Background: The small group tutorial is a cornerstone of problem-based learning. By implication, the role of the facilitator is of pivotal importance. The present investigation canvassed perceptions of facilitators with differing levels of experience regarding their roles and duties in the tutorial.
Methods: In January 2002, one year after problem-based learning implementation at the Nelson R. Mandela School of Medicine, facilitators with the following experience were canvassed: trained and about to facilitate, facilitated once only and facilitated more than one six-week theme. Student comments regarding facilitator skills were obtained from a 2001 course survey.
Results: While facilitators generally agreed that the three-day training workshop provided sufficient insight into the facilitation process, they become more comfortable with increasing experience. Many facilitators experienced difficulty not providing content expertise. Again, this improved with increasing experience. Most facilitators saw students as colleagues. They agreed that they should be role models, but were less enthusiastic about being mentors. Students were critical of facilitators who were not up to date with curriculum implementation or who appeared disinterested. While facilitator responses suggest that there was considerable intrinsic motivation, this might in fact not be the case.
Conclusions: Even if they had facilitated on all six themes, facilitators could still be considered as novices. Faculty support is therefore critical for the first few years of problem-based learning, particularly for those who had facilitated once only. Since student and facilitator expectations in the small group tutorial may differ, roles and duties of facilitators must be explicit for both parties from the outset.
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http://dx.doi.org/10.1186/1472-6920-3-9 | DOI Listing |
Heliyon
January 2025
School of Digital Science, Universiti Brunei Darussalam, Gadong BE1410, Brunei Darussalam.
Microlearning has become increasingly popular not only in education sector but also in corporate sector in recent years. However, its definition and didactics conceptualization, integration into instruction design, and effects on learning outcomes remain largely underexplored in terms of synthesized findings. Consequently, challenges persist in clarifying microlearning definition, and didactics, and designing effective microlearning instruction to yield improved learning outcomes.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Institute of Nursing and Health Promotion, Oslo Metropolitan University, Oslo, Norway.
Background: The digital shift in higher education is moving from teacher-focused models to active learning with digital technologies, including the integration of game-based learning strategies. We aim to identify, assess, and summarize the findings of evidence and determine the effectiveness of game-thinking on learning outcomes in nursing education.
Methods: A comprehensive search for relevant literature was conducted between April and May 2022 Seven databases ERIC, Scopus, ProQuest Education Source, MEDLINE, CINAHL, Web of Science, and Embase were utilized to locate original, peer-reviewed papers published in English.
BMC Med Educ
January 2025
Department of Anatomy, Clinical Sciences Building, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308323, Singapore.
Study Objective: Student-centered learning and unconventional teaching modalities are gaining popularity in medical education. One notable approach involves engaging students in producing creative projects to complement the learning of preclinical topics. A systematic review was conducted to characterize the impact of creative project-based learning on metacognition and knowledge gains in medical students.
View Article and Find Full Text PDFISA Trans
January 2025
School of Control Science and Engineering, Shandong University, Jinan, 250012, China. Electronic address:
The distributed microgrids cooperate to accomplish economic and environmental objectives, which have a vital impact on maintaining the reliable and economic operation of power systems. Therefore a distributed multi-agent reinforcement learning (MARL) algorithm is put forward incorporating the actor-critic architecture, which learns multiple critics for subtasks and utilizes only information from neighbors to find dispatch strategy. Based on our proposed algorithm, multi-objective optimal dispatch problem of microgrids with continuous state changes and power values is dealt with.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China. Electronic address:
Recent progress in Graph Convolutional Networks (GCNs) has facilitated their extensive application in recommendation, yielding notable performance gains. Nevertheless, existing GCN-based recommendation approaches are confronted with several challenges: (1) how to effectively leverage multi-order graph connectivity to derive meaningful node embeddings; (2) faced with sparse raw data, how to augment supervision signals without relying on auxiliary information; (3) given that GCNs necessitate the aggregation of neighborhood nodes, and the sparsity of these nodes can exacerbate the impact of noise data, how to mitigate the noise problem inherent in the raw data. For tackling aforementioned challenges, we devise a new hybrid propagation GCN-based method named S3HGN, incorporating a simplified self-supervised learning paradigm for recommendation.
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