The Role of Technology Integration in the Development of 21st Century Skills and Competencies in Life Sciences Teaching and Learning
Bibliographic record
Abstract
Development of 21st century skills and competencies in teaching and learning remains a key strategic imperative. Coherent development of skills and competencies requires adoption of innovative pedagogical strategies. Technology integration can be harnessed to foster effective teaching and learning. The study examined the role of technology integration in the development of 21st century skills and competencies in Life Sciences teaching and learning. The empirical investigation adopted an explanatory sequential mixed method design and involved 15 purposively selected teachers from five South African suburban schools. The study is underpinned by social constructivism as the underlying theoretical framework. Quantitative data was collected through the administration of a survey questionnaire with the participants while qualitative data was collected through semi-structured interviews. Technology integration was perceived to promote the acquisition of 21st century skills and competencies in Life Sciences teaching and learning. In particular, the teachers indicated that technology integration facilitates the development of skills such as communication, critical thinking, collaboration, problem solving and computational thinking. In addition, technology integration was largely perceived to create exciting teaching and learning environment which fosters the enhancement of academic achievement and motivation of learners. Theoretical implications for technology-enhanced teaching and learning are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".