12_17 Approach to Developing a CBE Model Which Leverages OERs and Analytics

Editor's Notes

• #3: Online – Over 800 courses; Over XXX sections of courses each term Over 80,000 Students; are working adults Over XXX faculty - Adjuncts are scholar/practitioners
• #4: Key initial elements: What is your definition of CBE? Purpose- retention, recruitment, quality, bandwagon? Replace current model or add to it- will you develop new programs or add additional programs
• #5: We began with a grant from BMI. We started with one section of an HRMN course with 18 students. We redesigned the course to include prerequisite assessments that unlocked content; a real world applied project with multiple parts; a faculty and a separate grader- two roles in the classroom; and no deadlines. Most valuable lesson learned: is what we need to learn. We learned who needs to be involved, what questions to ask, what we need to learn as we explore CBE. We were then ready to launch 4 courses in the computer networking program. Over 80 sections. We will describe how we organized our work for these 4 courses as well as for our larger scale up. First we determined we needed a glossary for all relevant terms for UMUC. If our staff googled CBE they would all have different impressions and interpretations of what we were doing. We also developed a charter, our guiding principles and a guidebook for everyone involved.
• #6: To coordinate efforts across the university, developed workstreams to meet weekly. As well as a functional group that consists of workstream leads. Tried to get as much cross-department and cross-function represented on each. Created a unique and wonderful experience for folks across the university to work together who had never really even known what the other did on a daily basis.
• #7: Governance and communication is key- with so many moving parts and every department involved and effected.
• #8: What will drive all our decisions University of Maryland University College (UMUC) establishes the following principles to describe the learning model for all of its academic programs and courses: UMUC defines learning in terms of what the learner can do, and defines learning mastery in terms of a learner’s ability to apply knowledge in real-world and career-relevant contexts. Academic programs are continuously updated to ensure that learners are prepared for current and future changes in their field. Learning activities are sequenced throughout each program to ensure learners’ progressive development. Learners successfully complete their program when all learning outcomes have been mastered. Faculty members enhance learner progress by providing an active and motivating presence, personal outreach, and mentorship to enhance and deepen learning. The student experience is further enhanced by appropriate academic support and extracurricular engagement. Learning spaces are informed by learning science and best practices in instructional design. Learning spaces are designed to enhance collaboration, communication, and affinity, and allow for learning anytime, anywhere, seamlessly across platforms. The learning model is supported by innovative technology, including data analytics, adaptive learning software, and other tools that provide learner support and continuous improvement of programs based on dynamic information about the learner.
• #9: The design elements focus on how we build the curriculum and the classroom environment for the students. Real-world scenarios (LDs) that not only give the student an opportunity to learn and demonstrate their learning, but also gives them the experience of doing the tasks of a profession; Career related content is woven throughout every course. Learning resources when and where they are needed; the focus is on the demonstration of learning, not on the engagement with a particular resource. Students may learn anywhere – from the learning resources, from colleagues, from their professional experiences, from prior education, but they must learn and be able to demonstrate that learning; Learning evaluation is focused on helping students perfect their ability to apply learning; even when performance is evaluated to be sufficient feedback may be given that the student can use in subsequent work; The scholar-practitioner faculty member is there to give advice to help students achieve mastery through application. When students require advice, faculty members provide it; when students submit their work for evaluation, faculty members quickly give them advice and feedback focused on helping refine ability. The curriculum within a course and across the courses within a program are designed as a group- each builds off the previous with a common set of goals for the student upon completion. The student, faculty and course designers use data to inform decisions. Students can see their progress and performance to determine what to do and when to ask for help. Faculty can see the students and determine when to reach out and when to guide. Course designers can see the full experience and take into account both the faculty and student experience to inform design decisions.
• #10: Can you find anything we have that summarizes this? Any data that can inform decisions about the learning model and environment. Need to evaluate what current systems provide- LMS, SIS, Student Evaluations Faculty analytics (data about the faculty) LMS behaviors grades assigned, rubric norming, feedback time turn around student evaluations Student analytics (data about the students) LMS behaviors academic performance student background characteristics support services accessed Program analytics (data about the program, curriculum, classrooms) course taking patterns content usage
• #11: Plan for continuous improvement: Faculty, student and course analytics Focus groups with faculty and students Surveys for faculty and students Regular workstream meetings to review inputs and make changes to curriculum, policies, standards, etc…