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Using Analytics to Integrate Critical Thinking and Quantitative Reasoning Skills in Business Program Curricula

Using Analytics to Integrate Critical Thinking and Quantitative Reasoning Skills in Business Program Curricula. Dr. Alan Burns, Dean of Business, Leadership and Psychology Dr. Ariane Schauer, Provost Marymount California University 2014 IACBE Annual Conference and Assembly Meeting

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Using Analytics to Integrate Critical Thinking and Quantitative Reasoning Skills in Business Program Curricula

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  1. Using Analytics to Integrate Critical Thinking and Quantitative Reasoning Skills in Business Program Curricula

    Dr. Alan Burns, Dean of Business, Leadership and Psychology Dr. Ariane Schauer, Provost Marymount California University 2014 IACBE Annual Conference and Assembly Meeting San Diego, California April 9, 2014
  2. Outline Goals of the Project Quantitative Reasoning Curriculum Mapping & Assessment Implementation Predictive Analytics Discussion/Questions
  3. Goals of the Project Use learning analytics to drive improvement for curriculum design and delivery Measure learning at the student level across the life of program Move toward predictive analytics and agile development of courses, programs Common metrics for assessment, maintaining flexibility for teaching Minimize the burden of faculty
  4. Quantitative reasoning
  5. MCU BA-Business Program Outcomes Quantitative Literacy Problem Solving Critical Thinking Creative Thinking Integrative Learning Ethics Global Perspective Inquiry Oral Communication Written Communication
  6. AAC&U Definitions Quantitative Literacy (QL) – also known as Numeracy or Quantitative Reasoning (QR) – is a "habit of mind," competency, and comfort in working with numerical data. Critical thinking is a habit of mind characterized by the comprehensive exploration of issues, ideas, artifacts, and events before accepting or formulating an opinion or conclusion. Problem solving is the process of designing, evaluating and implementing a strategy to answer an open-ended question or achieve a desired goal. Creative thinking is both the capacity to combine or synthesize existing ideas, images, or expertise in original ways and the experience of thinking, reacting, and working in an imaginative way characterized by a high degree of innovation, divergent thinking, and risk taking.
  7. Quantitative Reasoning Quantitative Literacy (QL): comfort, competency, and "habit of mind" in working with numerical data. Quantitative Reasoning (QR): higher-order reasoning and critical thinking skills needed to understand and to create sophisticated arguments supported by quantitative data. from the National Numeracy Network, http://serc.carleton.edu/nnn/index.html Elrod, S. & Lindholm, J. (2013). “An Introduction to Quantitative Reasoning and Assessment in Majors”, Retreat on Core Competencies: Quantitative Reasoning and Assessment in Majors, October 24 - 26, 2013, Pomona, CA.
  8. Quantitative Reasoning Learning Outcomes:An Example A graduating fourth-year undergraduate at the University of Virginia will be able to: Interpret mathematical models such as formulas, graphs, tables, and schematics, and draw inferences from them. Communicate mathematical information symbolically, visually, numerically, and verbally. Use arithmetical, algebraic, and geometric methods to solve problems. Estimate and check answers to mathematical problems in order to determine reasonableness. Solve word problems using quantitative techniques and interpret the results. Apply mathematical/statistical techniques and logical reasoning to produce predictions, identify optima, and make inferences based on a given set of data or quantitative information. Judge the soundness and accuracy of conclusions derived from quantitative information, recognizing that mathematical and statistical methods have limits and discriminating between association and causation. Solve multi-step problems. Apply statistics to evaluate claims and current literature. Demonstrate an understanding of the fundamental issues of statistical inference, including measurement and sampling. from http://avillage.web.virginia.edu/iaas/assess/data/competency/quantitativereasoning/quant07-08.shtm
  9. MCU Draft Quantitative Literacy Rubric Rubric Criteria Interpretation: Ability to explain information presented in mathematical forms (e.g., equations, graphs, diagrams, tables, words). Representation: Ability to convert relevant information into various mathematical forms (e.g., equations, graphs, diagrams, tables, words). Calculation Application / Analysis: Ability to make judgments and draw appropriate conclusions based on the quantitative analysis of data, while recognizing the limits of this analysis. Assumptions: Ability to make and evaluate important assumptions in estimation, modeling, and data analysis. Communication: Expressing quantitative evidence in support of the argument or purpose of the work (in terms of what evidence is used and how it is formatted, presented, and contextualized).
  10. Quantitative Literacy Rubric
  11. Curriculum mapping & assessment
  12. Curriculum Map A matrix of Program Outcomes vs. Courses How do courses contribute to development of a program outcome? What, where and how do they learn? Program Outcomes Broad enough to capture higher order learning (and reusability) Specific enough to drive improvement Developed over a series of courses Competency-based Course Outcome-Assignment-Rubric nodes
  13. Oral Communication Rubric Rubric Criteria Central message: The main point/thesis/"bottom line"/"take-away" of a presentation Delivery technique: Posture, gestures, eye contact, and use of the voice Language: Vocabulary, terminology, and sentence structure. Organization: The grouping and sequencing of ideas and supporting material in a presentation. Supporting material: Explanations, examples, illustrations, statistics, analogies, quotations from relevant authorities, and other kinds of information or analysis that supports the principal ideas of the presentation. Adapted from Association of American Colleges & Universities (AAC&U)
  14. Proposed Curriculum Map: PO#1, Communication Proposed Outcome: Students will communicate a central message in informative or persuasive ways using the appropriate form, channel, structure and style. Reinforce Develop / Mastery Introduce Develop BUS300 Principles of Management BUS498 Business Capstone ID117 (or ID217) Art of Being Human Various courses Assignments: Email response assignment Emerging Theme in Management paper Outcome: Develop written communication skills Rubric: Written Communication Rubric Assignments: Papers, presentations Outcome: Varies Rubrics: Oral and Written Communication Rubrics Assignments: Sales Pitch presentation Elevator speech Outcome: Develop oral communication and presentation skills Rubric: Oral Communication Rubric Assignments: Capstone Final Project Capstone Final Presentation Outcome: Summative Assessment on outcome Rubrics: Oral Communication Rubric Written Communication Rubric
  15. Oral Communication Assignments Develop and deliver a 30 second elevator speech describing why you should be hired to an HR manager; Develop and deliver an effective 2 minute sales pitch for a small business opportunity to a venture capitalist;
  16. Proposed Curriculum Map: PO#2, Analysis Proposed Outcome: Students will use critical thinking to analyze, interpret, represent and communicate quantitative data and information about financial statements, business performance and industry trends. Reinforce Develop / Mastery Introduce Develop BUS350 Principles of Marketing BUS497 Business Capstone Proposal ACCT201 Managerial Accounting BUS380 Corporate Finance Assignments: Case studies Outcome: Analyze, interpret quantitative data to draw conclusions Rubrics: Quantitative Literacy Rubric Assignments: Outcome paper Outcome: Connect experiential learning to coursework Rubrics: Quantitative Literacy Rubric Assignments: Create balance sheet, income statement, cash flow statement Outcome: Analyze, interpret and represent quantitative data Rubrics: Quantitative Literacy Rubric Assignments: Capstone proposal Outcome: Summative Assessment on outcome Rubrics: Quantitative Literacy Rubric
  17. Quantitative Literacy Assignments
  18. Implementation
  19. Learning Analytics Approach Learning Management System is Desire2Learn Using D2L Analytics to measure learning at the level of Program Course Course Offering Student
  20. D2L Structure Program Course Template Course Offering Activity Rubric
  21. Curriculum Map Lake Valley University
  22. Individual Student Progress
  23. Predictive analytics
  24. Risk Quadrant Success index designed to let you visualize and compare key factors – course access, content access, social learning, completion, grades
  25. Sociogram Visualize social network patterns for class discussion forums and identify isolated and connected students
  26. Grades Visualization Innovative grades visualization provide diagnostic insights Interactive drill-down and roll-up to view grade patterns
  27. Summary Competency-based assessment for program outcomes Goals include improving quantitative reasoning and literacy skills Using learning analytics at the program, course and student level Moving toward predictive analytics in the classroom
  28. Questions
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