How data has evolved at Sinclair Community College in collaboration with SAS. April 16, 2015
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1 Engaging Analytics: How data has evolved at Sinclair Community College in collaboration with SAS April 16, Recognized leaders for the advancement of educational technology planning.
2 Welcome» Nathan Sorensen Strategic IT Procurement Officer, MHEC» Georgia Mariani Principal Product Marketing Manager for Education, SAS» Karl Konsdorf Acting Director, Research, Analytics, and Reporting, Sinclair Community College 2 Recognized leaders for the advancement of educational technology planning.
3 Today s Agenda» Overview of the Midwestern Higher Education Compact (MHEC)» MHEC Contract Benefits» Statistical Software RFP» MHEC Collaboration with SAS» Overview of SAS» Featured Speaker: Sinclair Community College Case Study» Questions and Answers 3 Recognized leaders for the advancement of educational technology planning.
4 Understanding MHEC Recognized leaders for the advancement of educational technology planning. 4
5 MHEC Contract Benefits 5 Recognized leaders for the advancement of educational technology planning.
6 Statistical Software RFP» Specialized Software Academic and Institutional Research» Competitively Sourced RFP Published November 20, Intent to award to SAS Spring 2014» Contract Signed Term November 2014 November 30, 2017 with four one-year renewals (2021) 6 Recognized leaders for the advancement of educational technology planning.
7 Master Price Agreement (Contract No.: MHEC )» License Types Administrative Teaching and Research» Tiered Solutions Tier 1: SAS Foundational Analytics Tier 2: SAS Foundational Analytics with SAS Predictive Analytics Tier 3: SAS Enterprise Analytics» Pricing Number of Installs, and Full Time Enrollment» Plus 60% off SAS Academic Training Points 7 Recognized leaders for the advancement of educational technology planning.
8 Required Contracting Documents» SAS Master License Agreement New SAS customers only» SAS Software Supplement Teaching and Research License Administrative License Services Supplement» Academic Home Use and Remote Access» Contract mstrSAS pdf 8 Recognized leaders for the advancement of educational technology planning.
9 Who is eligible to purchase?» Member States by Compact MHEC IL, IN, IA, KS, MI, MN, MO, NE, ND, OH, SD, WI SREB AL, AR, DE, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN, VA, WV WICHE AK, AZ, CA, CO, HI, ID, MT, NV, NM, OR, UT, WA, WY» Eligible Organizations Higher Education K-12 districts and schools» 9 Recognized leaders for the advancement of educational technology planning.
10 Recognized leaders for the advancement of educational technology planning. 10
11 Software Options 11 Recognized leaders for the advancement of educational technology planning.
12 SAS Contract Highlights 12 Recognized leaders for the advancement of educational technology planning.
13 SAS Website 13 Recognized leaders for the advancement of educational technology planning.
14 Contracts Page 14 Recognized leaders for the advancement of educational technology planning.
15 MHEC Resources Contact Information:» Nathan Sorensen Strategic Information Technology (IT) Procurement Officer Rob Trembath Vice President and General Counsel Mary Roberson Director of Communications & Marketing Recognized leaders for the advancement of educational technology planning.
16 Thank you! Connect with us.» facebook.com/mhec12» youtube.com/user/mhec12» bit.ly/linkedinmhec 16 Recognized leaders for the advancement of educational technology planning.
17 SAS A LEADER IN DATA MANAGEMENT, ANALYTICS AND AGILE BUSINESS INTELLIGENCE 39 Years of Data Management, Analytics and BI Experience Customers in 75k sites across 140 countries 93% of the Fortune 500 companies use SAS 23% reinvestment in R&D SAS is utilized in: 3,000 education institutions worldwide Over 80% of Higher Education Institutions Over 30 State Education Agencies Hundreds of School Districts Copyright 2013, SAS Institute Inc. All rights reserved.
18 SAS Data Management Data access, data integration, data quality, data governance for data warehouses Data Visualization Providing information to the right people at the right time in the right way. Executive dashboards with drill down capability on mobile devices DATA MANAGEMENT Data Exploration Explore data of student behavior that indicates retention issues, correlations or enrollment trends Predictive Analytics Analyze massive data to accurately identify programs likely to produce the most desired results Copyright 2015, SAS Institute Inc. All rights reserved. OPTIMIZATION Arrive at Optimal Decisions Copyright 2013, SAS Institute Inc. All rights reserved.
19 DATA VISUALIZATION DRIVEN BY ANALYTICS Collaboration and Information Sharing MOBILE COLLABORATION APPLICATIONS DESKTOP APPLICATIONS WEB VISUALIZATIONS REPORTS DASHBOARDS COMMENTS INSIGHTS Copyright 2015, SAS Institute Inc. All rights reserved.
20 ENGAGING ANALYTICS: HOW DATA HAS EVOLVED AT SINCLAIR COMMUNITY COLLEGE Karl Konsdorf Acting Director Research, Analytics and Reporting
21 The Setting
22 Sinclair Community College
23 The Characters
24 Manager, Research Admin (PT) Research Analyst Director Database Admin (2) Manager, Analytics & Reporting Data Collection Analyst Research, Analytics and Reporting Organizational Structure Predictive Analytics Research (2) Data Quality Analyst Data Visualization Analyst (TAACCCT) Senior Report Analyst
25 The Problem
26 Water water everywhere, but not a drop to drink!
27 Data data everywhere, but no one to help me think!
28 The Data Pyramid Wisdom Knowledge Information Data
29 The Real Data Pyramid Wisdom Knowledge Information Data
30 Zettabyte is 1,180,591,620,717,411,303,424 Bytes
31 Can I have some Data? Please?
32 Who owns the data?
33
34 What is our enrollment?
35 What is our enrollment? What is our enrollment?
36 What is our enrollment? 23,564
37 Data Quality - Data that is fit for use Free of Defects Accessible Accurate Timely Complete Consistent with other sources Possesses desired features Relevant Comprehensive Proper level of detail Easy to read Easy to interpret
38 Data Quality - Data that is fit for use Free of Defects VW Jetta And Passat Named Tops For Safety Jetta and Passat named "top safety picks" by the Insurance Institute For Highway Safety December 07, 2005 Possesses desired features
39 Data Quality - Data that is fit for use Free of Defects VW Jetta And Passat Named Tops For Safety Jetta and Passat named "top safety picks" by the Insurance Institute For Highway Safety December 07, 2005 Possesses desired features
40 Data Quality - Data that is fit for use Free of Defects VW Jetta And Passat Named Tops For Safety Jetta and Passat named "top safety picks" by the Insurance Institute For Highway Safety December 07, 2005 Possesses desired features
41 The Solution
42
43 Sinclair s Approach to Data Quality Process Key Data Cleansing Information Flow Source Data Quality Data Integrity Information Quality External Balancing Operational Systems Load Process Validation Integration Enterprise Data Store Internal Balancing Data Owner Error Correction Information Product RAR Team Knowledge Worker Error Validation
44 Self-Service Decision Support Operational Teams Business Users BI Tool Metadata Report Analysts Enterprise Data Store
45 Report Evolution Creating Lift Through Tools
46 The Outcomes
47 Areas of Reports Enrollment, Registration Advising Finance Data Warehouse Learning Management System Financial Aid Over 1.5 Terabytes of data in the data warehouse from over 20 operational systems
48 Reporting Benefits Compliance Reporting/Data Submissions Automated Report Distributions Increased Visibility of Data/Information
49 Report Usage 3,000 2,500 2,350 2,000 1,750 1,500 Users 1,000 1, Reports
50 Analytics have been important as well Applicant Admission to Registration Technology Resource Utilization and Capacity Distance Learning Success Rates Text Mining Visual Analytics
51 Applicant Analysis Who s eating our lunch?
52 Applicant Analysis Who s eating our lunch?
53 Applicant Analysis Who s eating our lunch?
54 Applicant Analysis Who s eating our lunch?
55 Applicant Analysis Who s eating our lunch? So what does it all mean? Placement tests are the most influential factor Applicants who haven t taken a placement test represent a large opportunity for servicing the community Financial aid is an important factor Applicants who place into remedial math represent another at-risk segment We are not losing applicants to the competition, but it isn t likely to remain that way.
56 Analytics in Information Technology The purpose of the exercise is to identify the relationship between the Angel Application usage and database performance to predict the performance of the Angel database during high usage times thus avoiding database application failure in future enrollment terms
57 Analytics in Information Technology Predicted CPU Usage 1,800 1,600 1,400 1,200 1, Session % 6.7% 9.6% 28.6% 51.7% 59.5% 90.7% 96.2%
58 Analytics in Information Technology Our current hardware would not support predicted online enrollment in Winter The analytics enabled the purchase and installation of new hardware for online learning in the Summer Fall 2009 online learning had very few technical issues. No Performance issues as of Fall 2013.
59 Online Learning Success Rates Online Non-Online 70.66% 69.53% 69.99% 69.10% 65.22% 63.41% 64.60% 63.28% Fall 2007 Fall 2008 Fall 2009 Fall 2010
60 Online Learning Success Rate Fall 2010 Top 10 Courses Success Rates Online Non-Online College-wide General Comparison BIS % 56.14% 57.99% Online is better ENG % 71.74% 71.45% Online is lower COM % 63.87% 62.97% Online is lower ENG % 64.03% 62.52% Online is lower MAT % 48.13% 45.61% Online is lower HIM % 78.25% 79.74% Online is better ALH % 74.34% 74.23% About the same SCC % 62.07% 61.98% About the same ALH % 69.12% 70.17% Online is better SOC % 62.32% 60.53% Online is lower
61 Previous Studies December 2002 Key Findings GPA of 2.00 or better more likely to succeed Placement tests scores indicated success Enrollment in DEV also indicated [non] success Other factors such as demographics, financial aid, and previous online success had little influence Results Restriction of GPA of 2.0 implemented
62 Previous Studies December 2002 Study 1 Percent A, B, or C Study 2 Percent A, B, or C GPA of less than % 31.3% GPA 2.0 or better 79.4% 80.9% Results Restriction of GPA of 2.0 implemented
63 Previous Studies April 2008 Key Findings GPA still the best predictor of success Writing placement tests scores next best indicator Other factors such as demographics, financial aid, and previous online success had little influence on the model. Results No actions taken
64 New Study April 2011 Registration Data Demographics, Enrollment Behaviors, Success Data Online Student Survey Student Online Learning Experience Usage Data from the Online Learning Management System Number of Logins, Session Length, etc.. Online Student Success Course Faculty Online Course Certification
65 Analysis Techniques SAS Enterprise Miner Decision Tree Modeling Good technique for classifying binary outcomes Varied parameters to select the number of branches Logistic Regression Stepwise Regression Required imputed survey data Compared Modeling Techniques using misclassification rates
66 Modeling Results Decision Tree Model was chosen Misclassification rates was less than 10% 90% of the observations were correctly classified. Most important variable was.....
67 Online Learning Student Success The number of times the student login and viewed the online course in the learning management system! Students have greater chance of success if they connect with the online learning management systems at least 20 times during the learning period.
68 Online Learning Success Results and Next Steps Created a process to monitor the student course login interval. If the login interval in more than 3 days, then an in automatically generated and sent to the faculty indicating that the specific student hasn t logged in to the system.
69 Making Sense of Text Arocdnicg to rsceearch at Cmabrigde Uinervtisy, it deosn t mttaer in waht oredr the ltteers in a wrod are, the olny iprmoatnt tihng is taht the frist and lsat ltteer are in the rghit pcale. The rset can be a toatl mses and you can sitll raed it wouthit pobelrm. Tihs is buseace the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe.
70 Using Text Analytics on Jobs Data Background Check [eligibility, investigation, subject, select] Education [degree, bachelor, engineering, field] Ability to Obtain Security Clearance [clearance, secret, obtain, security, top] Communication Skills [skill, communication, written] Possession of Security Clearance [sci, ts, clearance, active, current] Wright Patterson AFB [afb, Wright, Patterson, headquarters] Air Force [air, force, center, command] Program Management [Program Management Professional, Acquisition Professional Development Program] Experience [experience, year, related working] Intelligence Analyst [data, intelligence, analyst, report, multiple source, geospatial, image] Software Developer [software, development, application, Microsoft, java] Job Requirement: Skills, Knowledge, and Abilities [parsed from "desired skills:" or "you must minimally meet the knowledge, skills, and abilities listed below"] Job Requirement: System Design and Development [system, requirement, design, test] Management [management, customer, project, data, program] Job Requirement: Responsibilities [responsibility, function, project] Job Location [join, Ohio, seek, individual] Desirable Qualifications [parsed from " degree preferred" or " a plus"] US Citizen [position, citizenship, required] Acquisitions [DOD, acquisition, procedure, knowledge] Network Administrator [network, configuration, system, management] Job Requirement: Analysis [analysis, responsibility, include, perform] Team Player [work, ability, environment, team] Health/Industrial Hygiene [health, occupational, risk, hygiene] Technical [technical, project, discipline] Requisition ID [artifact of the text: "Requisition ID: ########]
71 Using Text Analytics on Survey Data Favorite Thing about ANGEL (of 281 responses) n % of respondents Communicate with Students % Easy to Navigate % Grading % Course Maintenance (attendance, gradebook, syllabus) % Repository for Course Materials % % Easy to Upload Files % Discussion Tools % Drop Box % User Friendly %
72 Performance Based Funding * Access Category Weights Applied ADULT (over age 25 at time of enrollment) LOW-INCOME, Pell Eligible (ever in college career) MINORITY (African American, Hispanic, Native American)
73 Performance Based Funding Dashboard
74 Average Class Size Manage Classroom Efficiency Perform what-if analysis. Modify variables like course meeting date/time or enrollment levels to see the impact on Average Class Size
75 Average Class Size
76 The Sequel Text Analytics Sentiment and Polarity analysis of semi-structured data Information Visualization Course Scheduling Optimizations Offering the right course at the right time in the right format at the right location using historical enrollment, degree audit, and My Academic Plan data.
77 Questions? Karl Konsdorf Acting Director Research, Analytics and Reporting Learn more about our successes at
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