Enhancement of Academic Programs Using Digital Technology PROPOSAL COVER SHEET Project title: Developing a Browser of Student and Course Objects (BoSCO) as an Easy to Use Learning Analytics Tools to Facilitate Effective and Efficient Course and Curriculum Design Project lead · Name: Robert Dunbar · Title: Associate Professor · Phone: 507.258.8209 · Email: [email protected] Undergraduate academic degree program: BSHS University of Minnesota Rochester Number of students graduating in program: The Bachelors of Science in Health Sciences (BSHS) Program at the University of Minnesota Rochester is a new program, and is on track to graduate its first full class of students in the Spring of 2013. It has a current enrollment of 371 students across the academic years. Number of faculty associated with program: 10 Tenure Track; 25 Non-tenure track Number of course offerings associated with program: There are currently 71 courses associated with the program. Are there a significant number of students taking classes associated with this degree program who are pursuing a different degree program? No Please summarize briefly the key improvements in academic outcomes that would result from support for your proposal: This grant would fund the development of a learning analytics tool that would: 1) allow faculty to intelligently and dynamically explore a large amount of course and student data, 2) facilitate curricular integration and improved course design, and 3) leverage currently available University resources. 1 Background: While designing an integrated curriculum for a Health Science degree, the faculty at the University of Minnesota Rochester have encountered many challenging questions. Among these are: Does an integrated curriculum improve student performance? What content in mathematics is necessary for a Health Science degree? What content and skills in humanities courses should we include? What are the early indicators in student grades, attitudes and background that allow us to predict student success? To start to answer these questions and others, 10 faculty members amassed a preliminary (IRB compliant) dataset containing the gradebook information from 72 courses, which were offered over five consecutive semesters, and represent a variety of disciplinary areas (BIOL, BIOC, CHEM, CLI, HUM, MATH, PHYS, SOC). However, our collective research is hampered by a lack of easy ways to update, sort, represent, and analyze the data. It has become clear that our faculty, and perhaps faculty throughout the University of Minnesota system, would benefit from having a tool that permitted the efficient collection and analysis of course and student data. Learning analytics, which Siemens (2010) defines as “the use of intelligent data, learnerproduced data, and analysis models to discover information and social connections, and to predict and advise on learning,” is emerging as a strategy with the potential to draw meaningful connections among a large and growing body of learner-centered data, analyses of those data, and student performance. Information gathered using well-designed learning analytics tools might also be extremely useful for course and curriculum design with the intent of improving student learning outcomes. To that end, several tools have shown promise in visualizing how students navigate course content in specific learning management environments (Ali, 2012; Ferguson, 2012; Macfadyen and Dawson, 2010). However, the results from these programs also serve to highlight two major problems faced by the emerging field of learning analytics. The first problem is how to determine what data is pedagogically useful (Ali, 2012; Ferguson, 2012) and the second is synthesizing the data in a way that will encourage faculty to use them (Ali, 2012). We believe that one of the best ways to encourage faculty to connect learning analytics with course design is to have them drive the process of identifying the variables that are significant to them, and then provide them with a meaningful way to interact with student and course related data in a dynamic fashion. We are in a unique situation to develop and use such a tool by bringing together faculty, IT, and administration to share data, knowledge, and resources and build a platform for learning analytics across the curriculum. The BSHS program at UMR has a number of qualities that align with the goal and will increase the probability of success for the proposed project. First, there are no discipline specific departments and all design faculty are required to do research on learning as part of their tenure requirements. As a result, our faculty are accustomed to discussing a variety of different disciplinary and cross-disciplinary indicators for assessment and research. In fact, we already have approved IRB protocols (#1008E87333; #0908S71602) in place for collecting student grades and course work across all courses offered in our department. Second, much student data is already stored in relatively central locations. All instructors use our in-house curriculum management system, iSEAL, which contains student assignments and course grade books. We are also fortunate that, as part of the University of Minnesota system, we have the ability to share and access assignments and activities that are stored within both Google Drive and Moodle. Third, formal and informal pathways for communication among faculty and IT staff are already established and are continuously reinforced as a result of the ongoing development of iSEAL. Finally, the small size of our faculty population and general openness to sharing deidentified data 3 means it would be relatively easy to test the preliminary dashboard across a wide array of disciplines. Because of our highly integrated curriculum, we can analyze how performance on specific assignments in a course correlates with another assignment in a different course. As an example of the latter, Dr. Dunbar and Dr. Prat-Resina are currently involved with a funded project to investigate how students' performance on writing papers in Humanities courses correlates with the writing in Laboratory reports in Biology and Chemistry. Goals: For this project, our primary goal is to bring together an interdisciplinary group of faculty to identify a set of relevant research questions, indicators, and databases in order to develop and test a tool that would allow faculty to connect learning analytics with course design. We propose that developing an effective and easy to use learning analytics tool that allows faculty to intelligently and dynamically explore the large amount of student and course data would increase the probability of faculty adoption. This Browser of Student and Course Objects (BoSCO; See example schematic in Figure 1) would therefore help faculty connect course design and learning outcomes at both the course and curricular level and, therefore, increase both the efficiency and effectiveness of course and curriculum design. Figure 1: A schematic representation of the BoSCO interface showing how it allows the combination of different kinds of student and course data for analysis. Partnerships and Internal Resources: The PI’s have the skills and knowledge to design a prototype for the tool, and collectively have expertise in collaborative learning, collection and analysis of quantitative and qualitative data, MySQL database management, and graphical user interface design. UMR Partnerships- In addition to support from our Vice Chancellor/Director, we have also worked directly with Michael Olesen, UMR IT Director, who has reviewed the proposal and agreed to partner with us in the event of funding. UMR IT’s Web and Software Development Group currently consists of four full-time IT professionals with extensive skills in software and web development, database administration, and data analysis. Staff from this group will be dedicated to this project if it is funded, and their expertise would be critical toward the goal of connecting the tool to iSEAL. 4 UMN Partnerships - UMR, in developing its information technology infrastructure, has worked closely with the Office of Information Technology (OIT) to leverage central resources to support UMR programs. The iSEAL curriculum delivery system and its data are hosted on OIT virtual servers. OIT provides UMR services ranging from networking and storage infrastructure to academic technology support. It is our intention, in coordination with the UMR IT Director, to leverage OIT and other UMN IT resources in supporting this project. This will involve seeking expertise interfacing with and extracting data from Google Drives, Google Sites, and in data analytics. Discussions are currently underway to determine the resources that are available and interest in collaboration. Timeline: Reaching our goal will require a coordinated effort between design faculty and IT staff with expertise in software development and database analysis. The efforts of the individuals involved in the project will be managed by the PI’s through a series of formal meetings and the summarized outcome of each meeting will mark specific milestones of progress throughout the funding period. The timeline of the two years of funding will be as follows: Early in the first six months, the PI's will meet with a multidisciplinary group of CLI faculty collaborators. In this meeting the group will identify a list of indicators associated with pedagogical questions relevant to the evaluation and design of their courses. During this meeting, the group will also discuss the location (iSEAL, Moodle, Google Drive, etc.) and accessibility of the data associated with each indicator. The PI’s will then work together to: summarize the outcome of the meeting, generate a hierarchical list of questions with the associated indicators, and identify the databases in which the data is stored. PI’s and collaborators will then meet to finalize the prioritized list as well as begin to discuss possible statistical tools that might be useful. The prioritized questions, the associated indicators, the relevant databases, and suggested statistical tools that are finalized in this meeting will all be used in months seven through twelve by the PI’s and a software developer/database analyst to build a mock-up of the learning analytics tool (BoSCO). The beta version of BoSCO will be completed by the end of the first year of funding. Early in funding months 13-18, the PI’s will present BoSCO to our collaborators for feedback. The PI’s and IT developer will then make changes to BoSCO based on collaborator feedback. An updated version of the analytics tool will then be distributed to our collaborators for use early in funding months 19-24. During this testing period, we plan to collect data on how frequently and in what way our collaborators use the tool. In the last two months of year two, the PI’s will generate a summary of the tool, its capabilities, how it was used, and examples of data analyzed. This summary will represent the final milestone of this stage of the project and we have every expectation that the results will be distributed as a presentation or publication. References: Ali, L., Marek, H., Gasevic, D., Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education 58, 470-489. Ferguson, R. and Buckingham Shum, S. (2012). Social Learning Analytics: Five Approaches. Proc. 2nd International Conference on Learning Analytics & Knowledge, (29 Apr-2 May, Vancouver, BC). ACM Press: New York Macfadyen, L.P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education 54, 588-599. Siemens, G. (2010). What are Learning Analytics? Retrieved January 5, 2013, from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/ 5 Proposed Budget: Year One: PI summer salary and fringe (x 3) = $12,300 Collaborators summer salary and fringe (x 7) = $12,700 Developer/Analyst (1/8 FTE; 5hrs/week) salary and fringe (x 1) = $10,000 Total for Year One: = $35,000 Year Two: PI summer salary and fringe (x 3) = $11,000 Collaborators summer salary and fringe (x 7) = $10,000 Developer/Analyst (1/8 FTE; 5hrs/week) salary and fringe (x 1) = $10,000 Travel (Dissemination of findings) = $4,000 Total for Year One: = $35,000 6 Molly J. Dingel, Ph.D. Assistant Professor, Center for Learning Innovation University of Minnesota Rochester Education/Professional Preparation: B.A. Grinnell College, May 1998, Mathematics and Sociology M.A. University of Kansas, May 2000, Sociology, with honors Ph.D. University of Kansas, December 2005, Sociology Appointments: 2005-2007: Postdoctoral Fellow, Biomedical Ethics Research, Mayo Clinic College of Medicine 2007-2008: Assistant Professor, Sociology, North Dakota State University 2009 - present: Assistant Professor, University of Minnesota Rochester Select Publications/Presentations: Dingel, Molly J., Wei Wei, and Aminul Huq. Forthcoming. “Cooperative learning and peer evaluation: the effect of free riders on team performance and the relationship between course performance and peer evaluation.” Journal of Scholarship on Teaching and Learning. Accepted January 9, 2013. Dingel, Molly J., Rachel Hammer, Jenny Ostergren, and Jennifer B. McCormick. 2012. “Chronic Addiction, Compulsion, and the Empirical Evidence.” American Journal of Bioethics Neuroscience. 3(2):58-59. Dingel, Molly J., Ashley Hicks, Marguerite Robinson and Barbara A. Koenig. 2012. “Integrating Genetic Studies of Nicotine Addiction into Public Health Practice: Stakeholder Perspectives on Challenges, Barriers, Opportunities.” Public Health Genomics. 15(1):4655. Dingel, Molly J. 2013. “Teaching Critical Thinking: A Review Of and Reflection On Classroom Activities.” Accepted for presentation at the Midwest Sociological Society annual meeting. Chicago, IL, March 27-30, 2013. Metzger, Kelsey and Molly J. Dingel. 2012. “Preparing health sciences undergraduates for the complexities of individualized medicine: a curriculum overview.” Individualizing Medicine Conference 2012. Rochester, MN. October 1-3. Huq, Aminul, Molly J. Dingel, and Jered Bartels. 2011. “Integrating Introductory Statistics and Sociology Courses through Group Projects in a Health Sciences Program.” Joint Statistical Meetings. Miami Beach, FL, July 30-August 4, 2011. Dingel, Molly J., Rebecca Bamford, and Robert L. Dunbar. 2010. “Optimizing groupwork to advance learning: exploring collaborative work in undergraduate courses.” Midwest Sociological Society annual meeting. Chicago, IL, April, 2010. 7 Robert L. Dunbar, Ph.D. Associate Professor, Center for Learning Innovation University of Minnesota Rochester Education: B.S.S. Cornell College, May 1993, Biology and History M.A. Drake University, May 1995, Biology Ph.D. University of Minnesota, January 2002, Neuroscience Appointments: 2002-2003: Postdoctoral Fellow, Duke University, Neuroscience Research 2003-2008: Assistant Professor,Biology, Buena Vista University 2009 - present: Associate Professor, University of Minnesota Rochester Select Publications/Presentations: Dunbar, R. L., & Nichols, M. D. Fostering empathy in undergraduate health science majors through the reconciliation of objectivity and subjectivity: An integrated approach. Anatomical sciences education, 5(5), 301–308, 2012. Aryal, B., Dunbar, R.L., Muthyala, R.S. “Assessment of Group Learning in Interdisciplinary Environments”. Proceedings of the 2012 National Association for Research in Science Teaching Annual Meeting, 2012. Petzold, AM, Nichols, MD and Dunbar RL. Teaching artful and accurate scientific presentation skills at the undergraduate level: A multidisciplinary approach. ACUBE annual meeting. October 19-20, 2012. Dingel, Molly J., Rebecca Bamford, and Robert L. Dunbar. 2010. “Optimizing groupwork to advance learning: exploring collaborative work in undergraduate courses.” Midwest Sociological Society annual meeting. Chicago, IL, April, 2010. Dunbar, R.L. “Integrative courses: anatomy and beyond.” Anatomical Sciences Education, 3(2):73-6, 2010. Berglund, K., Dunbar, R.L., Lee, P., Feng, G., and Augustine, G.J. “A Practical Guide: Imaging Synaptic Inhibition with Clomeleon, a Genetically Encoded Chloride Indicator.” Imaging in Neuroscience and Development: A Laboratory Manual, eds. A. Konnerth, F. Lanni, and R. Yuste, Cold Spring Harbor Laboratory Press, 2005. Dunbar, R.L., Chen, G., Gao, W., Reinert, K.C., Feddersen, R., and Ebner, T.J. “Imaging Parallel Fiber and Climbing Fiber Responses and Their Short-term Interactions in the Mouse Cerebellar Cortex in vivo.” Journal of Neuroscience, 126(1):213-27, 2004. 8 Xavier Prat-Resina, PhD Assistant Professor, Center for Learning Innovation University of Minnesota Rochester Education/Professional Preparation: BSc. Chemistry. Universitat de Barcelona (Spain) 1999 PhD. Theoretical Chemistry, Universitat Autonoma de Barcelona (Spain) 2004 Postdoctoral Associate. University of Wisconsin Madison. 2005-2006. Appointments: 2007: "Juan de la Cierva" researcher at the Barcelona Biomedical Research Park (Spain) 2008 2010 : Research Associate for the "Chemical Education Digital Library", Chemistry Department, University of Wisconsin at Madison. 2010 : Associated Lecturer, Chemistry Department, University of Wisconsin at Madison. 2010 - Present: Assistant Professor, University of Minnesota at Rochester Select Publications/Presentations: (in chemical education) Author of ChemEd DL application "Molecules 360" http://www.chemeddl.org/resources/models360 , 2008-2012 Author of ChemEd X Data web platform http://chemdata.umr.umn.edu/chemedXdata/, 2012 Xavier Prat-Resina and Bernadette Caldwell. JCE Concept Connections: Computational Molecular Modeling, . J. Chem. Educ., 86 (8): 958, 2009. (in theoretical chemistry) Andrea Bottoni, Giampietro Miscione, Juan Novoa, and Xavier Prat-Resina. A DFT computational study of the mechanism of allyl halides carbonylation catalyzed by nickel tetracarbonyl. J. Am. Chem. Soc., 125(34):10412-10419, 2003. Xavier Prat-Resina, Josep Maria Bofill, Angels González-Lafont, and José Maria Lluch. Geometry optimization and transition state search in enzymes: Different options in the microiterative method. Int. J. Quant. Chem., 98 (4):367-377, 2004. Xavier Prat-Resina, Angels González-Lafont, and José Maria Lluch. Reaction Mechanism of the Mandelate Anion Racemization Catalyzed by Mandelate Racemase Enzyme: A QM/MM Molecular Dynamics Free Energy Study J.Phys.Chem.B, 109 (44): 21089-21101, 2005. Demian Riccardi, Partricia Schaefer, Yang Yang, Haibo Yu, Nilanjan Ghosh, Xavier PratResina, Peter Koenig, Guohui Li, Dingguo Xu, Hua Guo, Marcus Elstner and Qiang Cui. Development of effective QM/MM methods for complex biological processes J.Phys. Chem.B, 110 (13):6458-6469, 2006 Feature Article, (Cover). Nilanjan Ghosh, Xavier Prat-Resina, M. R. Gunner and Qiang Cui Microscopic pKa Analysis of Glu286 in Cytochrome c Oxidase (Rhodobacter sphaeroides): Toward a Calibrated Molecular Model Biochemistry, 48 (11): 2468–2485, 2009 9
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