Background: Adaptation to learning styles has been proposed to enhance learning.
Objective: We hypothesized that learners with sensing learning style would perform better using a problem-first instructional method while intuitive learners would do better using an information-first method.
Design: Randomized, controlled, crossover trial.
Setting: Resident ambulatory clinics.
Participants: 123 internal medicine residents.
Interventions: Four Web-based modules in ambulatory internal medicine were developed in both "didactic" (information first, followed by patient problem and questions) and "problem" (case and questions first, followed by information) format.
Measurements: Knowledge posttest, format preference, learning style (Index of Learning Styles).
Results: Knowledge scores were similar between the didactic (mean +/- standard error, 83.0 +/- 0.8) and problem (82.3 +/- 0.8) formats (p = .42; 95% confidence interval [CI] for difference, -2.3 to 0.9). There was no difference between formats in regression slopes of knowledge scores on sensing-intuitive scores (p = .63) or in analysis of knowledge scores by styles classification (sensing 82.5 +/- 1.0, intermediate 83.7 +/- 1.2, intuitive 81.0 +/- 1.5; p = .37 for main effect, p = .59 for interaction with format). Format preference was neutral (3.2 +/- 0.2 [1 strongly prefers didactic, 6 strongly prefers problem], p = .12), and there was no association between learning styles and preference (p = .44). Formats were similar in time to complete modules (43.7 +/- 2.2 vs 43.2 +/- 2.2 minutes, p = .72).
Conclusions: Starting instruction with a problem (versus employing problems later on) may not improve learning outcomes. Sensing and intuitive learners perform similarly following problem-first and didactic-first instruction. Results may apply to other instructional media.
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http://dx.doi.org/10.1007/s10459-007-9089-8 | DOI Listing |
Front Plant Sci
January 2025
College of Information Technology, Jilin Agricultural University, Changchun, China.
Introduction: Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification.
View Article and Find Full Text PDFCureus
December 2024
Medical Education, University of South Florida Morsani College of Medicine, Tampa, USA.
Background AI language models have been shown to achieve a passing score on certain imageless diagnostic tests of the USMLE. However, they have failed certain specialty-specific examinations. This suggests there may be a difference in AI ability by medical topic or question difficulty.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
USC Viterbi School of Engineering, University of Southern California, Los Angeles, California 90089-1455, USA.
Voice quality serves as a rich source of information about speakers, providing listeners with impressions of identity, emotional state, age, sex, reproductive fitness, and other biologically and socially salient characteristics. Understanding how this information is transmitted, accessed, and exploited requires knowledge of the psychoacoustic dimensions along which voices vary, an area that remains largely unexplored. Recent studies of English speakers have shown that two factors related to speaker size and arousal consistently emerge as the most important determinants of quality, regardless of who is speaking.
View Article and Find Full Text PDFJ Funct Morphol Kinesiol
December 2024
Department of Biomedical Sciences for Health, Università degli Studi di Milano, 20133 Milan, Italy.
Background/objectives: The educational system thinking approach (ST) takes a holistic vision of instructors/teachers and learners' relationships, making sports pivotal for reflection on education. This study evaluated the efficacy of a multisport ST-based course on minirugby instructors' teaching competence and children players' motor conduct.
Methods: The twenty-five rugby instructors (IAC) attended the 25 h course and the children of their teams (n = 109, Ch-IAC) participated in this study as experimental groups.
BMC Public Health
January 2025
Department of Health and Intelligent Engineering, College of Health Management, China Medical University, 110122, Shenyang, Liaoning Province, China.
Background: Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms may be linked to various factors experienced during both childhood and adolescence. Machine learning (ML) has attracted substantial attention in the field of adolescent depression; however, studies establishing prediction models have primarily considered childhood or adolescent features separately, resulting in a lack of analyses that incorporate factors from both stages.
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