Traditionally, whenever there are significant computational requirements, computer science, computer engineering, or information science programs will adjust their teaching contents to fit the needs from those fields by introducing more background knowledge to the students in these computational programs. However, this solution will not be sustainable when the computational challenges are from all the fields at the same time. One alternative and more sustainable solution is to expand computational thinking centered training into all other fields. This is what this team is proposing. More specifically, this team will integrate computational thinking into health science education in an approach named ?Health Computing?. For students in health sciences and many fields other than computer science, computer engineering, or information science, core computer science courses are difficult. It is also difficult for them to understand the relevance of computer science courses and the core courses in their specific fields. However, the digitalization of all Americans? health records and the progress made in biomedical technologies require health science students to have more advanced training in computing technologies, methods, and tools in order to be globally competitive in their future career. In this proposed project, the team will develop a series of computer and information science courses which are highly relevant to health sciences and arrange a number of activities (such as high school outreach, summer internship, faculty development, multidisciplinary collaboration) to expose the computational thinking centered course materials and teaching strategies to a wider audience.
The project plans to provide teaching opportunities to the graduate and undergraduate students as well. In addition, this project will provide summer internship opportunities to the participating students through the business partners of School of Health and Rehabilitation Sciences at University of Pittsburgh. This will allow the students to apply their knowledge and skills beyond the classroom. The high school outreach program proposed in this project will be combined with the one offered by the School of Engineering at University of Pittsburgh, which has a long successful history of recruiting underrepresented students in STEM programs. This project will introduce the proposed teaching strategy and course materials to other health science schools and institutes since they will be applicable and transferable in many other fields.
The general goal of this project is to improve computational thinking ability of health science students via courses, projects, internship, and outreach activities. The following is a brief summary of the project outcome. The team of this project created new courses, course modules, tutorials in six different topics (programming, database, data mining, biomedical modeling, information security and privacy, and computational genomics) and at five different levels. All these course materials were integrated into an updated undergraduate curriculum and delivered to undergraduate students in the past three years. Feedback obtained from students and faculty members indicated that these course materials helped students to improve their computational thinking ability. All the course materials are freely available online to anyone who is interested in using them in their teaching. Faculty members in the team guided undergraduate students to work on research projects in courses and outside the classroom. Nine undergraduate students received genomics research training in a genomics course; 79 undergraduate students received basic genomics research training in a quality management course; 77 undergraduate students received computational modeling training in an epidemiology course; and 11 undergraduate students received extensive research training in comparative genomics, data visualization, and biomedical modeling. Almost all students who received these types of research training believed that research projects made them more active learners and they had better problem-solving ability. We arranged multiple internship experiences for undergraduate students in their junior and senior years. Each year, roughly 30 students in average received internship experience in various sites such as research labs, companies, small clinics, and hospitals. In the internship experience surveys, quite a number of students reported that they noticed that computer technologies had been extensively used in healthcare industry and they kept changing. These students also figured out the importance of courses in their curriculum since they were extremely useful for finishing their internship assignments. We outreached 354 high school students (majority of them were female students) and provided tutorials on four topics: genomics, data mining, biomedical modeling, and information security and privacy. Faculty members in the team worked together to move the project forward and also developed collaborative research partnership. We conducted several joint research projects and created several research proposals together. Faculty members and undergraduate researchers in this project published 5 journal articles, gave 22 conference presentations, and submitted one journal article and two conference abstracts for review. In 3 out of these 5 published journal articles, the first authors were undergraduate students in this project. In 9 out of these 22 conference presentations, the presenters were undergraduate students. In other words, with proper research training and guidance from faculty members, undergraduate students can make significant contributions in scientific research and demonstrate dramatic improvement in their computational thinking ability. Faculty members in this project also shared their teaching approaches and disseminated findings from this project by publishing these journal articles and giving presentations at national conferences. Below is a list of journal publications and presentations with undergraduate students as the first author. The names of undergraduate authors are underlined. Yenerall, P. and Zhou, L. (2012) "Identifying the Mechanisms of Intron Gain: Progress and Trends", Biology Direct, 7:29. Yenerall P., Krupa, B., Zhou, L. (2011) "Mechanisms of Intron Gain and Loss in Drosophila", BMC Evolutionary Biology, 11:363. Ludwig, B., Zhou, L., Watzlaf, V., Abdelhak, M. (2010) "Adding a Genomic Healthcare Component to a Health Information Management Curriculum", Perspectives in Health Information Management, 7:1b. Curtin, A., Zhou, L., (2012), "Development of an Agent-Based Model Simulating the Effects of Angioplasty and Bare-Metal Stent Implantation in an Atherosclerotic Blood Vessel", Poster, 2012 Biomedical Engineering Society Annual Meeting. Yenerall, P., Jiang, Y, Zhou, L., (2012), "MIGL: A Database for Identifying the Mechanisms of Intron Gain and Loss", Poster, 2nd IEEE International Conference on Computational Advances in Bio and Medical Sciences. Yenerall, P., Zhou, L., (2012), "Insights into the Mechanisms of Intron Gain and Loss Using Drosophila Genomes", Poster, 53rd Annual Drosophila Research Conference. Yenerall, P., Zhou, L., (2011), "Transposons: A Source of Novel Introns", Poster, Science 2011, University of Pittsburgh. Karim, H., Zhou, L. (2011), "Atherosclerotic Plaque Rupture and Thrombosis Formation: An Investigation Using Agent-Based Modeling Approach", Poster, 2011 Biomedical Engineering Society Annual Meeting. Krupa, B., Zhou, L. (2011), "Genome Viewer - A Web-based Tool for Comparing Bacteria Genomes", University of Pittsburgh Undergraduate Research Fair. Yenerall, P., Zhou, L., Krupa, B., Wang, M. (2011), "Mechanisms of Spliceosomal Intron Gain and Loss: An Investigation and Review Using 12 Drosophila Species", Poster, 52nd Annual Drosophila Research Conference. Karim, H., Zhou, L., (2010), "Investigating Relationships between Obesity and the Built Environment Using Agent-Based Modeling", Platform Presentation and Poster, 8th Annual Rocky Mountain Bioinformatics Conference. Karim, H., Yanerall, P., Zhou, L., (2010), "Manually Annotating Genes in Multiple Drosophila Genomes Using Evidence-Based Approach", Poster, Science 2010, University of Pittsburgh.
