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.

<p>The discipline of data science has gained substantial attention recently. This is mainly attributed to the technological advancement that led to an exponential increase in computing power and has made the generation and recording of enormous amounts of data possible on an everyday basis. It has become crucial for industries to wrangle, curate, and analyze data using data science techniques to make informed decisions. Making informed decisions is complex. Therefore, a trained data science workforce is required to analyze data on a real-time basis. The increasing demand for data science professionals has caused higher education institutions to develop courses and train students starting from the undergraduate level about the data science concepts and tools.</p> <p>Despite the efforts from the institutions and national agency such as National Academies of Sciences, Engineering, and Medicine, it has been witnessed that there have been significant challenges in retaining and attracting students in the discipline of data science. The novice learners in data science are required to possess the skills of a programmer, a statistician, research skills, and non-technical skills such as communication and critical thinking. The undergraduate students do not possess all the required skills, which, in turn, creates a cognitive load for novice learners (Koby & Orit, 2020). Research suggests that improving the teaching and mentoring methodologies can improve retention for students from all demographic groups (Seymour, 2002). Previous studies (e.g., Hoffmann et al., 2002, Flynn, 2015; Lenning & Ebbers, 1999) have revealed that learning communities are effective in improving student retention, especially at the undergraduate level, as it helps students develop a sense of belonging, socialize, and form their own identities. Learning communities have been identified as <em>high impact practices</em> (Kuh, 2008) that helps to develop identities and sense of belonging, however to the best of our knowledge there are few studies that focus on the development of the psychosocial and cognitive skills of the students enrolled in a data science learning community.</p> <p>To meet the demand for the future workforce and help undergraduate students develop data science skills, The Data Mine (TDM) at Purdue University has undertaken an initiative in the discipline of data science. The Data Mine is an interdisciplinary living-learning community that allows students from various disciplines to enroll and learn data science skills under the guidance of competent faculty and corporate mentors. The residential nature of the learning community allows the undergraduate students to live, learn and socialize with peers of similar interests and develop a sense of belonging. The constant interaction with knowledgeable faculty and mentors in real-world projects allows novice learners to master data science skills and develop an identity. The study aims to characterize the effects of identity formation, socialization, and learning of the undergraduate students enrolled in The Data Mine and answer the following research question:</p> <p><br></p> <p><strong>Quantitative: RQ 1:</strong> What are the perceptions of students regarding their identity formation, socialization opportunities, self-belief, and academic/intellectual development in The Data Mine? </p> <p><strong>Qualitative: Guiding RQ 2:</strong> How do students’ participation in activities and interaction with peers, faculty, staff at The Data Mine contribute to becoming an experienced member of the learning community?</p> <ul> <li><strong>Sub-RQ 2(a):</strong> What are the perceived benefits and challenges of participating in The Data Mine?</li> <li><strong>Sub-RQ 2(b):</strong> How do students describe their levels of socialization and a sense of belonging within The Data Mine?</li> <li><strong>Sub-RQ 2(c):</strong> How do students’ participation and interaction in The Data Mine help them form their identity?</li> </ul> <p>To approach the above research questions, we conducted a sequential explanatory mixed method study to understand the growth journey of students in terms of socialization, sense of belonging and identity formation. The data were collected in two phases: a quantitative survey study followed by qualitative semi-structured interviews. The quantitative data was analyzed using descriptive and inferential statistics, and qualitative data were analyzed using thematic analysis, followed by narrative analysis. The results of the quantitative and qualitative analysis demonstrated that learning in The Data Mine happened through interaction and socialization of the students with faculty, staff, and peers at The Data Mine. Students found multiple opportunities to learn and develop data science skills, such as working on real-world projects or working in groups. This continuous interaction with peers, faculty and staff at The Data Mine helped them to learn and develop identities. This study revealed that students did develop a data science identity, but the corporate partner TAs developed a leader identity along with the data science identity. In summary all students grew and served as mentor, guide, and role models for new incoming students.</p>