Microlearning in Health Professions Education: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Microlearning, the acquisition of knowledge or skills in the form of small units, is endorsed by health professions educators as a means of facilitating student learning, training, and continuing education, but it is difficult to define in terms of its features and outcomes. OBJECTIVE: This review aimed to conduct a systematic search of the literature on microlearning in health professions education to identify key concepts, characterize microlearning as an educational strategy, and evaluate pedagogical outcomes experienced by health professions students. METHODS: A scoping review was performed using the bibliographic databases PubMed (MEDLINE), CINAHL, Education Resources Information Center, EMBASE, PsycINFO, Education Full Text (HW Wilson), and ProQuest Dissertations and Theses Global. A combination of keywords and subject headings related to microlearning, electronic learning, or just-in-time learning combined with health professions education was used. No date limits were placed on the search, but inclusion was limited to materials published in English. Pedagogical outcomes were evaluated according to the 4-level Kirkpatrick model. RESULTS: A total of 3096 references were retrieved, of which 17 articles were selected after applying the inclusion and exclusion criteria. Articles that met the criteria were published between 2011 and 2018, and their authors were from a range of countries, including the United States, China, India, Australia, Canada, Iran, Netherlands, Taiwan, and the United Kingdom. The 17 studies reviewed included various health-related disciplines, such as medicine, nursing, pharmacy, dentistry, and allied health. Although microlearning appeared in a variety of subject areas, different technologies, such as podcast, short messaging service, microblogging, and social networking service, were also used. On the basis of Buchem and Hamelmann's 10 microlearning concepts, each study satisfied at least 40% of the characteristics, whereas all studies featured concepts of maximum time spent less than 15 min as well as content aggregation. According to our assessment of each article using the Kirkpatrick model, 94% (16/17) assessed student reactions to the microlearning (level 1), 82% (14/17) evaluated knowledge or skill acquisition (level 2), 29% (5/17) measured the effect of the microlearning on student behavior (level 3), and no studies were found at the highest level. CONCLUSIONS: Microlearning as an educational strategy has demonstrated a positive effect on the knowledge and confidence of health professions students in performing procedures, retaining knowledge, studying, and engaging in collaborative learning. However, downsides to microlearning include pedagogical discomfort, technology inequalities, and privacy concerns. Future research should look at higher-level outcomes, including benefits to patients or practice changes. The findings of this scoping review will inform education researchers, faculty, and academic administrators on the application of microlearning, pinpoint gaps in the literature, and help identify opportunities for instructional designers and subject matter experts to improve course content in didactic and clinical settings.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".