020) software. The results revealed six key strategies categorized into teacher-related, institution-related, and organization-related. A co-constructed theoretical framework illustrates the roles of various stakeholders in EdTech integration, underpinned by ecological systems theory, diffusion of innovations, and the unified theory of acceptance and use of technology. Credibility was ensured through a member-checking survey. The study advocates for further quantitative research to evaluate the correlation between strategies and educational technology integration outcomes, with replication across diverse contexts and stakeholders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".