Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study

Size: px
Start display at page:

Download "Purdue Data Summit Communication of Big Data Analytics. New SAT Predictive Validity Case Study"

Transcription

1 Purdue Data Summit 2017 Communication of Big Data Analytics New SAT Predictive Validity Case Study Paul M. Johnson, Ed.D. Associate Vice President for Enrollment Management, Research & Enrollment Information Services (REIS)

2 Optional Presentation Title Communication of Research to Stakeholders Early Communication electronically and at in-person meetings the what, why, how, when, etc. Format similar to research with which academia is familiar (e.g., research article) Clearly communicate data as tool to help inform their decision-making, if applicable Visuals to help show meaning of statistical concepts Link research\data to university and area (e.g., Enrollment Management) strategic plan to which stakeholders have already helped create and support Communicate progress on research, results, implementation, next steps electronically/in-person Office of Enrollment Management 2

3 Optional Presentation Title Example: Overview of Intro meeting academic research article approach Background Method: Multiple Linear Regression Validity Study Hypotheses Outcomes Simulation & Implementation Questions Office of Enrollment Management 3

4 Optional Presentation Title Example: Hypotheses SAT-Writing will correlate significantly with FY GPA SAT-Writing & SAT-Critical Reading (CR) are heavily correlated Incorporating SAT-Writing and recent cohorts will improve multiple correlation or R with FY GPA vs. current regression equation Office of Enrollment Management 4

5 Optional Presentation Title Examples: Visual--Outcomes SAT-Writing & SAT-CR were heavily correlated (.7 to.8 where 1 is perfect), presenting multicollinearity challenges. Writing Critical Reading Office of Enrollment Management 5

6 Optional Presentation Title Office of Enrollment Management 6

7 Example: Discussion & Approval Optional Presentation Title First discussed with deans during meetings in annual Enrollment Management updates, Summer-Fall Sounded good to everyone; Engineering raised some concern regarding their need to weight SAT-Math heavily. Following study (Summer 2009), weights and background sent to deans for review and their recommendations. Weights changed accordingly based on deans requests less change than data suggested. Noticed that SAT-Math received significantly lower weight (e.g., less than 10% at SEBS) based on regression. Office of Enrollment Management 7

8 USER CENTERED DESIGN IN DATA SCIENCE IAN PYTLARZ SENIOR DATA SCIENTIST

9 Why Design Is Needed In Data Science Many Ways Data Science Goes Wrong Automation instead of augmentation Tendency to think about replacing people, even if this isn t the optimal solution This generates a lot of fear towards data scientists Accidental stupidity Tay Microsoft chat bot gone horribly wrong Promotion of fake news Racist image recognition The problem with the designs of most engineers is that they are too logical. We have to accept human behavior the way it is, not the way we would wish it to be Don Norman, The Design of Everyday Things PURDUE DATA SUMMIT

10 Examples At Purdue Forecast & Grades Modelling Thinking About User Perspective The model predicts a student to fail if they have > 50% likelihood of doing so Low bar to clear, so students end up with many failures This could be demoralizing to a student if they had many failure predictions Instead of presenting the binary output, we binned the output to only show students as in danger if the had > 80% likelihood We could be doing even better! A small minority of students still show as failing everything without special intervention PURDUE DATA SUMMIT

11 Examples At Purdue Forecast & Grades Modelling Predictions Alone Won t Change Behavior The goal of Forecast is to change student behavior, we needed to bear that in mind when analyzing the model We need to provide information on WHY those predictions were made, to nudge behavior in the right direction Currently shows students the relationships between behaviors and success Student can influence this! Again, we could be doing this even better Should focus dynamically on students who have successful behaviors that they could be improving on PURDUE DATA SUMMIT

12 Next Steps at Purdue Augmentation A Website Will Never Replace Human Beings There is no automatic tool that is going to drastically alter student behaviors by itself Human intervention is the best way to effect change in students Luckily, we have humans who already do that job! Advisors can be augmented with machines to improve student success 1 This is in-progress at Purdue, both in modelling and in process improvements 1 PURDUE DATA SUMMIT

13 UK LEADS: Using Data Analytics to Drive Decision Making at Craig Rudick - Executive Director of Institutional Research and Lead Data Scientist 13

14 High School Readiness Index (HSRI) = HSGPA*10 + ACT/2 Unmet Financial Need is a major driver of student success. 14

15 UK LEADS: Leveraging Economic Affordability for Developing Success Shifting resources toward need-based financial aid Simulate changing financial aid awards to optimize: Yield Retention/Progression Net Tuition Revenue URM/Pell/First Gen/etc. Financial Aid Yield Retention Total Enrollment Net Tuition Revenue Demographics/Diversity 15

16 Designing effective analyses for decision support: Use the simplest methods possible Create tools others can use A picture is worth a thousand algorithms Focus algorithm output on useful decisionpoints Pilots and test cases to prove validity Make all your underlying data available 16

DRAFT VERSION 2, 02/24/12

DRAFT VERSION 2, 02/24/12 DRAFT VERSION 2, 02/24/12 Incentive-Based Budget Model Pilot Project for Academic Master s Program Tuition (Optional) CURRENT The core of support for the university s instructional mission has historically

More information

Access Center Assessment Report

Access Center Assessment Report Access Center Assessment Report The purpose of this report is to provide a description of the demographics as well as higher education access and success of Access Center students at CSU. College access

More information

OFFICE OF ENROLLMENT MANAGEMENT. Annual Report

OFFICE OF ENROLLMENT MANAGEMENT. Annual Report 2014-2015 OFFICE OF ENROLLMENT MANAGEMENT Annual Report Table of Contents 2014 2015 MESSAGE FROM THE VICE PROVOST A YEAR OF RECORDS 3 Undergraduate Enrollment 6 First-Year Students MOVING FORWARD THROUGH

More information

Introduction to Simulation

Introduction to Simulation Introduction to Simulation Spring 2010 Dr. Louis Luangkesorn University of Pittsburgh January 19, 2010 Dr. Louis Luangkesorn ( University of Pittsburgh ) Introduction to Simulation January 19, 2010 1 /

More information

WHY GRADUATE SCHOOL? Turning Today s Technical Talent Into Tomorrow s Technology Leaders

WHY GRADUATE SCHOOL? Turning Today s Technical Talent Into Tomorrow s Technology Leaders WHY GRADUATE SCHOOL? Turning Today s Technical Talent Into Tomorrow s Technology Leaders (This presentation has been ripped-off from a number of on-line sources) Outline Why Should I Go to Graduate School?

More information

Visit us at:

Visit us at: White Paper Integrating Six Sigma and Software Testing Process for Removal of Wastage & Optimizing Resource Utilization 24 October 2013 With resources working for extended hours and in a pressurized environment,

More information

Do multi-year scholarships increase retention? Results

Do multi-year scholarships increase retention? Results Do multi-year scholarships increase retention? In the past, Boise State has mainly offered one-year scholarships to new freshmen. Recently, however, the institution moved toward offering more two and four-year

More information

(Sub)Gradient Descent

(Sub)Gradient Descent (Sub)Gradient Descent CMSC 422 MARINE CARPUAT marine@cs.umd.edu Figures credit: Piyush Rai Logistics Midterm is on Thursday 3/24 during class time closed book/internet/etc, one page of notes. will include

More information

Educational Leadership and Policy Studies Doctoral Programs (Ed.D. and Ph.D.)

Educational Leadership and Policy Studies Doctoral Programs (Ed.D. and Ph.D.) Contact: Susan Korach susan.korach@du.edu Morgridge Office of Admissions mce@du.edu http://morgridge.du.edu/ Educational Leadership and Policy Studies Doctoral Programs (Ed.D. and Ph.D.) Doctoral (Ed.D.

More information

Math Pathways Task Force Recommendations February Background

Math Pathways Task Force Recommendations February Background Math Pathways Task Force Recommendations February 2017 Background In October 2011, Oklahoma joined Complete College America (CCA) to increase the number of degrees and certificates earned in Oklahoma.

More information

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and

A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and A Decision Tree Analysis of the Transfer Student Emma Gunu, MS Research Analyst Robert M Roe, PhD Executive Director of Institutional Research and Planning Overview Motivation for Analyses Analyses and

More information

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur)

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) 1 Interviews, diary studies Start stats Thursday: Ethics/IRB Tuesday: More stats New homework is available

More information

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point.

STT 231 Test 1. Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. STT 231 Test 1 Fill in the Letter of Your Choice to Each Question in the Scantron. Each question is worth 2 point. 1. A professor has kept records on grades that students have earned in his class. If he

More information

White Paper. The Art of Learning

White Paper. The Art of Learning The Art of Learning Based upon years of observation of adult learners in both our face-to-face classroom courses and using our Mentored Email 1 distance learning methodology, it is fascinating to see how

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

Strategic Plan Dashboard Results. Office of Institutional Research and Assessment

Strategic Plan Dashboard Results. Office of Institutional Research and Assessment 29-21 Strategic Plan Dashboard Results Office of Institutional Research and Assessment Binghamton University Office of Institutional Research and Assessment Definitions Fall Undergraduate and Graduate

More information

Dublin City Schools Career and College Ready Academies FAQ. General

Dublin City Schools Career and College Ready Academies FAQ. General Dublin City Schools Career and College Ready Academies FAQ General Question: Will transportation be provided to/from the academy? Available transportation will be determined after the academy enrollment

More information

Value of Athletics in Higher Education March Prepared by Edward J. Ray, President Oregon State University

Value of Athletics in Higher Education March Prepared by Edward J. Ray, President Oregon State University Materials linked from the 5/12/09 OSU Faculty Senate agenda 1. Who Participates Value of Athletics in Higher Education March 2009 Prepared by Edward J. Ray, President Oregon State University Today, more

More information

Go fishing! Responsibility judgments when cooperation breaks down

Go fishing! Responsibility judgments when cooperation breaks down Go fishing! Responsibility judgments when cooperation breaks down Kelsey Allen (krallen@mit.edu), Julian Jara-Ettinger (jjara@mit.edu), Tobias Gerstenberg (tger@mit.edu), Max Kleiman-Weiner (maxkw@mit.edu)

More information

Radius STEM Readiness TM

Radius STEM Readiness TM Curriculum Guide Radius STEM Readiness TM While today s teens are surrounded by technology, we face a stark and imminent shortage of graduates pursuing careers in Science, Technology, Engineering, and

More information

College Pricing and Income Inequality

College Pricing and Income Inequality College Pricing and Income Inequality Zhifeng Cai U of Minnesota, Rutgers University, and FRB Minneapolis Jonathan Heathcote FRB Minneapolis NBER Income Distribution, July 20, 2017 The views expressed

More information

College Pricing and Income Inequality

College Pricing and Income Inequality College Pricing and Income Inequality Zhifeng Cai U of Minnesota and FRB Minneapolis Jonathan Heathcote FRB Minneapolis OSU, November 15 2016 The views expressed herein are those of the authors and not

More information

Leveraging MOOCs to bring entrepreneurship and innovation to everyone on campus

Leveraging MOOCs to bring entrepreneurship and innovation to everyone on campus Paper ID #9305 Leveraging MOOCs to bring entrepreneurship and innovation to everyone on campus Dr. James V Green, University of Maryland, College Park Dr. James V. Green leads the education activities

More information

Differential Tuition Budget Proposal FY

Differential Tuition Budget Proposal FY Differential Tuition Budget Proposal FY 2013-2014 MPA Differential Tuition Subcommittee MPA Faculty This document presents the budget proposal of the MPA Differential Tuition Subcommittee (MPADTS) for

More information

ME 443/643 Design Techniques in Mechanical Engineering. Lecture 1: Introduction

ME 443/643 Design Techniques in Mechanical Engineering. Lecture 1: Introduction ME 443/643 Design Techniques in Mechanical Engineering Lecture 1: Introduction Instructor: Dr. Jagadeep Thota Instructor Introduction Born in Bangalore, India. B.S. in ME @ Bangalore University, India.

More information

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1

Notes on The Sciences of the Artificial Adapted from a shorter document written for course (Deciding What to Design) 1 Notes on The Sciences of the Artificial Adapted from a shorter document written for course 17-652 (Deciding What to Design) 1 Ali Almossawi December 29, 2005 1 Introduction The Sciences of the Artificial

More information

12- A whirlwind tour of statistics

12- A whirlwind tour of statistics CyLab HT 05-436 / 05-836 / 08-534 / 08-734 / 19-534 / 19-734 Usable Privacy and Security TP :// C DU February 22, 2016 y & Secu rivac rity P le ratory bo La Lujo Bauer, Nicolas Christin, and Abby Marsh

More information

College Pricing. Ben Johnson. April 30, Abstract. Colleges in the United States price discriminate based on student characteristics

College Pricing. Ben Johnson. April 30, Abstract. Colleges in the United States price discriminate based on student characteristics College Pricing Ben Johnson April 30, 2012 Abstract Colleges in the United States price discriminate based on student characteristics such as ability and income. This paper develops a model of college

More information

Alex Robinson Financial Aid

Alex Robinson Financial Aid Alex Robinson Financial Aid Image Source: https://www.google.com/search?q=college+decisions+and+financial+fit&espv=2&biw=1366&bih=643&source=lnms&tb m=isch&sa=x&ved=0cagq_auoa2ovchmi6vt40tknxwivee6ich2ipgcw#imgrc=45cmbyr3nan8gm%3a

More information

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016 AGENDA Advanced Learning Theories Alejandra J. Magana, Ph.D. admagana@purdue.edu Introduction to Learning Theories Role of Learning Theories and Frameworks Learning Design Research Design Dual Coding Theory

More information

Analysis of Enzyme Kinetic Data

Analysis of Enzyme Kinetic Data Analysis of Enzyme Kinetic Data To Marilú Analysis of Enzyme Kinetic Data ATHEL CORNISH-BOWDEN Directeur de Recherche Émérite, Centre National de la Recherche Scientifique, Marseilles OXFORD UNIVERSITY

More information

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA

Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing A Moving Target: How Do We Test Machine Learning Systems? Peter Varhol Technology Strategy Research, USA Testing a Moving Target How Do We Test Machine Learning Systems? Peter Varhol, Technology

More information

Research Design & Analysis Made Easy! Brainstorming Worksheet

Research Design & Analysis Made Easy! Brainstorming Worksheet Brainstorming Worksheet 1) Choose a Topic a) What are you passionate about? b) What are your library s strengths? c) What are your library s weaknesses? d) What is a hot topic in the field right now that

More information

Math Placement at Paci c Lutheran University

Math Placement at Paci c Lutheran University Math Placement at Paci c Lutheran University The Art of Matching Students to Math Courses Professor Je Stuart Math Placement Director Paci c Lutheran University Tacoma, WA 98447 USA je rey.stuart@plu.edu

More information

HOLMER GREEN SENIOR SCHOOL CURRICULUM INFORMATION

HOLMER GREEN SENIOR SCHOOL CURRICULUM INFORMATION HOLMER GREEN SENIOR SCHOOL CURRICULUM INFORMATION Subject: Mathematics Year Group: 7 Exam Board: (For years 10, 11, 12 and 13 only) Assessment requirements: Students will take 3 large assessments during

More information

Understanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010)

Understanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010) Understanding and Interpreting the NRC s Data-Based Assessment of Research-Doctorate Programs in the United States (2010) Jaxk Reeves, SCC Director Kim Love-Myers, SCC Associate Director Presented at UGA

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

More information

*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN

*Net Perceptions, Inc West 78th Street Suite 300 Minneapolis, MN From: AAAI Technical Report WS-98-08. Compilation copyright 1998, AAAI (www.aaai.org). All rights reserved. Recommender Systems: A GroupLens Perspective Joseph A. Konstan *t, John Riedl *t, AI Borchers,

More information

Relationships Between Motivation And Student Performance In A Technology-Rich Classroom Environment

Relationships Between Motivation And Student Performance In A Technology-Rich Classroom Environment Relationships Between Motivation And Student Performance In A Technology-Rich Classroom Environment John Tapper & Sara Dalton Arden Brookstein, Derek Beaton, Stephen Hegedus jtapper@donahue.umassp.edu,

More information

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017

Lahore University of Management Sciences. FINN 321 Econometrics Fall Semester 2017 Instructor Syed Zahid Ali Room No. 247 Economics Wing First Floor Office Hours Email szahid@lums.edu.pk Telephone Ext. 8074 Secretary/TA TA Office Hours Course URL (if any) Suraj.lums.edu.pk FINN 321 Econometrics

More information

Lucintel. Publisher Sample

Lucintel.  Publisher Sample Lucintel http://www.marketresearch.com/lucintel-v2747/ Publisher Sample Phone: 800.298.5699 (US) or +1.240.747.3093 or +1.240.747.3093 (Int'l) Hours: Monday - Thursday: 5:30am - 6:30pm EST Fridays: 5:30am

More information

Chapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4

Chapters 1-5 Cumulative Assessment AP Statistics November 2008 Gillespie, Block 4 Chapters 1-5 Cumulative Assessment AP Statistics Name: November 2008 Gillespie, Block 4 Part I: Multiple Choice This portion of the test will determine 60% of your overall test grade. Each question is

More information

A MEANINGFUL CAREER IN LESS THAN ONE YEAR MASTER IN TEACHING

A MEANINGFUL CAREER IN LESS THAN ONE YEAR MASTER IN TEACHING A MEANINGFUL CAREER IN LESS THAN ONE YEAR MASTER IN TEACHING Washington State Residency Teacher Certification DON T JUST MAKE A LIVING. MAKE A DIFFERENCE. introduction to the MASTER IN TEACHING DEGREE

More information

VOL VISION 2020 STRATEGIC PLAN IMPLEMENTATION

VOL VISION 2020 STRATEGIC PLAN IMPLEMENTATION VOL VISION 2020 STRATEGIC PLAN IMPLEMENTATION CONTENTS Vol Vision 2020 Summary Overview Approach Plan Phase 1 Key Initiatives, Timelines, Accountability Strategy Dashboard Phase 1 Metrics and Indicators

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

The Strong Minimalist Thesis and Bounded Optimality

The Strong Minimalist Thesis and Bounded Optimality The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this

More information

OFFICE SUPPORT SPECIALIST Technical Diploma

OFFICE SUPPORT SPECIALIST Technical Diploma OFFICE SUPPORT SPECIALIST Technical Diploma Program Code: 31-106-8 our graduates INDEMAND 2017/2018 mstc.edu administrative professional career pathway OFFICE SUPPORT SPECIALIST CUSTOMER RELATIONSHIP PROFESSIONAL

More information

The lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design.

The lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design. Name: Partner(s): Lab #1 The Scientific Method Due 6/25 Objective The lab is designed to remind you how to work with scientific data (including dealing with uncertainty) and to review experimental design.

More information

Team Dispersal. Some shaping ideas

Team Dispersal. Some shaping ideas Team Dispersal Some shaping ideas The storyline is how distributed teams can be a liability or an asset or anything in between. It isn t simply a case of neutralizing the down side Nick Clare, January

More information

B.S/M.A in Mathematics

B.S/M.A in Mathematics B.S/M.A in Mathematics The dual Bachelor of Science/Master of Arts in Mathematics program provides an opportunity for individuals to pursue advanced study in mathematics and to develop skills that can

More information

Introduction to CS 100 Overview of UK. CS September 2015

Introduction to CS 100 Overview of UK. CS September 2015 Introduction to CS 100 Overview of CS @ UK CS 100 1 September 2015 Outline CS100: Structure and Expectations Context: Organization, mission, etc. BS in CS Degree Program Department Locations Our Faculty

More information

Medical Complexity: A Pragmatic Theory

Medical Complexity: A Pragmatic Theory http://eoimages.gsfc.nasa.gov/images/imagerecords/57000/57747/cloud_combined_2048.jpg Medical Complexity: A Pragmatic Theory Chris Feudtner, MD PhD MPH The Children s Hospital of Philadelphia Main Thesis

More information

Planning for Preassessment. Kathy Paul Johnston CSD Johnston, Iowa

Planning for Preassessment. Kathy Paul Johnston CSD Johnston, Iowa Planning for Preassessment Kathy Paul Johnston CSD Johnston, Iowa Why Plan? Establishes the starting point for learning Students can t learn what they already know Match instructional strategies to individual

More information

TU-E2090 Research Assignment in Operations Management and Services

TU-E2090 Research Assignment in Operations Management and Services Aalto University School of Science Operations and Service Management TU-E2090 Research Assignment in Operations Management and Services Version 2016-08-29 COURSE INSTRUCTOR: OFFICE HOURS: CONTACT: Saara

More information

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and

More information

ACADEMIC AND COLLEGE PLANNING NIGHT

ACADEMIC AND COLLEGE PLANNING NIGHT ACADEMIC AND COLLEGE PLANNING NIGHT PLUS OPEN HOUSE SEMINAR ON ACADEMIC AND COLLEGE PLANNING TOPICS JANUARY 11, 2017 ACADEMIC AND COLLEGE PLANNING NIGHT SESSION SCHEDULE 5:45 PM Open House program 6:15

More information

Strategic Plan Dashboard

Strategic Plan Dashboard Strategic Plan Dashboard 2015-16 2010-18* *Strategic Plan extended until 2018 (1) Goal 1: Continue to operate in a fiscally responsible manner. Focus Area 1A: Reduce costs/expenses where possible Strategy

More information

Evaluation of a College Freshman Diversity Research Program

Evaluation of a College Freshman Diversity Research Program Evaluation of a College Freshman Diversity Research Program Sarah Garner University of Washington, Seattle, Washington 98195 Michael J. Tremmel University of Washington, Seattle, Washington 98195 Sarah

More information

Writing Research Articles

Writing Research Articles Marek J. Druzdzel with minor additions from Peter Brusilovsky University of Pittsburgh School of Information Sciences and Intelligent Systems Program marek@sis.pitt.edu http://www.pitt.edu/~druzdzel Overview

More information

New Jersey Institute of Technology Newark College of Engineering

New Jersey Institute of Technology Newark College of Engineering New Jersey Institute of Technology Newark College of Engineering AND IN ELECTRICAL AND COMPUTER ENGINEERING Program Review Last Update: Nov. 23, 2005 MISSION STATEMENTS DOCTOR OF PHILOSOPHY IN ELECTRICAL

More information

A comparative study on cost-sharing in higher education Using the case study approach to contribute to evidence-based policy

A comparative study on cost-sharing in higher education Using the case study approach to contribute to evidence-based policy A comparative study on cost-sharing in higher education Using the case study approach to contribute to evidence-based policy Tuition fees between sacred cow and cash cow Conference of Vlaams Verbond van

More information

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF

ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Read Online and Download Ebook ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY DOWNLOAD EBOOK : ADVANCED MACHINE LEARNING WITH PYTHON BY JOHN HEARTY PDF Click link bellow and free register to download

More information

What is a Mental Model?

What is a Mental Model? Mental Models for Program Understanding Dr. Jonathan I. Maletic Computer Science Department Kent State University What is a Mental Model? Internal (mental) representation of a real system s behavior,

More information

Bachelor of Science. Undergraduate Program. Department of Physics

Bachelor of Science. Undergraduate Program. Department of Physics Department of Physics Undergraduate Program Bachelor of Science Students with a strong interest in understanding the fundamental whys and hows of natural physical phenomena are encouraged to consider majoring

More information

Why Pay Attention to Race?

Why Pay Attention to Race? Why Pay Attention to Race? Witnessing Whiteness Chapter 1 Workshop 1.1 1.1-1 Dear Facilitator(s), This workshop series was carefully crafted, reviewed (by a multiracial team), and revised with several

More information

Race, Class, and the Selective College Experience

Race, Class, and the Selective College Experience Race, Class, and the Selective College Experience Thomas J. Espenshade Alexandria Walton Radford Chang Young Chung Office of Population Research Princeton University December 15, 2009 1 Overview of NSCE

More information

Welcome. Paulo Goes Dean, Eller College of Management Welcome Our region

Welcome. Paulo Goes Dean, Eller College of Management Welcome Our region Welcome. Paulo Goes Dean, Welcome. Our region Outlook for Tucson Patricia Feeney Executive Director, Southern Arizona Market Chase George W. Hammond, Ph.D. Director, University of Arizona 1 Visit the award-winning

More information

Communication Disorders Program. Strategic Plan January 2012 December 2016

Communication Disorders Program. Strategic Plan January 2012 December 2016 Communication Disorders Program Strategic Plan January 2012 December 2016 Preamble The Communication Disorders Program (CD) at Georgia State University began with only one faculty member in 1974. The Program

More information

American Journal of Business Education October 2009 Volume 2, Number 7

American Journal of Business Education October 2009 Volume 2, Number 7 Factors Affecting Students Grades In Principles Of Economics Orhan Kara, West Chester University, USA Fathollah Bagheri, University of North Dakota, USA Thomas Tolin, West Chester University, USA ABSTRACT

More information

Knowledge management styles and performance: a knowledge space model from both theoretical and empirical perspectives

Knowledge management styles and performance: a knowledge space model from both theoretical and empirical perspectives University of Wollongong Research Online University of Wollongong Thesis Collection University of Wollongong Thesis Collections 2004 Knowledge management styles and performance: a knowledge space model

More information

Testimony in front of the Assembly Committee on Jobs and the Economy Special Session Assembly Bill 1 Ray Cross, UW System President August 3, 2017

Testimony in front of the Assembly Committee on Jobs and the Economy Special Session Assembly Bill 1 Ray Cross, UW System President August 3, 2017 Office of the President 1700 Van Hise Hall 1220 Linden Drive Madison, Wisconsin 53706-1559 (608) 262-2321 Phone (608) 262-3985 Fax e-mail: rcross@uwsa.edu website: www.wisconsin.edu/ Testimony in front

More information

Detailed course syllabus

Detailed course syllabus Detailed course syllabus 1. Linear regression model. Ordinary least squares method. This introductory class covers basic definitions of econometrics, econometric model, and economic data. Classification

More information

The Condition of College & Career Readiness 2016

The Condition of College & Career Readiness 2016 The Condition of College and Career Readiness This report looks at the progress of the 16 ACT -tested graduating class relative to college and career readiness. This year s report shows that 64% of students

More information

ACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014

ACTL5103 Stochastic Modelling For Actuaries. Course Outline Semester 2, 2014 UNSW Australia Business School School of Risk and Actuarial Studies ACTL5103 Stochastic Modelling For Actuaries Course Outline Semester 2, 2014 Part A: Course-Specific Information Please consult Part B

More information

Biomedical Sciences (BC98)

Biomedical Sciences (BC98) Be one of the first to experience the new undergraduate science programme at a university leading the way in biomedical teaching and research Biomedical Sciences (BC98) BA in Cell and Systems Biology BA

More information

10 Tips For Using Your Ipad as An AAC Device. A practical guide for parents and professionals

10 Tips For Using Your Ipad as An AAC Device. A practical guide for parents and professionals 10 Tips For Using Your Ipad as An AAC Device A practical guide for parents and professionals Introduction The ipad continues to provide innovative ways to make communication and language skill development

More information

Foundations of Knowledge Representation in Cyc

Foundations of Knowledge Representation in Cyc Foundations of Knowledge Representation in Cyc Why use logic? CycL Syntax Collections and Individuals (#$isa and #$genls) Microtheories This is an introduction to the foundations of knowledge representation

More information

A CASE STUDY FOR THE SYSTEMS APPROACH FOR DEVELOPING CURRICULA DON T THROW OUT THE BABY WITH THE BATH WATER. Dr. Anthony A.

A CASE STUDY FOR THE SYSTEMS APPROACH FOR DEVELOPING CURRICULA DON T THROW OUT THE BABY WITH THE BATH WATER. Dr. Anthony A. A Case Study for the Systems OPINION Approach for Developing Curricula A CASE STUDY FOR THE SYSTEMS APPROACH FOR DEVELOPING CURRICULA DON T THROW OUT THE BABY WITH THE BATH WATER Dr. Anthony A. Scafati

More information

November 17, 2017 ARIZONA STATE UNIVERSITY. ADDENDUM 3 RFP Digital Integrated Enrollment Support for Students

November 17, 2017 ARIZONA STATE UNIVERSITY. ADDENDUM 3 RFP Digital Integrated Enrollment Support for Students November 17, 2017 ARIZONA STATE UNIVERSITY ADDENDUM 3 RFP 331801 Digital Integrated Enrollment Support for Students Please note the following answers to questions that were asked prior to the deadline

More information

BENCHMARK TREND COMPARISON REPORT:

BENCHMARK TREND COMPARISON REPORT: National Survey of Student Engagement (NSSE) BENCHMARK TREND COMPARISON REPORT: CARNEGIE PEER INSTITUTIONS, 2003-2011 PREPARED BY: ANGEL A. SANCHEZ, DIRECTOR KELLI PAYNE, ADMINISTRATIVE ANALYST/ SPECIALIST

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

On-Line Data Analytics

On-Line Data Analytics International Journal of Computer Applications in Engineering Sciences [VOL I, ISSUE III, SEPTEMBER 2011] [ISSN: 2231-4946] On-Line Data Analytics Yugandhar Vemulapalli #, Devarapalli Raghu *, Raja Jacob

More information

Early Warning System Implementation Guide

Early Warning System Implementation Guide Linking Research and Resources for Better High Schools betterhighschools.org September 2010 Early Warning System Implementation Guide For use with the National High School Center s Early Warning System

More information

Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach

Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach IOP Conference Series: Materials Science and Engineering PAPER OPEN ACCESS Historical maintenance relevant information roadmap for a self-learning maintenance prediction procedural approach To cite this

More information

UNIT ONE Tools of Algebra

UNIT ONE Tools of Algebra UNIT ONE Tools of Algebra Subject: Algebra 1 Grade: 9 th 10 th Standards and Benchmarks: 1 a, b,e; 3 a, b; 4 a, b; Overview My Lessons are following the first unit from Prentice Hall Algebra 1 1. Students

More information

School of Innovative Technologies and Engineering

School of Innovative Technologies and Engineering School of Innovative Technologies and Engineering Department of Applied Mathematical Sciences Proficiency Course in MATLAB COURSE DOCUMENT VERSION 1.0 PCMv1.0 July 2012 University of Technology, Mauritius

More information

Investment in e- journals, use and research outcomes

Investment in e- journals, use and research outcomes Investment in e- journals, use and research outcomes David Nicholas CIBER Research Limited, UK Ian Rowlands University of Leicester, UK Library Return on Investment seminar Universite de Lyon, 20-21 February

More information

DegreeWorks Training Guide

DegreeWorks Training Guide DegreeWorks Training Guide A Degree Evaluation and Advising Tool for MERCY COLLEGE Information for Students Last updated 03/2014 What Is DegreeWorks? DegreeWorks is a web-based tool that will provide a

More information

A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements

A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2006 A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements Donna S. Kroos Virginia

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Citrine Informatics. The Latest from Citrine. Citrine Informatics. The data analytics platform for the physical world

Citrine Informatics. The Latest from Citrine. Citrine Informatics. The data analytics platform for the physical world Citrine Informatics The data analytics platform for the physical world The Latest from Citrine Summit on Data and Analytics for Materials Research 31 October 2016 Our Mission is Simple Add as much value

More information

Top Ten Persuasive Strategies Used on the Web - Cathy SooHoo, 5/17/01

Top Ten Persuasive Strategies Used on the Web - Cathy SooHoo, 5/17/01 Top Ten Persuasive Strategies Used on the Web - Cathy SooHoo, 5/17/01 Introduction Although there is nothing new about the human use of persuasive strategies, web technologies usher forth a new level of

More information

CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University

CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE. Mingon Kang, PhD Computer Science, Kennesaw State University CS4491/CS 7265 BIG DATA ANALYTICS INTRODUCTION TO THE COURSE Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction Mingon Kang, PhD Homepage: http://ksuweb.kennesaw.edu/~mkang9

More information

Process Evaluations for a Multisite Nutrition Education Program

Process Evaluations for a Multisite Nutrition Education Program Process Evaluations for a Multisite Nutrition Education Program Paul Branscum 1 and Gail Kaye 2 1 The University of Oklahoma 2 The Ohio State University Abstract Process evaluations are an often-overlooked

More information

Alpha provides an overall measure of the internal reliability of the test. The Coefficient Alphas for the STEP are:

Alpha provides an overall measure of the internal reliability of the test. The Coefficient Alphas for the STEP are: Every individual is unique. From the way we look to how we behave, speak, and act, we all do it differently. We also have our own unique methods of learning. Once those methods are identified, it can make

More information

What is PDE? Research Report. Paul Nichols

What is PDE? Research Report. Paul Nichols What is PDE? Research Report Paul Nichols December 2013 WHAT IS PDE? 1 About Pearson Everything we do at Pearson grows out of a clear mission: to help people make progress in their lives through personalized

More information

Speech Recognition at ICSI: Broadcast News and beyond

Speech Recognition at ICSI: Broadcast News and beyond Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI

More information

STA 225: Introductory Statistics (CT)

STA 225: Introductory Statistics (CT) Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic

More information

Objectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition

Objectives. Chapter 2: The Representation of Knowledge. Expert Systems: Principles and Programming, Fourth Edition Chapter 2: The Representation of Knowledge Expert Systems: Principles and Programming, Fourth Edition Objectives Introduce the study of logic Learn the difference between formal logic and informal logic

More information

Circuit Simulators: A Revolutionary E-Learning Platform

Circuit Simulators: A Revolutionary E-Learning Platform Circuit Simulators: A Revolutionary E-Learning Platform Mahi Itagi Padre Conceicao College of Engineering, Verna, Goa, India. itagimahi@gmail.com Akhil Deshpande Gogte Institute of Technology, Udyambag,

More information