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© Arizona State University Data Based Decision Making November 2013.

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Presentation on theme: "© Arizona State University Data Based Decision Making November 2013."— Presentation transcript:

1 © Arizona State University Data Based Decision Making November 2013

2 © Arizona State University 2012 ASU Total Headcount Enrollment 73,378 Total Students (59,382 Undergraduates)

3 © Arizona State University Total Student Body 2012 33% Minority (39% of Freshman Class Minority)

4 © Arizona State University ASU Pell Grant Recipients: Ten-year Trend 182% increase over ten years

5 © Arizona State University Analytical Tools eAdvisor Extended to the community colleges: MAPP Possible extension to high schools Retention Dashboard Data analytics embedded in courses

6 © Arizona State University What is eAdvisor? Implemented first at ASU in 2007. Monitors student progress Provides online academic resources Uses personalized tools to move students to graduation Provides early intervention if needed Facilitates choice of major based on interest and career goals Improves advisor efficiency and effectiveness Improves classroom scheduling

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20 Critical Requirements Diagnostic of success in a major Must take early If don’t succeed after two attempts, must change major

21 © Arizona State University Critical Requirement Audit: student and advisor view

22 © Arizona State University Monitor The system notifies advisors and students when a student is not completing critical requirements and is not on track for graduation

23 © Arizona State University Feedback If a student doesn’t meet a critical requirement, the student is notified by email and their status is displayed on their My ASU page. They are required to see an advisor.

24 © Arizona State University Data Deans and administrators have data to track unit as well as institutional progress.

25 © Arizona State University Gains in Student Progress In spite of substantial tuition increases, FTFT r etention increased from 77% for the 2006 cohort to 84% Currently on track to 84% for 2013 Four-year graduation increased 33 % for the 2006 cohort to 45% for the 2009 cohort Six-year graduation rate projected to well exceed 60% for the 2008 cohort

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27 Retention Dashboard o Takes data pertaining to “at-risk” indicators o Continuously updated o Allows advisors to scan all of their students in one screen o Drill down further for students with retention indicators

28 © Arizona State University 1.Finances (Scholarship renewal, to do’s, SAP) 2.Calculated Index (HS GPA, Test Scores) 3.Academic Status Report 4.eAdvisor (off Track 5.My ASU Usage (engagement compared to their cohort) 6. GPA 7. Probation 8. Transcripts Sent (excluding medical, law schools) 9. Enrollment Holds

29 © Arizona State University RETENTION DASHBOARD Identify more risk points

30 © Arizona State University eAdvisor® Outcomes/Savings New Graduates Total Student Cost Savings ASU Cost Savings 2,024 additional graduates $49.5 Million equal to one additional year at ASU $7.3 Million advising costs $6.5-6.9 Million instructional costs

31 © Arizona State University Redesign of General Education: Goals Course design to: Improve student success in the course Improve student success in subsequent courses Improve retention and graduation Promote higher learning – critical thinking and problem solving

32 © Arizona State University Math and General Education Freshmen MATH and: BIO100 The Living World CHM101 Introductory Chemistry PGS101 Introduction to Psychology PHY101 General Physics ECN211 Macroeconomic Principles ECN212 Microeconomic Principles

33 © Arizona State University Design Teams We have progressed to this point because of our partnerships. None of the partners could have achieved this without the full partnership.

34 © Arizona State University Students who earned below a C have a: Students who earned a C or better in have a: 57% retention rate in year one. 85% retention rate in year one. 42% in year two.75% in year two. 31% six-year graduation rate. 64% six-year graduation rate. Importance of Success in Math Classes College Algebra

35 © Arizona State University The data show that students who earned a C in the intro courses did not do well in the follow-up math course, often passing the next class at rates below 40% on the first try. These results led us to the conclusion that the redesign of introductory courses must stress mastery of all subject areas within the courses so as to prepare students for their next class. Math Progression

36 © Arizona State University ASU Math Redesign Adaptive Learning Students are engaged. Instructors answer individual questions. Student attendance is strong.

37 © Arizona State University Success in the courses is based on mastery of subject matter rather than percentage grade points in self paced format:  Mastery of all skills at certain proficiency level  Transparency for students and instructors  Self-paced  Hybrid: Individualized and Interactive environments  Improved critical thinking skills Important Pedagogical Features

38 © Arizona State University http://links.asu.edu/learningpath Personalized Learning

39 © Arizona State University Content Mastery Model

40 © Arizona State University Mapping Content Tagging and organizing existing content Creating new content which doesn’t already exist.

41 © Arizona State University Math Learning Loop

42 © Arizona State University Student Dashboards Scheduled upcoming assignments (based on the objectives contained within those assignments) Skill Development (objectives with which a student is struggling) Knowledge Graph Mastery (the most efficient path through the course)

43 © Arizona State University Faculty Dashboards

44 © Arizona State University Faculty Dashboards

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46 Next Up: Adaptive + Interactive

47 © Arizona State University Learning a Materials Engineering content using tasks that vary across 4 activity modes supports ICAP: 8- 10% improvement per mode (Menekse, Stump, Krause & Chi, in press) READUNDERLINEEXPLAINEXPLAIN JOINTLY


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