1. Rethinking Developmental Mathematics, High School Math Courses, and College Readiness Advisory Team for Quantitative Reasoning HS and HE courses Charles A. Dana Center The University of Texas at Austin September 23, 2013 Advisory Team for Quantitative Reasoning HS and HE courses Charles A. Dana Center The University of Texas at Austin September 23, 2013

2. About the Dana Center 2 The Charles A. Dana Center at The University of Texas at Austin works with our nation’s education systems to ensure that every student leaves school prepared for success in postsecondary education and the contemporary workplace. Our work, based on research and two decades of experience, focuses on K–16 mathematics and science education with an emphasis on strategies for improving student engagement, motivation, persistence, and achievement. We develop innovative curricula, tools, protocols, and instructional supports and deliver powerful instructional and leadership development.

3. There are serious disconnects HS  Higher Ed 3 Source: Complete College America

4. In mathematics, developmental education is even more common  67% of community college students are referred to one or more developmental math courses (and 80%+ in some systems) 4 Developmental Education Pipeline at Public Two-Year Colleges Fall 2007 Cohort Cohort total: 99,097 Of students below state standard in mathematics 100 Enrolled in developmental education83 Achieved college readiness39 Attempted first college-level course21 Successfully completed first college-level course15 Source: Texas Higher Education Coordinating Board

5. Long developmental sequences increase attrition Two-year cohort data from a typical large community college in Texas

6. There are serious disconnects HS  Higher Ed High school preparation often has not led to college readiness… “Well, I think the biggest thing for them is, here, they’ve graduated from high school but they come and take our placement test and they’re still in pre-college … math and they don’t understand that if they stop taking math in their sophomore year that, you know, they don’t get it… and I think the sad thing is that they say…‘no one told me that I should be taking math all the way through’.” -community college advisor (Stanford University Project report, 2004) 6

7. If high school had demanded more, graduates would have worked harder 82% 80% Would have worked harder Strongly feel I would have worked harder Wouldn ’ t have worked harder High school graduates who went to college High school graduates who did not go to college Source: Peter D. Hart Research Associates/Public Opinion Strategies, Rising to the Challenge: Are High School Graduates Prepared for College and Work? prepared for Achieve, Inc., 2005. Achieve, 2005

8. Majority of graduates would have taken harder courses Knowing what you know today about the expectations of college/work … Would have taken more challenging courses in: Would have taken more challenging courses in at least one area Math Science English Source: Peter D. Hart Research Associates/Public Opinion Strategies, Rising to the Challenge: Are High School Graduates Prepared for College and Work? prepared for Achieve, Inc., 2005. Achieve, 2005

9. The Dana Center is working on a multi-pronged approach to smooth transition HS  Higher Ed  Redesign higher education remediation at scale: the Dana Center’s New Mathways Project  Support districts’ high school standards implementation to ensure college-ready mathematics for all students (CCSSM, TEKS)  Introduce a new breed of 4 th -year high school mathematics courses that provide an additional pathway into college readiness  Work in the policy sphere to ensure students can transition successfully 9

10. Audience: Students graduating from high school should be college ready 10 HS HE math College ready

11. The Dana Center is working on a multi-pronged approach to smooth transition HS  Higher Ed  Redesign higher education remediation at scale: the Dana Center’s New Mathways Project  Support districts’ high school standards implementation to ensure college-ready mathematics for all students (CCSSM, TEKS)  Introduce a new breed of 4th-year high school mathematics courses that provide an additional pathway into college readiness  Work in the policy sphere to ensure students can transition successfully 11

12. NMP audience: Students directly from high school and not college ready in mathematics 12 HS HE math NMP Not college ready College ready

13. NMP audience: Students not directly from high school and not college ready in mathematics 13 HE math NMP Not college ready Not directly from HS College ready

14. New Mathways Project: Setting the agenda Charles A. Dana Center at the University of Texas at Austin  Led development of original curricula for pathways to college success—Statway and Quantway—in partnership with the Carnegie Foundation Texas Association of Community Colleges  Represents all 50 community college systems in Texas  Represents the interests of community colleges in state policymaking and budgeting A unique partnership of colleges setting the agenda for reform  addresses issues from the classroom to state policy  allows for collaboration and input from people at all levels of the system 14

15. The Principles of the NMP Model A systemic approach to improving student success and completion by reforming developmental and gateway mathematics: 1.Multiple pathways with relevant and challenging mathematics content aligned to specific fields of study 1.Acceleration that allows students to complete a college-level math course within 1 year—more quickly than in the traditional developmental math sequence. 1.Intentional use of strategies to help students develop skills as learners and experience college success 1.Curriculum design and pedagogy based on proven practice 15

16. The NMP Courses 16

17. The NMP Courses 17

18. The Dana Center is working on a multi-pronged approach to smooth transition HS  Higher Ed  Redesign higher education remediation at scale: the Dana Center’s New Mathways Project  Support districts’ high school standards implementation to ensure college-ready mathematics for all students (CCSSM, TEKS)  Introduce a new breed of 4 th -year high school mathematics courses that provide an additional pathway into college readiness  Work in the policy sphere to ensure students can transition successfully 18

19. Audience: Students graduating from high school should be college ready 19 HS HE math College ready

20. Audience: Students graduating from high school with college credit are, de facto, college ready 20 HS HE math

21. Proposed pathways to readiness for college math 21 Alg IGeomAlg IIGrades 6, 7, 8 PrecalAP Stat AMDM/ AQR (and other 4 th yr ) QR (and other dual enroll) College mathematics Successful completion certifies college readiness in mathematics

22. If the goal of college readiness is for students to succeed in college-level courses, students need access to— and experience in—college-level courses. 22 Quantitative Reasoning: A proposed new dual enrollment course

23.  Built using similar outcomes as college-level QR courses  Emphasizes content meaningful to students’ professional, civic, and personal lives  Develops skills in interpreting, understanding, and using quantitative information  Teaches algebraic and modeling skills through quantitative literacy lens  Emphasizes critical thinking and strategic reasoning  Designed for non-STEM intending students 23 Quantitative Reasoning: A proposed new dual enrollment course

24. Quantitative Reasoning from a National Perspective Eric Gaze, Ph.D. President-elect, National Numeracy Network Bowdoin College, Brunswick, Maine 24 Quantitative Reasoning: What is it?

25. The Dana Center is working on a multi-pronged approach to smooth transition HS  Higher Ed  Redesign higher education remediation at scale: the Dana Center’s New Mathways Project  Support districts’ high school standards implementation to ensure college-ready mathematics for all students (CCSSM, TEKS)  Introduce a new breed of 4 th -year high school mathematics courses that provide an additional pathway into college readiness  Work in the policy sphere to ensure students can transition successfully 25

26. The Dana Center is working on a multi-pronged approach to smooth transition HS  Higher Ed What are the policy implications we need to consider? 26

27. Contact Information  General information about the Dana Center: www.utdanacenter.org www.utdanacenter.org  Higher Education work: www.utdanacenter.org/higher- education/ www.utdanacenter.org/higher- education/  To receive monthly updates about the NMP, contact us at: mathways@austin.utexas.edumathways@austin.utexas.edu  Staff contacts:  Susan Hudson Hull (mathematics): shhull@austin.utexas.edushhull@austin.utexas.edu  Amy Getz (NMP general project issues): getz_a@austin.utexas.edugetz_a@austin.utexas.edu  Connie Richardson (NMP QR development): cjrichardson@austin.utexas.educjrichardson@austin.utexas.edu  Kathi Cook (HS course program): klcook@austin.utexas.eduklcook@austin.utexas.edu  Lindsay Fitzpatrick (policy issues): lindsay.fitzpatrick@austin.utexas.edulindsay.fitzpatrick@austin.utexas.edu 27

28. 28 Quantitative Reasoning Outcome Planning

29. 29 High School Course Descriptors Proportional reasoning, ratios, rates, percents, vectors, matrices, network models, mathematical models, voting, selection, geometric representations, inaccessible distances, probability, expected value, analysis of statistics, conduct statistical studies, function models, multiple representations, reasoning, evaluation, communication, connections, vectors, matrices, polynomials, rational expressions, equations, inequalities, trigonometric functions, similarity, right triangles, measurement, dimension, geometric modeling, statistics, probability, interpreting data, making inferences, justifying conclusions, decision-making, numerical reasoning, probability, statistical analysis, finance, mathematical selection, modeling, algebra, geometry, trigonometry, discrete, analyze real-world numerical data, risk, return, analyze statistical summaries, make decisions, conduct statistical studies, communication, ranking, selection, modeling, predicting, evaluating, modeling change, finance, networks, geometric modeling

30. 30 High School Course Descriptors

31. 31 Higher Ed Course Descriptors

32. 32 Combined Course Descriptors

33. 33 Occurrences > 5 times4 times3 times2 times statisticsdatacommunicationanalysisinterpretingexponential probabilityfinanceconsumeranalyzelinearfunctions modelinggeometricmathematicsapplicationslogicinequalities modelsgeometrymeasurementchangemakinginformation reasoningmathematicalpercentagesconclusionsmatricesproportion statisticalratesconductnumberstudies theoryrepresentationsdecisionofsystems selectionequationsprocessingvectors

34. 34 Single Occurrences algebradiscretefunctionnetworkrealtables centraldistancesgamenetworksreturntendency citizenshipdivisiongraphnumerationrighttriangles codingdrawinginaccessiblepatternsrisktrigonometric combinatoricseffectiveinferencespolynomialsscalestrigonometry connectionsevaluatinginformedpredictingscienceunits conversionsevaluationinterestproportionalsetvalue countingevidencejustifyingproportionalitysetsvoting decision-makingexpectedmakerankingsimilarityworld decisionsexpressionsmanagementratiosophisticated descriptivefairmeasuresrationalsummaries dimension frommultipleratiossymmetry

35. 35 Statistics The student makes decisions based on understanding, analysis, and critique of reported statistical information and statistical summaries. The student is expected to: (A)Identify limitations or lack of information in studies reporting statistical information, including when studies are reported in condensed form; (B)Interpret and compare the results of polls, given a margin of error; (C)Identify uses and misuses of statistical analyses in studies reporting statistics or using statistics to justify particular conclusions; and (D)Describe strengths and weaknesses of sampling techniques, data, and graphical displays, and interpretations of summary statistics or other results appearing in a study.

36. 36 Task What are the “buckets”? Learning outcomes Performance descriptors