Cultivating Curiosity and Resilience: Exploring Teachers’ Perspectives in Integrating Instructional Innovation for Learners' Competency Development
Bibliographic record
Abstract
This study explores primary school teachers' perspectives on integrating technology-enhanced instructional innovations to cultivate curiosity, resilience, and learner competencies in Nigerian classrooms. Utilising an interpretative phenomenological approach, seven purposively selected teachers from a high school in Port Harcourt participated in semi-structured interviews. The findings reveal that teachers actively employ diverse strategies, such as inquiry-based learning, role-play, and digital tools, to foster cognitive, socio-emotional, and ethical growth. However, systemic barriers, including inadequate resources, infrastructural deficiencies, and limited professional development opportunities, impede the full realisation of instructional innovations. Despite these challenges, teacher agency plays a pivotal role in adapting and personalising practices to meet diverse learner needs. The study underscores the importance of differentiated instruction, collaborative learning environments, and targeted policy interventions to bridge the gap between policy aspirations and classroom realities. Sustainable integration of instructional innovation requires robust professional development, collaborative teacher networks, and infrastructural improvements, transforming classrooms into dynamic spaces that equip learners with the competencies needed for 21st-century success.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".