Temporal patterns and dynamics of e-learning usage in medical education

Panzarasa, Pietro; Kujawski, Bernard; Hammond, E. J.; Roberts, Christopher

doi:10.1007/s11423-015-9407-4

Cited by 19 publications

(13 citation statements)

References 57 publications

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“…This table only shows the terms with definitions (21 total). Terms identified without definitions were: Dispersed learning; Distributed learning; Repeated retrieval practice; Spaced instruction; Spaced training; Spaced retrieval practice; Spaced studying, and Spaced testing …”

Section: Resultsmentioning

confidence: 99%

Conceptualising spaced learning in health professions education: A scoping review

et al. 2019

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Objectives To investigate the definitions and applications of ‘spaced learning’ and to propose future directions for advancing its study and practice in health professions education. Method The authors searched five online databases for articles published on spaced learning in health professions education prior to February 2018. Two researchers independently screened articles for eligibility with set inclusion criteria. They extracted and analysed key data using both quantitative and qualitative methods. Results Of the 2972 records retrieved, 120 articles were included in the review. More than 90% of these articles were published in the last 10 years. The definition of spaced learning varied widely and was often not theoretically grounded. Spaced learning was applied in distinct contexts, including online learning, simulation training and classroom settings. There was a large variety of spacing formats, ranging from dispersion of information or practice on a single day, to intervals lasting several months. Generally, spaced learning was implemented in practice or testing phases and rarely during teaching. Conclusions Spaced learning is infrequently and poorly defined in the health professions education literature. We propose a comprehensive definition of spaced learning and emphasise that detailed descriptions of spacing formats are needed in future research to facilitate the operationalisation of spaced learning research and practice in health professions education.

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Section: Resultsmentioning

confidence: 99%

Conceptualising spaced learning in health professions education: A scoping review

et al. 2019

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“…The timeline of events (Additional file 1 : Video S1) showed that the development of the online community was initiated by the instructor, students reacted enthusiastically along the course, and stopped around exam times. What is interesting was the instrumental role of the teacher presence that kept students engaged most of the course time, in contrast to the irregular and spiky patterns reported in non-moderated environments [ 14 , 15 , 60 ]. The sustained engagement during the course might have been the best advantage of having a moderator [ 14 , 15 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

How the study of online collaborative learning can guide teachers and predict students’ performance in a medical course

2018

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BackgroundCollaborative learning facilitates reflection, diversifies understanding and stimulates skills of critical and higher-order thinking. Although the benefits of collaborative learning have long been recognized, it is still rarely studied by social network analysis (SNA) in medical education, and the relationship of parameters that can be obtained via SNA with students’ performance remains largely unknown. The aim of this work was to assess the potential of SNA for studying online collaborative clinical case discussions in a medical course and to find out which activities correlate with better performance and help predict final grade or explain variance in performance.MethodsInteraction data were extracted from the learning management system (LMS) forum module of the Surgery course in Qassim University, College of Medicine. The data were analyzed using social network analysis. The analysis included visual as well as a statistical analysis. Correlation with students’ performance was calculated, and automatic linear regression was used to predict students’ performance.ResultsBy using social network analysis, we were able to analyze a large number of interactions in online collaborative discussions and gain an overall insight of the course social structure, track the knowledge flow and the interaction patterns, as well as identify the active participants and the prominent discussion moderators. When augmented with calculated network parameters, SNA offered an accurate view of the course network, each user’s position, and level of connectedness. Results from correlation coefficients, linear regression, and logistic regression indicated that a student’s position and role in information relay in online case discussions, combined with the strength of that student’s network (social capital), can be used as predictors of performance in relevant settings.ConclusionBy using social network analysis, researchers can analyze the social structure of an online course and reveal important information about students’ and teachers’ interactions that can be valuable in guiding teachers, improve students’ engagement, and contribute to learning analytics insights.Electronic supplementary materialThe online version of this article (10.1186/s12909-018-1126-1) contains supplementary material, which is available to authorized users.

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“…This fact could have contributed to a better grading of this group in Human Immunology, but also in a higher difference compared to the grades obtained in related courses during the first year of Medicine. Therefore, competitive strategies can be designed to get a more uniform distribution of knowledge sessions over time and, therefore, promoting learners’ deep understanding and long-term retention [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Effects of competitive learning tools on medical students: A case study

et al. 2018

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ObjectiveCompetitive learning techniques are being successfully used in courses of different disciplines. However, there is still a significant gap in analyzing their effects in medical students competing individually. The authors conducted this study to assess the effectiveness of the use of a competitive learning tool on the academic achievement and satisfaction of medical students.MethodsThe authors collected data from a Human Immunology course in medical students (n = 285) and conducted a nonrandomized (quasi-experimental) control group pretest-posttest design. They used the Mann-Whitney U-test to measure the strength of the association between two variables and to compare the two student groups.ResultsThe improvement and academic outcomes of the experimental group students were significantly higher than those of the control group students. The students using the competitive learning tool had better academic performance, and they were satisfied with this type of learning. The study, however, had some limitations. The authors did not make a random assignment to the control and experimental groups and the groups were not completely homogenous.ConclusionThe use of competitive learning techniques motivates medical students, improves their academic outcomes and may foster the cooperation among students and provide a pleasant classroom environment. The authors are planning further studies with a more complete evaluation of cognitive learning styles or incorporating chronometry as well as team-competition.

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Temporal patterns and dynamics of e-learning usage in medical education

Cited by 19 publications

References 57 publications

Conceptualising spaced learning in health professions education: A scoping review

Conceptualising spaced learning in health professions education: A scoping review

How the study of online collaborative learning can guide teachers and predict students’ performance in a medical course

Effects of competitive learning tools on medical students: A case study

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