Datapalooza
Download
1 / 100

DataPalooza - PowerPoint PPT Presentation


  • 107 Views
  • Uploaded on

DataPalooza. Institutional Effectiveness Day August 24, 2011. DataPalooza. ??????????????. datapalooza an extraordinary or unusual Institutional Effectiveness Day data thing, person, or event. Introduction. Continuous Improvement of Student Learning Outcomes.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' DataPalooza' - ramya


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Datapalooza

DataPalooza

Institutional Effectiveness Day

August 24, 2011


Datapalooza1
DataPalooza

??????????????

datapalooza

an extraordinary or unusual Institutional Effectiveness Daydatathing, person, or event.

DataPalooza IE Day 2011


Introduction
Introduction

Continuous Improvement of Student Learning Outcomes

Developing and refining teaching and learning practices

Brief excerpts from full reports

Quality Enhancement Plan (QEP) Topics

  • Student Analytics & Research

  • Some facts about our students

  • Key benchmark summaries

    • SENSE, CCSSE, NCCBP, ETS

  • General Education Measures

    • ETS Proficiency Profile

  • Academic Success Centers (ASC)

  • Student Life Skills (SLS)

  • What do our students think of us?

    • SENSE, CCSSE

  • Special projects

  • Predictive analytics

DataPalooza IE Day 2011


Student Analytics & Research

DataPalooza IE Day 2011


Student analytics research
Student Analytics & Research

  • What We Do

    • Support the mission, vision, values, and goals of the college by developing and improving data-driven decision-making capabilities and capacity

    • Perform primary and secondary research related to student learning and performance

    • Lead key institutional research projects involving standardized and specialized surveys, examinations, assessments and evaluations

    • Collaborate closely with others across the college to support key projects and initiatives

    • Develop and support research projects and initiatives related to institutional effectiveness and regional accreditation

    • Utilize quantitative and qualitative data mining and predictive analytic tools and strategies to more completely understand our institutional data and better serve our students

  • Our Location

    • Administrative offices, 2nd floor, 501 W. State St., Jacksonville, FL

  • Our web site: http://www.fccj.org/sar/

DataPalooza IE Day 2011


Student Characteristics

DataPalooza IE Day 2011


Who are our students some quick facts
Who Are Our Students?Some Quick Facts

As of fall 2010 term, all students, all programs

44% male, 56% female

Average student age was 29 years

Median student age was 25 years

73% part-time, 27% full-time

75% work

DataPalooza IE Day 2011






College prep students
College Prep Students

aka: remedial

Of 2,595 First Time in College Students fall 2010

  • 69.60% placed into > 1 college prep courses

  • 46% male and 54% female

  • 70% were between 17 and 24 years old

DataPalooza IE Day 2011


aka: baccalaureate

n=1,360

DataPalooza IE Day 2011


DataPalooza IE Day 2011



Key Benchmark Comparisons

DataPalooza IE Day 2011


Benchmarks
Benchmarks

Community College Survey of Student Engagement (CCSSE)

National Community College Benchmark Project (NCCBP)

Survey of Entering Student Engagement (SENSE)

DataPalooza IE Day 2011


Ccsse benchmarks 2010
CCSSE Benchmarks2010

Mission

improvement of education in the south through accreditation

Compare participating Florida Institutions to other U.S. and SACS regional colleges

Benchmarks

  • Active and Collaborative Learning

  • Student Effort

  • Academic Challenge

  • Student-Faculty Interaction

  • Support for Learners

DataPalooza IE Day 2011


Highlights
Highlights

Florida State College at Jacksonville

  • Exceeded four of five CCSSE benchmarks

  • Exceeded or equaled Florida comparison group, U.S. Extra Large cohort, and SACS regional colleges on

    • Active and Collaborative Learning

    • Student Effort

    • Student-Faculty Interaction

  • Featured in Florida College System Zoom Report (2010-05, Dec. 2010) with 10 other FL institutions

DataPalooza IE Day 2011


Benchmark comparisons
Benchmark Comparisons

DataPalooza IE Day 2011


National community college benchmark project nccbp 2011
National Community College Benchmark Project (NCCBP)2011

Established to standardize nation-wide benchmark reporting

Since 2004, 391 colleges have participated

280 community colleges participated in 2011

Florida State College College at Jacksonville has participated in NCCBP since 2009

DataPalooza IE Day 2011


ABC/ABCDFW

ABC/ABCDF

DataPalooza IE Day 2011


Survey of entering student engagement sense benchmarks
Survey of Entering Student Engagement (SENSE) Benchmarks

Early Connections (EARLYCON)

When students describe their early college experiences, they typically reflect on occasions when they felt discouraged or thought about dropping out. Their reasons for persisting almost always include one common element: a strong, early connection to someone at the college.

High Expectations and Aspirations (HIEXPECT)

When entering students perceive clear, high expectations from college staff and faculty, they are more likely to understand what it takes to be successful and adopt behaviors that lead to achievement. Students then often rise to meet expectations, making it more likely that they will attain their goals.

Clear Academic Plan and Pathway (ACADPLAN)

Students are more likely to persist if they not only are advised about what courses to take, but also are helped to set academic goals and to create a plan for achieving them.

Effective Track to College Readiness (COLLREAD)

Nationally, more than six in 10 entering community college students are underprepared for college-level work. Thus, significant improvements in student success will hinge upon effective assessment, placement of students into appropriate courses, and implementation of effective strategies to ensure that students build academic skills and receive needed support.

Engaged Learning (ENGAGLRN)

Instructional approaches that foster engaged learning are critical for student success. Because most community college students attend college part-time, and most also must find ways to balance their studies with work and family responsibilities, the most effective learning experiences will be those the college intentionally designs.

Academic and Social Support Network (ACSOCSUP)

Students benefit from having a personal network that enables them to obtain information about college services, along with the academic and social support critical to student success. Because entering students often don’t know what they don’t know, colleges must purposefully create those networks.

DataPalooza IE Day 2011



General Education Measures

ETS Proficiency Profile

DataPalooza IE Day 2011


Introduction1
Introduction

ntotal = 2,822

  • 4-year longitudinal summary (including online)

    • 2008 (n = 685)

    • 2009 (n = 786)

    • 2010 (n = 608)

    • 2011 paper (n=529)

    • 2011 unproctored online (n=214) upper division

  • Total score possible range is 400 to 500

  • Skill/context based sub score possible range 100 to 130

  • Benchmark comparisons

    • Associate’s Colleges freshmen and sophomores

DataPalooza IE Day 2011


Range: 400 - 500

DataPalooza IE Day 2011


ETS Proficiency Profile

2008 – 2011 Combined

All Years Combined

Mean = 434.56

Std. Dev.= 16.26

N = 2,822

425 - 430

DataPalooza IE Day 2011


Range: 100 - 130

DataPalooza IE Day 2011


1% imagination

DataPalooza IE Day 2011



Total Scale Score Differences by Age Groups: the two youngest differ from the two oldest. The break occurs between 21 and 22 years.

DataPalooza IE Day 2011


ETS Mean Total Score by Major youngest differ from the two oldest. The break occurs between 21 and 22 years.

ANOVA p < 0.000

F = 4.672, df = 35

Majors with <2 selections excluded

DataPalooza IE Day 2011


n = 672 youngest differ from the two oldest. The break occurs between 21 and 22 years.

DataPalooza IE Day 2011


Five General Education Areas youngest differ from the two oldest. The break occurs between 21 and 22 years.

n = 649

DataPalooza IE Day 2011


n = 660 youngest differ from the two oldest. The break occurs between 21 and 22 years.

DataPalooza IE Day 2011


Academic Success Centers youngest differ from the two oldest. The break occurs between 21 and 22 years.

DataPalooza IE Day 2011


Academic success centers asc
Academic Success Centers (ASC) youngest differ from the two oldest. The break occurs between 21 and 22 years.

Student Success and Retention Analyses

  • fall 2009 to fall 2010 based on 16,530 academic history grades; compared results by course, campus, term, and other factors

  • fall 2010 and spring 2011 for highest level remedial Reading (REA0010), Writing (ENC0021), Math (MAT0024, MAT1033) courses based on 13,231 academic history grades

  • ASC Survey of Student Experience

  • DataPalooza IE Day 2011


    MAT0024 FALL 2010 - SPRING 2011 COMBINED STUDENT SUCCESS BY LOCATION (n = 5,409)

    0.78

    Grand Mean = 0.55

    0.41

    DataPalooza IE Day 2011


    MAT1033 FALL 2010 - SPRING 2011 COMBINED STUDENT SUCCESS BY LOCATION (n = 4,642)

    0.93

    Grand Mean = 0.62

    0.53

    DataPalooza IE Day 2011


    Asc survey of student experience
    ASC Survey of Student Experience LOCATION (n = 4,642)

    • Gauges student perceptions of ASC effectiveness, quality, and helpfulness

      • Mathematics (MAT0002, MAT0024, MAT1033)

      • Writing (ENC0001, ENC0021)

      • Reading (REA0006, REA0008, REA0010)

    • Administered fall 2010 (n=502) and spring 2011 (n=363)

      • A slightly modified version is now being administered (summer 2011)

    • Includes “open text” written comments

      • Please share comments about your experience as an Academic Success Center student.

    DataPalooza IE Day 2011


    n = 285 LOCATION (n = 4,642)

    DataPalooza IE Day 2011


    n = 166 LOCATION (n = 4,642)

    DataPalooza IE Day 2011


    n = 94 LOCATION (n = 4,642)

    DataPalooza IE Day 2011


    ASC Survey Student Written Comments Text Miner Model Summary LOCATION (n = 4,642)

    Please share comments about your experience as an Academic Success Center student.

    Respondents

    Shared Responses

    fall-spring combined (n=163)

    DataPalooza IE Day 2011


    Student likes dislikes asc student written comments
    Student Likes/Dislikes LOCATION (n = 4,642)ASC Student Written Comments

    I love the new computer and lab area it makes it easier to work on my homework during class and in between classes. [math]

    The Academic Success Center is helpful. I work on computer lab assignments. I get the work done. [reading]

    I love working in the ASC. It is very helpful. I am going to be sad next semester when I cannot utilize the ASC anymore. :( [writing]

    DataPalooza IE Day 2011


    Learning technology asc student written comments
    Learning Technology LOCATION (n = 4,642)ASC Student Written Comments

    My only issue with this type course is that used books are not available. I have yet to open the text that I paid over $200.00 for. The software and instructor have more than adequately prepared me for everything I need in the course. [math]

    It's a great alternative for people who don't have the equipment at their disposal. [writing]

    DataPalooza IE Day 2011


    Instructor asc student written comments
    Instructor LOCATION (n = 4,642)ASC Student Written Comments

    My experience as a student in the Academic Success Center has been amazing. All of the staff (including my instructor) in the math labs were incredible. They had a lot of patience when explaining the concepts and rules while they all understood and accepted that not everyone finds math easy to do. I never felt "stupid" and even times when I didn't want to have to ask yet another question, the insight of the staff would notice I was struggling, pull up a chair and walk me through. I would recommend Academic Success Center to all students, and I am grateful that such an opportunity is available. This was the first time in many, many years that I could leave a math class with a smile. Thank you. [math]

    DataPalooza IE Day 2011


    Learning asc student written comments
    Learning LOCATION (n = 4,642)ASC Student Written Comments

    I learned how to do things at my own pace (and) also how important it is to check my work. [math]

    I have enjoyed my time working in (the ASC). It has been a great learning experience…also my instructor made this class seem simple and I understood all of what was taught in class. [writing]

    DataPalooza IE Day 2011


    Student Life Skills (SLS) LOCATION (n = 4,642)

    DataPalooza IE Day 2011


    Student life skills sls
    Student Life Skills (SLS) LOCATION (n = 4,642)

    Compared subsequent overall student success and retention for June 2009 High School graduates and June 2010 graduates based on whether they completed SLS1101 using 17,822 (academic history) grades (ABCDFFNW)

    Found June 2010 Grads who completed SLS1101 have an overall Success Rate of 82.6% . This is significantly higher than the 76% rate for both June 2009 and June 2010 grads who did not complete SLS1101

    DataPalooza IE Day 2011


    ANOVA SIG. p < .0001 LOCATION (n = 4,642)

    N = 8,512

    DataPalooza IE Day 2011


    What Our Students Think About Us: Student Engagement and Satisfaction

    DataPalooza IE Day 2011


    What our students think about us
    What Our Students Think About Us Satisfaction

    • Entering Students

      • Survey of Entering Student Engagement (SENSE)

    • Continuing Students

      • Community College Survey of Student Engagement (CCSSE)

    • Graduating Students

      • Spring 2011 Graduate Survey

    DataPalooza IE Day 2011


    SENSE Satisfaction

    Item 18 Group (21 items)

    DataPalooza IE Day 2011


    SACS FSCJ Satisfaction

    11 9

    SACS Colleges

    DataPalooza IE Day 2011


    Ccsse multi year longitudinal analysis
    CCSSE Multi-Year Longitudinal Analysis Satisfaction

    CCSSE

    • Disaggregated five-years of results

      • 2006 - 2010

    • Compared and summarized year-to-year changes in all item means

    • Trended demographics for five-year period

    DataPalooza IE Day 2011



    Used the Internet or instant messaging to work on an assignment

    In your experiences at this college during the current school year, about how often have you done each of the following?

    Scale: 1=Never, 2=Sometimes, 3=Often, 4=Very often

    DataPalooza IE Day 2011


    Worked harder than you thought you could to meet an instructor's standards or expectations

    In your experiences at this college during the current school year, about how often have you done each of the following?

    Scale: 1=Never, 2=Sometimes, 3=Often, 4=Very often

    DataPalooza IE Day 2011


    Skipped class instructor's standards or expectations

    In your experiences at this college during the current school year, about how often have you done each of the following?

    Scale: 1=Never, 2=Sometimes, 3=Often, 4=Very often

    DataPalooza IE Day 2011


    Number of assigned textbooks, manuals, books, or book-length packs of course readings

    During the current school year, about how much reading and writing have you done at this college?

    Scale: 1=None, 2=Between 1 and 4 , 3=Between 5 and 10, 4=Between 11 and 20 , 5=More than 20

    DataPalooza IE Day 2011


    Encouraging you to spend significant amounts of time studying

    How much does this college emphasize each of the following?

    Scale: 1=Very little, 2=Some, 3=Quite a bit, 4=Very much

    DataPalooza IE Day 2011


    Acquiring a broad general education studying

    How much has YOUR EXPERIENCE AT THIS COLLEGE contributed to your knowledge, skills, and personal development in the following areas?

    Scale: 1=Very little, 2=Some, 3=Quite a bit, 4=Very much

    DataPalooza IE Day 2011


    Thinking critically and analytically studying

    How much has YOUR EXPERIENCE AT THIS COLLEGE contributed to your knowledge, skills, and personal development in the following areas?

    Scale: 1=Very little, 2=Some, 3=Quite a bit, 4=Very much

    DataPalooza IE Day 2011


    Solving numerical problems studying

    How much has YOUR EXPERIENCE AT THIS COLLEGE contributed to your knowledge, skills, and personal development in the following areas?

    Scale: 1=Very little, 2=Some, 3=Quite a bit, 4=Very much

    DataPalooza IE Day 2011


    GRADUATE SURVEY studying

    DataPalooza IE Day 2011



    Special Projects studying

    DataPalooza IE Day 2011


    Specialized analyses reports and services
    Specialized Analyses, Reports, and Services studying

    • JIRA Request System

      • Online service request system

      • Access from the Student Analytics site

    • Surveys

      • Transient Student, College Survey of Student Cost, Applied But Not Enrolled (ABNER), Syllabus Survey, Graduates, many others

    • Iowa State University Sirius Effectiveness

    • Economic Impact Study of the college

    • Demographic Research Bartram Completion Center

    • Standardized Exam Score Analysis

    • Campus-based projects and evaluations

    • Successful Student Analysis

    DataPalooza IE Day 2011


    College Survey of Student Cost studying

    DataPalooza IE Day 2011


    Characteristics of successful students

    Successful Students studying

    Characteristics of Successful Students

    Potential Students

    Applicants

    Enrolled Students

    Individual characteristics, motivations, abilities, behaviors

    • Life Circumstances

    • Financial Resources

    • Support systems

    DataPalooza IE Day 2011


    Characteristics of successful students1
    Characteristics of Successful Students studying

    Potential Students

    Applied But Not Enrolled

    (ABNER)

    Applicants

    Defer Enrollment

    Enrolled Students

    Attend Different Institution

    • Successful Students

    Stop Out

    Drop Out

    Change Plans

    Move

    • U.S. Federal (IPEDS)

    • Common Education Data Standards

      • Death

      • Disability (total & permanent)

      • Armed Forces

      • Foreign Aid Service

      • Official Church Mission Service

    Go to work

    Transfer Out/

    Change Colleges

    DataPalooza IE Day 2011


    Characteristics of successful students2
    Characteristics of Successful Students studying

    Potential Students

    Spring 2011

    Graduation Term

    Fall 2008

    Matriculation Term

    Applicants

    Compare characteristics and attributes of successful spring 2011 graduates to non-graduates from fall 2008 peer cohort

    5,879 Applied fall ‘08

    • High School New

      • Degree seeking

      • No prior course history in Orion

    • Award Types

      • Associate of Arts

      • Associate of Science

      • Associate of Ap. Sci.

      • Technical Certificates

    • Demographic Characteristics

    • Received ANY financial aid

    • College Prep Requirements

    • SLS completion, Main Campus

    • Term-Term Retention/Graduation

    • Course Retention/Success

    • Course Delivery Methods

    57%

    2,540 ABNER Spring 2011

    Enrolled Students

    3,339 found enrolled as of Spring 2011

    • 8 Terms Total

      • 6 Terms = 150% completion time

    • Courses per term

    • College credit hours per term

    • Benchmark rates 25%, 50%, 75%,

    • ~3,000 Successful Students

    • Spring 2011

    DataPalooza IE Day 2011


    Applied But Not Enrolled studying

    (ABNER)

    29%

    54%

    83%

    Note—Partial results online only survey. A mail-out (postcard) version is currently being deployed.

    DataPalooza IE Day 2011


    Health education systems inc hesi national council licensure examination nclex

    Standardized Exams studying

    Health Education Systems, Inc (HESI)National Council Licensure Examination (NCLEX)

    students scoring < 717.5 on HESI have a 50% NCLEX pass rate, but those scoring >717.5 have a 88% pass rate

    726 HESI Mean

    838 HESI Mean

    DataPalooza IE Day 2011


    Student course withdrawals
    Student Course Withdrawals studying

    When students enroll in, but fail to complete a course, it costs the student and the state money, reduces available classroom space, and increases the amount of time for the student to complete their degree. Clearly, many withdrawals are necessary for personal and academic reasons, but when withdrawals become excessive they pose a significant burden on the student, the college, and the state.

    (Florida Department of Education, March, 2011, p. 1)

    DataPalooza IE Day 2011


    Student course withdrawal text mining project in a nutshell
    Student Course Withdrawal Text Mining studyingProject in a Nutshell

    • Analyzed 616 withdrawal comments from fall 2010 term

      • Generated initial model containing 11 categories (nodes)

    • Used model to analyze 679 comments from spring 2011 term

    • Compared results fall to spring

    • Combined and analyzed all (n=1,295) results together

    • Generated final model

      • Exported model results for additional quantitative analysis

        • Record coding correlations between categories

        • Record groupings using Hierarchical Cluster Analysis (HCA), Principal Components Analysis (PCA), Multiple Correspondence Analysis (MCORA)

    • Recommend further research and collaboration

      • Text miner definitions, libraries, models, coding schemas between institutions

    • Practical results: add categorical selection choices to Connections for known high-frequency course withdrawal reasons

    DataPalooza IE Day 2011


    Student Course Withdrawals Category Web Diagram studyingFinal Model

    Figure 1. Category web diagram of all responses from fall 2010 data. A total of 512 responses were categorized into 11 nodes. The overall model categorized 96.1% of cases. (AIR Forum, Presentation, Toronto, Ontario, May 2011)

    DataPalooza IE Day 2011


    Predictive Analytics studying

    DataPalooza IE Day 2011


    Some background
    Some Background studying

    DataPalooza IE Day 2011


    Data and text mining
    Data and Text Mining studying

    Extractinguseful information from large data sets

    Data Mining

    process of explorationand analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and rules (Nisbet, Elder, Elder, & Miner, 2009)

    “the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories” (Shmueli, Patel, & Bruce, 2010)

    Text mining

    the discovery of useful and previously unknown “gems” of information from textual document repositories based upon patterns extracted from natural language(Zhang & Segall, 2010)

    DataPalooza IE Day 2011


    Automatic linear modeling ets proficiency profile total scaled score
    Automatic Linear Modeling studyingETS Proficiency Profile Total Scaled Score

    Predicts a continuous target based on linear relationships between the target and one or more predictors

    Target

    ETS Total Score

    400-500

    20 PREDICTORS

    % General Education Courses Completed

    Intended/Current Major

    Humanities Core Completed

    Social Science Core Completed

    Natural Science Core Completed

    Math Core Completed

    Humanities Electives Completed

    Social Science Electives Completed

    Natural Science Electives Completed

    Math Electives Completed

    Age

    Sex

    Credit Hours Successfully Completed

    Race/Ethnicity

    English Language (Primary)

    Enrollment Status (full/part time)

    Transfer Status

    Hours Working

    GPA

    Program Enrolled

    DataPalooza IE Day 2011







    Decision trees
    Decision studyingTrees

    • Creates tree-based classification model by classifying cases into groups

    • Predicts values of a dependent (target) variable based on values of independent (predictor) variables

      • CHAID (Chi-squared Automatic Interaction Detection): At each step in Tree creation, CHAID chooses the independent (predictor) variable that has the strongest interaction with the dependent variable. Categories of each predictor are merged if they are not significantly different with respect to the dependent variable.

      • Exhaustive CHAID. CHAID variation that examines all possible splits for each predictor

      • CRT (Classification and Regression Trees): CRT splits the data into segments that are as homogeneous as possible with respect to the dependent variable.

      • QUEST (Quick, Unbiased, Efficient Statistical Tree): A method that is fast and avoids other methods' bias in favor of predictors with many categories.

    DataPalooza IE Day 2011


    Predictive analysis of student success pass
    Predictive Analysis of Student Success (PASS) studying

    • Predicts remedial course success for reading, writing, and math based on fall 2010 and spring 2011 term results using 13 predictors

      • Expansion of “Think Tank” analysis presented by Dr. Green at the Math faculty retreat June 8, 2011

    • Analysis involves the highest level remedial courses for Writing, Math, Reading based on 13,231 student grade results

    DataPalooza IE Day 2011


    Pass model summary
    PASS Model Summary studying

    • Exhaustive CHAID

    • PREDICTORS

      • Academic Term, Campus/Center, Course ID, Instructor Name, Course Delivery Method, Student Sex, Student Race, Student Birthdate, High School Graduation Date, Student Zip, Student High School, High School City, High School State

    • TARGET

      • Student Success in course (grade = A, B, C)

    DataPalooza IE Day 2011


    Summary of counts and percentages
    Summary of Counts and Percentages studying

    n = 13,231 Academic History Grades

    63.4% Success Overall

    DataPalooza IE Day 2011


    Tree model all remedial combined
    Tree Model (all remedial combined) studying

    63.4% Success Overall

    56%

    63%

    74%

    80%

    MAT1033

    MAT0024

    ENC0021

    REA0010

    Given these results, the same analysis was performed for each course individually

    DataPalooza IE Day 2011


    Mat0024 tree diagram
    MAT0024 Tree Diagram studying

    56.2% Success

    Highest Success Instructor Group 76.2%

    (Node 5)

    Instructor

    High School City

    Student zip code is the next level predictor (nodes 1 – 4);

    High School City is the next level predictor for node 5; there is no next level predictor for node 6

    Continued next slide

    DataPalooza IE Day 2011


    Mat0024 tree diagram continued
    MAT0024 Tree Diagram (continued) studying

    Students from these high school cities were 100% successful with node 5 instructors in MAT0024

    Students from these high school cities were 69.7% successful with node 5 instructors in MAT0024.

    DataPalooza IE Day 2011


    Mat1033 tree diagram
    MAT1033 Tree Diagram studying

    Instructor

    High School City

    100% not successful

    High School Name

    100% successful

    DataPalooza IE Day 2011


    Model quality comparisons mat1033
    Model Quality Comparisons MAT1033 studying

    DECISION TREE MODEL (Target Category = Successful)

    PREDICTIONS

    Models used all predictors EXCEPT: Academic Term, Student Sex, Student Race, Student Age (1/1/2011), Student Age (HS Graduation), Student Zip, Student High School, High School City, High School State, Campus/Center, Instructor Name, Course Delivery Method, Same Course Previous Attempts

    DataPalooza IE Day 2011


    What’s Next? studying

    DataPalooza IE Day 2011


    Closing comments
    Closing Comments studying

    Please feel free to contact us

    Check out our web site for additional details

    Keep thinking about and using data to guide and improve your results!

    DataPalooza IE Day 2011


    Thanks for your patience. studying

    Have a fantastic fall 2011 term!

    DataPalooza IE Day 2011


    ad