Self-Study as a Method for Continuous Professional Learning in Nursing Education
Bibliographic record
Abstract
Self-study is recognized as an effective method for examining, understanding, and improving one’s teaching practices in teacher education. This research employed self-study as a continuous professional learning (CPL) approach to become a better clinical nursing instructor, to learn about the effectiveness of formative assessment for promoting more independent self-regulated learning in nursing students, and to build knowledge for scholarship in the profession of nursing. This research primarily employed a qualitative methodology in three connected studies conducted over three years in clinical nursing education. In each self-study a reflective journal was used for systematic documentation of instructor planning and decision-making, and student responses to the implementation of five formative assessment strategies. Iterative cycles of planning, implementing, and reflecting on instruction and students’ responses facilitated the collection of rich contextually bound data for analysis and interpretation. Study one examined self-study as an approach for a clinical instructor to translate and implement five specific formative assessment strategies, shown to promote student learning in education, in clinical nursing education. To continue learning about formative assessment and to explore others’ perspectives and experiences, study two employed surveys and interviews with clinical instructor colleagues and their nursing students. The third study, conducted in a new self-study context, examined student reflection assignments to examine the potential of a curriculum-required reflection on clinical practice for supporting independent self-regulated learning. This research presents the first known self-study of pedagogical practice in clinical nursing education. Results of this study suggest that self-study is an effective approach for CPL to advance competency in clinical instruction. Embedding formative assessment strategies in clinical nursing education was seen to influence and support both teaching and learning. Participation in learning activities that integrated multiple feedback loops (instructor, peer, self, other) was seen to provide students with more helpful evidence for regulating their learning. The results of this study suggest that instructors and curriculum developers must be explicit in communicating learning intentions and success criteria, and strategic in embedding formative assessment in the instruction and curriculum in support of students’ self-regulation of learning.
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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 | 0.092 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".