Presentation on theme: "Academic Efficiencies: Using MERLOT to Engage Faculty in the Discussion Phil Moss, Oklahoma State Regents for Higher Education Richard Boyd, Rogers State."— Presentation transcript:
Academic Efficiencies: Using MERLOT to Engage Faculty in the Discussion Phil Moss, Oklahoma State Regents for Higher Education Richard Boyd, Rogers State University Kurt Cogswell, South Dakota State University Amy Smith, Northeastern State University
The “Double Whammy” System Funding and FTE Enrollment History and Projections 119,115 121,111 128,530 134,874 140,250 est. $829.1 $767.8 $791.5 $816.2 $772.2 105,000 110,000 115,000 120,000 125,000 130,000 135,000 140,000 145,000 FY00FY01FY02FY03FY04 $767.0 $777.0 $787.0 $797.0 $807.0 $817.0 $827.0 $837.0 $847.0 $857.0 FTE EnrollmentAppropriations
INSTRUCTIONAL SPENDING PER FTE, BY STATE Percent Change and Current Position Relative to U.S. Average -50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% -50%-40%-30%-20%-10%0%10%20%30%40%50% Percent Over/Under the U.S. Average, FY02 Percent Change, FY91 - FY02 high and increasing low and increasing low and decreasing high and decreasing AK MS WV MD KY IL NJ NYTX AL SD TN CO NE FL OK LA MT NM NC HI RI, MA WA SC ND AR VA ID KSMO GA NH MN OH WI IA ME MI PA VT U.S. WY UT CA IN OR NVAZ DE (66%)
Email from the New Chancellor “The premise is that by working together, faculty in specific topical areas can provide higher-quality learning experiences in more cost- effective ways…The product would be a toolkit of teaching processes/ materials that can be used by faculty across Oklahoma colleges and universities.”
“The intention of the project to achieve uniformly high-quality teaching at all of our colleges & universities. With time and resources saved by pooling ideas and approaches, faculty would free up time for other uses, such as spending more time with students, developing new teaching material, conducting research or providing public service.” Concluding Observation
Some Initial Reactions Don’t call it efficiency! Do we have to? Not if it takes $ away from us! Go beyond course materials to courses/programs Don’t call it efficiency!
Academic Efficiencies - Leadership Council on Instruction (COI) –Represents all Public Higher Education Community Colleges Regional Universities Comprehensive Universities –Members are Chief Academic Officers –Advisory to the Chancellor, State Regents –Meets Monthly (September – June) –Committees Report to the Council
Academic Efficiencies - Planning COI Time-Lines –Discussion at COI through September –Academic Efficiency Committee formed October 2003, 1 st meeting - November –December/January—Workshop Planning –February—Two-day Workshop –April—One day Follow-up –September—Action Plans Developed
The 2-by-2-Pronged Plan Disciplines –A specialized, low-enrollment discipline not currently identified as a MERLOT community Ecology –A general, high-enrollment discipline established as a MERLOT community Mathematics Granularity –Big (Course Level) –Small (Learning Objects)
Goals for Initial Workshop –Ecology and Math (General Education) –Two Faculty from each campus –Nominated by Chief Academic Officer –Funding Assistance from Chancellor’s Office –Keynote Speakers—Set Tone for Project –Faculty Facilitators from Out-Of-State –Build Cooperation/Collaboration
Academic Efficiencies Workshop Agenda –Welcome by Chancellor, Chairs of President’s Council and COI –Center for Academic Transformation Carolyn Jarmon, Associate Director Course Redesign in Action—Campus Perspective (Univ. of Idaho, Fairfield Univ.) –MERLOT Gerry Hanley, Executive Director Collaboration-Sharing Resources (SDSU, Thomas Nelson Community College)
Academic Efficiencies Workshop Goals Understand the Concepts of Efficiencies Textbook Selection Internet Materials Classroom/laboratory equipment/facilities Field trips and Projects Placement and testing tools Consulting and Research Sharing, Creating Learning Community Maintaining Academic Quality and Rigor
MERLOT Mathematics at the Oklahoma Academic Efficiencies Conference
Oklahoma Academic Efficiencies Conference Day One: Large Scale Course Redesign Dr. Carolyn Jarmon -- large scale course redesign Good results backed up by hard data –Student performance does not decrease, often increases –Yearly course operational costs drop Why not do this for every course? –Redesign requires large initial resource commitment –Need large enrollment and/or multi-section classes to achieve goal of a net cost reduction over time –Small fraction ( ~1% ) of classes qualify What about all the other classes?
Oklahoma Academic Efficiencies Conference Day Two: MERLOT and Efficiency Restructuring small fraction of courses (~1%) –Good efficiency increase is possible in a few courses. MERLOT all courses –Typically, smaller increases in efficiency possible per course, but in a much larger number of courses. Why is MERLOT appropriate for all courses? –Much less resource intensive than redesign –Much more flexible than redesign Restructuring Shock and Awe MERLOT Guerrilla warfare
Structure of MERLOT Mathematics Objects and the Category Scheme Objects and the Category Scheme Reviews –A Comparison of Numerical Integration AppletsA Comparison of Numerical Integration Applets –Numerical Integration Applet.Numerical Integration Applet Assignments –Normal Probability ToolNormal Probability Tool Personal Collections –Jim Rutledge Personal CollectionsJim Rutledge Personal Collections
Using the Structure to Enhance Efficiency Target a course for MERLOT-based efficiency enhancement Create a MERLOT identity for the course Distribute login info to participating faculty Create Personal Collections to hold categories of objects What Categories? –Resources –Demonstration Tools –Exploration Tools –Other (topic specific, etc.) These Personal Collections facilitate continuous course improvement and clearly produce other efficiency gains. This would be of particular value to adjuncts and new faculty
More on Resources Examples –Matrix FactsMatrix Facts –Fractal GeometryFractal Geometry Efficiency? Why reinvent the wheel?
More on Exploration Tools Used very selectively. Focus on major ideas that are known to be ineffectively taught using traditional means, and/or ideas that will be used extensively later in the curriculum. Find MERLOT object suitable for guided exploration In a computer lab, student groups discover ideas on their own. Instructor circulates to guide the process. Assessment? Occurs as instructor circulates. Efficiency? Improved teaching of key concepts improves teaching effectiveness and efficiency throughout the curriculum. Example: –Numerical Integration AppletNumerical Integration Applet
MERLOT-Based Efficiency Gains Efficiency measured by Production/Cost Two ways to increase efficiency: –Increase production MERLOT functions to increase both quality and quantity of “product”, graduates. How to quantify??? Where’s the research??? –Decrease cost MERLOT decreases failure/repetition rate. MERLOT decreases faculty time per course. How to quantify??? Where’s the research??? Challenge for MERLOT: produce hard data that demonstrates MERLOT’s positive impact on academic efficiency.
Follow-Up Faculty Workshop Desired outcome –Develop draft plans to implement an efficiency project within ecology including Program development Incorporation Methods for evaluation
The process Facilitated meeting format Address challenge/issue Determine faculty’s objective and outcomes Develop list of potential solutions Identify strategies to address each category to pursue Identify next steps to incorporate strategies
Development – desired outcomes Collaborate! Develop a OK web resource Identify locations for field trips Develop and share activities
Development – strategies Collaborate! Develop a web page –Designed as project in web page development course –Regional web pages linked
Development – strategies Identify locations for field trips –Site information –Local experts –Data bases –Activities
Development – strategies Develop and share activities –Activities by faculty –Links to web-published resources –Specific methods and instrumentation identified –Student directed activities identified
Product – main page Map courtesy of The Nature Conservancy
Product – example field site CATEGORYEXAMPLE AreaNortheast Oklahoma Site NameSequoyah State Park Contact Person(s)Dr. Amy Smith or Dr. Erik Terdal Site MapMap with GPS coordinates What’s Available and When Fees/Permits/Restrictions Facilities/LodgingCamping Seasons Data CollectedAnnual Deer Drive Proximal InstitutionNortheastern State University Available Support at UniversityPersonnel, Rooms/Labs, Equipment News
Product – activities and data sets Activity – Deer census by Dr. Erik Terdal Background A basic parameter of population biology is the density of a population: how many individuals of a species are there per unit area. Population ecologists have long been interested in how the environment affects density and how the density of one population affects populations of other species (competitors, predators, etc.). Wildlife managers use population ecology theory to manage populations of wild animals for the benefit of humans. Locally, white-tailed deer (Odocoileus virginanus) are of interest as game animals, potential crop pests, and as a tourist attraction. The Sequoyah State Park (SSP) near Hulbert on Lake Ft. Gibson has a large population of white-tailed deer. These deer are an important reason why visitors come to the park. As they have not been hunted in many years they are somewhat habituated to humans, permitting close observation. Unfortunately, the high density of deer at SSP has impacted the vegetation of the park. It has also contributed to an abundance of ticks, which affect humans and other animals at the park. For these reasons, former SSP naturalist (and NSU alum)
Product – activities and data sets Table 1. Raw data from Spring drive censuses of white-tailed deer in Sequoyah State Park (Cherokee Co., Oklahoma, USA) conducted from 1989 to 2002. Total park area is approximately 2500 acres (~1000 ha). Year # participants Acres covered # deer counted 1989114616199 1990 12338 99 1991 37382 60 1992 63382 63 1993 44382 64 1996 85382116 1999108382129 2000 (Feb.) 67 274 24 2000 (Dec.) 38150 23 2001 (Feb.) 33150 32 2001 (Nov.) 51150 53 2002 (Feb.) 50150 51 Data set – Deer census by Dr. Erik Terdal