Theories informing technology enhanced learning in nursing and midwifery education: A systematic review and typological classification
Bibliographic record
Abstract
BACKGROUND: Learning is a complex process involving internal cognitive processes and external stimuli from curricula, pedagogical strategies, and the learning environment. Theories are used extensively in higher education to understand the intricacies of adult learning and improve student outcomes. Nursing and midwifery education uses a range of technology enhanced learning (e-learning) approaches, some of which are underpinned by theoretical frameworks. OBJECTIVE: Synthesise literature on theories that inform technology enhanced learning in nursing and midwifery education. DESIGN: A systematic review. DATA SOURCE: CINAHL, ERIC, MEDLINE and PubMed were searched for relevant studies (2000-2021). Reference lists of related literature reviews were hand searched. REVIEW METHODS: Title and abstract, followed by full texts were screened by two reviewers independently using predefined eligibility criteria. Quality appraisal was not undertaken. Data were extracted and Merriam and Bierema's typology of adult learning theories used to categorise theories in each study. RESULTS: Thirty-three studies were included, incorporating twenty-nine distinct learning theories from the behaviourist, cognitivist, constructivist, and social cognitivist domains, with constructivist being the most widely used. Kolb's Experiential Learning Theory and Driscoll's Constructivist Learning Theory were the most commonly reported theories. The population of learners were mainly undergraduate nursing students who used a range of online, mobile, blended or computerised learning, virtual reality, or digital forms of simulation, primarily in university settings. Theories were employed to inform the technology enhanced learning intervention or to help explain how these could improve student learning. CONCLUSION: This review highlighted a range of theories, particularly constructivist approaches, that underpin research on technology enhanced learning in nursing education, by informing or explaining how these digital interventions support learning. More rigorous research that examines the myriad of theoretical frameworks and their effectiveness in informing and explaining technology enhanced learning is needed to justify this approach to pedagogical nursing research and practice.
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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 | 0.054 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.076 | 0.060 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".