This dissertation explores creating a set of domain-specific learning materials for the biomedical sciences to meet the educational gap in biomedical informatics, while also meeting the call for statisticians advocating for process improvements in other disciplines. Data science educational materials are plenty enough to become a commodity. This provides the opportunity to create domain-specific learning materials to better motivate learning using real-world examples while also capturing intricacies of working with data in a specific domain. This dissertation shows how the use of persona methodologies can be combined with a backwards design approach of creating domain-specific learning materials.
The work is divided into three (3) major steps:
(1) create and validate a learner self-assessment survey that can identify learner personas by clustering.
(2) combine the information from persona methodology with a backwards design approach using formative and summative assessments to curate, plan, and assess domain-specific data science workshop materials for short term and long term efficacy.
(3) pilot and identify at how to manage real-time feedback within a data coding teaching session to drive better learner motivation and engagement.
The key findings from this dissertation suggests using a structured framework to plan and curate learning materials is an effective way to identify key concepts in data science. However, just creating and teaching learning materials is not enough for long-term retention of knowledge. More effort for long-term lesson maintenance and long-term strategies for practice will help retain the concepts learned from live instruction. Finally, it is essential that we are careful and purposeful in our content creation as to not overwhelm learners and to integrate their needs into the materials as a primary focus. Overall, this contributes to the growing need for data science education in the biomedical sciences to train future clinicians use and work with data and improve patient outcomes. / Doctor of Philosophy / Regardless of the field and domain you are in, we are all inundated with data. The more agency we can give individuals to work with data, the better equipped they will be to bring their own expertise to complex problems and work in multidisciplinary teams. There already exists a plethora of data science learning materials to help learners work with data; however, many are not domain-focused and can be overwhelming to new learners. By integrating in domain specificity to data science education, we hypothesize that we can help learners learn and retain knowledge by keeping them more engaged and motivated. This dissertation focuses on the domain of the biomedical sciences to use best practices on how to improve data science education and impact the field. Specifically, we explore how to address major gaps in data education in the biomedical field and create a set of domain-specific learning materials (e.g. workshops) for the biomedical sciences. We use best educational practices to curate these learning materials and assess how effective they are. This assessment was performed in three (3) major steps including: (1) identify who the learners are and what they already know in the context of using a programming language to work with data, (2) plan and curate a learning path for the learners and assessing materials created for short and long term effectiveness, and (3) pilot and identify at how to manage real-time feedback within a data coding teaching session to drive better learner motivation and engagement. The key findings from this dissertation suggest using a structured framework to plan and curate learning materials is an effective way to identify key concepts in data science. However, just creating the materials and teaching them is not enough for long-term retention of knowledge. More effort for long-term lesson maintenance and long-term strategies for practice will help retain the concepts learned from live instruction. Finally, it is essential that we are careful and purposeful in our content creation as to not overwhelm learners and to integrate their needs into the materials as a primary focus. Overall, this contributes to the growing need for data science education in the biomedical sciences to train future clinicians to use and work with data and improve patient outcomes.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/108073 |
| Date | 01 February 2022 |
| Creators | Chen, Daniel |
| Contributors | Genetics, Bioinformatics, and Computational Biology, Brown, Anne M., Lewis, Stephanie N., Higdon, David, Hanlon, Alexandra L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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