Blending Digital Literacy and Pedagogical Innovation: Enhancing Teacher Competence for Transformative ICT-based Curriculum Delivery
Bibliographic record
Abstract
Background: Educators must connect their expertise of digital literacy with instructional advancements to deliver quality educational content through ICT platforms because of rapid digital adoption in education. The successful integration of ICT causes significant obstacles for classrooms because they struggle with inadequate facilities and insufficient training and hesitancy toward adopting modern teaching approaches. Research indicates that teaching methods which integrate digital literacy education produce effective educator competencies which help educators address their institutional and systemic challenges. Aim: The present study examine show blending digital literacy and pedagogical innovation enhances teacher competence. Method: The study utilized survey research techniques to examine 117 secondary STEM teachers operating within Oyo Metropolis of Nigeria. The collected data from the structured questionnaire demonstrated its validity through expert approval before researchers used descriptive statistics and percentage dependent analysis to interpret the results. Results: Study results showed that the majority of teachers (77%) demonstrated digital tool competence yet time limitations affected 81.2% of teachers and technical problems impacted 76.9% of teachers. Among the studied group, 72.6% accepted pedagogical innovation although 81.2% indicated that long preparation schedules were their main hindrance. A large majority of educators (85.5%) presented positive views about blended learning but assessment approaches were identified by 78.6% of teachers as sticking to traditional educational approaches. The research demonstrates that qualified teachers need entire institutional support together with suitable infrastructure and prepared digital learners in order to advance effective digital learning initiatives. Conclusion: Educational stakeholders must collaborate to offer joint educational and technological training to teachers and develop peer-based mentoring systems and assess blending learning approaches against real classroom settings. Transformation needs fundamental system-level modifications for eliminating administrative troubles and distributing resources equally to achieve effective implementation of ICT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".