The effect of mLearning on motivation in the Continuing Professional Development of nursing professionals: A Self-Determination Theory perspective
Bibliographic record
Abstract
Mobile learning (mLearning) has gained popularity in recent years, particularly in the clinical setting. mLearning reduces the theory-practice gap by providing relevant information to nurses and boosting clinical skills. Despite the vast majority of work in this area, few studies in nursing have investigated the correlation between motivation and mLearning for continuing practice development (CPD). Motivation is an essential theoretical concept used to explain human motive that is not new in nursing. Understanding the notion of motivation directed towards learning may clarify the role of technology within pedagogy. Additionally, associating motivation and self-determination may be crucial in understanding motivation in professional nursing practice and education. This study determines the effect of mLearning on motivation to enhance CPD in nursing professionals (NP) analysed critically through a Self-Determination Theory lens. Twenty-three qualified nurses working within the clinical area participated by using a specific mobile application on their smartphone to learn nursing related skills. Over three weeks, participants logged in their learning experience, providing an overview of the relationship between motivation and mLearning. The nurses participating in the study found mLearning motivational in the clinical setting and indicated ownership of their learning, suggesting perceived autonomy. Furthermore, the mobile application enhanced nursing practices through gaining competency and fostered team building through interactions with other health professionals in the clinical area, demonstrating relatedness. This work suggests that having ownership of the learning experience fosters motivation through intrinsic and external needs, supporting learning and gaining competency in the clinical area. Also, the need to become competent and share with others further nurtures motivation to learn in the clinical area. Additionally, these findings suggest mLearning features that motivate NP towards clinical development. This study concludes with implications for the scholarship on mLearning for the continual practice development of nurses.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".