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This study identified aspects of medical curriculum that play an important role in how medical education is conducted

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education.

PEERJ, (2014): e683-e683

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Abstract

Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in ...More

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Introduction
  • The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze.
  • The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes.
  • It appears to be a useful tool to explore such data with possible future implications on healthcare education
  • It opens a new direction in medical education informatics research.
  • Previously unperceived discrepancies between taught and the assessed curriculum in medical program were revealed using a web-based learning objectives database (Hege et al, 2010)
Highlights
  • The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze
  • The connections between teaching methods and learning outcomes depict the percentages of which each teaching method’s content is used to teach each learning outcome. This visualization provides a map of learning outcomes and teaching methods in which starting from 100% of the teaching methods and going through the connections to the percentages of individual teaching methods, a hierarchy of teaching methods is created according to the relative percentages they are used
  • Through the assessment of existing Visual analytics (VA) tools, no validated VA tool for analysis and visualization of curriculum data was found and the most appropriate one was selected to build an abstract model of the examined data in three different approaches: (i) learning outcomes and teaching methods, (ii) examination and learning outcomes, and (iii) teaching methods, learning outcomes, examination results, and gap analysis
  • The level of analysis offered by these three approaches allow the user to create novel, penetrating insights and an understanding of the underlying information in the curriculum data that was not possible before the use of VA
  • The value of the VA method applied in this study resides in the promotion of analytical reasoning in order to create both a detailed understanding and a high-level overview of the examined data, to master past and present situations and support decision-making for future situations
  • The findings of this study provide a novel way of evaluating and verifying how ongoing medical education is structured by perceiving the role of learning and assessment activities towards the desired learning outcomes in the big picture of the curriculum, and foster planning and designing with a solid understanding of the core information in the curriculum with VA as a tool with possible positive implications on medical education informatics research and on how quality improvement of medical education is designed
Methods
  • Exploring novel ways of analyzing and representing medical curriculum data implies the creation of an abstract model to represent the curriculum data in the initial form.
  • Modeling methodology does not concentrate only on the model itself, and allows the model to be used as an instrument to study the research object.
  • It does not strictly define the modeling approach but instead is flexible, allowing the researcher to make decisions concerning the importance of various aspects of the real system that is to be modeled (Amaral et al, 2011).
  • It consists of different learning activities, assessment methods, learning outcomes (LO1–LO16), and main outcomes
Results
  • Identified aspects Below are eleven aspects as they were identified in the curriculum data of the CM-RD course after they were analyzed according to the methods described above.
  • The connections between teaching methods and learning outcomes depict the percentages of which each teaching method’s content is used to teach each learning outcome.
  • Comparing learning outcomes and teaching methods, in the event that the learning outcome’s percentage is equal to the teaching method’s percentage, this means that the teaching method uses learning activities to address this learning outcome fully.
  • The map of learning outcomes and teaching methods is complete with the course’s non-addressed learning outcomes and activities that are not teaching-oriented
Conclusion
  • This study strove to use VA to provide novel ways of analyzing and representing big educational data that is regularly collected for healthcare education evaluation purposes.
  • The enormous amounts of educational data produced in medical education in relation to teaching, learning, assessment, and outcomes and the different sources and forms these educational data can take, make it an area in which big data and visual analytics can be extremely useful to make sense of the complex information to be found in large and diverse datasets (Ellaway et al, 2014).In this study the authors used curriculum mapping to make sense of collected curriculum data from an undergraduate medical program
  • This made it possible to analyze the data and identify important aspects that affect how medical education is conducted.
  • The findings of this study provide a novel way of evaluating and verifying how ongoing medical education is structured by perceiving the role of learning and assessment activities towards the desired learning outcomes in the big picture of the curriculum, and foster planning and designing with a solid understanding of the core information in the curriculum with VA as a tool with possible positive implications on medical education informatics research and on how quality improvement of medical education is designed
Funding
  • The study was funded with intramural funds from Karolinska Institutet. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Grant Disclosures The following grant information was disclosed by the authors: Intramural funds from Karolinska Institutet.
Study subjects and analysis
students: 16
Teaching methods, learning outcomes, examination results and gap analysis In Fig. 6, the teaching methods are depicted in green, main outcomes in blue, learning outcomes—taught on the left side of the blue circles, non-taught on the bottom left side, assessed on the right side of the blue circles, non-assessed on the bottom center and assessed but non-taught on the bottom right side—in dark pink and the number of points on questions on the written examination are in orange (points A9–A11). Percentages of connections between assessed learning outcomes and the number of points depict the success rate on a learning outcome from an average of sixteen students’ answers on the written examination. The three dark pink circles surrounded with black lines on the right side of the blue circles depict the three different places that the assessed but non-taught LO4 outcome, itself on the bottom right side, can be found

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