October 23, 2004FIE 2004 WIP: Using the Kolbe Conative Index ™ for Improving Retention of Computer Science Students Robert Lingard Elizabeth Berry Brenda.

Slides:



Advertisements
Similar presentations
Impact of Career Services Retention, Graduation, Confidence, SLOs & SDOs.
Advertisements

Rationales for Problem Based Learning in the New Chemistry Curriculum or Why don’t we lecture all of the time? Chemistry Department.
Chapter 10 Quality Control McGraw-Hill/Irwin
A Process for the Direct Assessment of Program Learning Outcomes Based on the Principles and Practices of Software Engineering Robert W. Lingard California.
Method IntroductionResults Discussion Effects of Plans and Workloads on Academic Performance Mark C. Schroeder University of Nebraska – Lincoln College.
October 23, 2004 Using the Kolbe Conative Index ™ for Improving Retention of Computer Science Students Robert Lingard Elizabeth Berry Brenda Timmerman.
November 8, 2003 Assessment of Active Learning with Upper Division Computer Science Students Brenda Timmerman Robert Lingard California State University,
October 20, 2005 Using the Kolbe A ™ Conative Index to Study Retention of Computer Science Students Robert Lingard Brenda Timmerman Elizabeth Berry California.
October 23, 2004 WIP: Using the Kolbe Conative Index ™ for Improving Retention of Computer Science Students Robert Lingard Elizabeth Berry Brenda Timmerman.
2002 ASEE/IEEE FIE Conference1 Teaching Teamwork Skills in Software Engineering Based on an Understanding of Factors Affecting Group Performance Robert.
Lecture 2: Thu, Jan 16 Hypothesis Testing – Introduction (Ch 11)
6/21/20151 Active Learning with Upper Division Computer Science Students Brenda Timmerman Robert Lingard G. Michael Barnes California State University,
6/26/20151 Active Learning with Upper Division Computer Science Students Brenda Timmerman Robert Lingard G. Michael Barnes California State University,
A Balanced and Effective Team “The better the mix, the better the performance”
Simple Correlation Scatterplots & r Interpreting r Outcomes vs. RH:
Qualitative and Quantitative Data
Applied Business Forecasting and Planning
Testing Hypotheses.
Thinking Critically with Psychological Science Chapter 1
Business Communication: Process and Product, 6e Mary Ellen Guffey Copyright © 2008 Chapter 12 Informal Business Reports.
Copyright © 2006 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide
Near East University Department of English Language Teaching Advanced Research Techniques Correlational Studies Abdalmonam H. Elkorbow.
Analyzing Reliability and Validity in Outcomes Assessment (Part 1) Robert W. Lingard and Deborah K. van Alphen California State University, Northridge.
Robert W. Lingard California State University, Northridge EEET July 11, 2009.
§ 1.1 An Overview of Statistics. Data and Statistics Data consists of information coming from observations, counts, measurements, or responses. Statistics.
Lesson 6: Market Research. Objectives Outline the five major steps in the market research process Describe how surveys can be used to learn about customer.
Market Research Lesson 6. Objectives Outline the five major steps in the market research process Describe how surveys can be used to learn about customer.
Research & Statistics Looking for Conclusions. Statistics Mathematics is used to organize, summarize, and interpret mathematical data 2 types of statistics.
Chapter 1 What is Organizational Behavior? McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
CS 110: Introduction to Computer Science Frequently asked questions about a CS major and CS career.
Emotions and Learning Styles: Why these are important Chapter Two- Supplement McGraw-Hill/Irwin.
Adolescent Literacy – Professional Development
Normal Distr Practice Major League baseball attendance in 2011 averaged 30,000 with a standard deviation of 6,000. i. What percentage of teams had between.
AP-1 5. Project Management. AP-2 Software Failure Software fails at a significant rate What is failure? Not delivering it on time is an estimation failure.
Formal Assessment Week 6 & 7. Formal Assessment Formal assessment is typical in the form of paper-pencil assessment or computer based. These tests are.
August 20, Learning-Centered Education Bob Lingard.
Colbi Custis Salisbury University RPDS Conference May 2011.
Using your PLAN Results Using your PLAN Results. 1.How am I doing so far? 2.What are my plans and goals after high school? 3.Am I on track for college.
BUS 362 Marketing Research SPSS Exam Spring 2014 Name: Emilija Naumoska Time of the exam start: digit/letter code:
Cyberlearning Opportunity and Challenge 1. Need Students, in addition with teachers need to be taught how to properly manage large amounts of data whether.
MAP the Way to Success in Math: A Hybridization of Tutoring and SI Support Evin Deschamps Northern Arizona University Student Learning Centers.
Business Studies Find your chair: Look at the picture what does this mean to you? Be prepared to answer if called upon: This does not require any verbal.
An Overview of Statistics NOTES Coach Bridges What you should learn: The definition of data and statistics How to distinguish between a population and.
An essential part of workplace success!
Ted talk: The Key to Success? Grit.
Writing Interpretive Reports meaningful & useful suggestions.
1 Doing Statistics for Business Doing Statistics for Business Data, Inference, and Decision Making Marilyn K. Pelosi Theresa M. Sandifer Chapter 12 Multiple.
 EquiLearn Ltd About the TeamCoach  Workshop Build your team’s effectiveness with a 2 Day TeamCoach  Workshop Designed and Implemented by EquiLearn.
 400 hiring executives of major corporations were asked this simple but significant question…their collective answer? o …. Not really  Found students.
Module 4: Systems Development Chapter 13: Investigation and Analysis.
Hypothesis Testing Introduction to Statistics Chapter 8 Feb 24-26, 2009 Classes #12-13.
STATISTICS STATISTICS Numerical data. How Do We Make Sense of the Data? descriptively Researchers use statistics for two major purposes: (1) descriptively.
Outline of Today’s Discussion 1.The Chi-Square Test of Independence 2.The Chi-Square Test of Goodness of Fit.
The population in a statistical study is the entire group of individuals about which we want information The population is the group we want to study.
1 Collecting and Interpreting Quantitative Data Deborah K. van Alphen and Robert W. Lingard California State University, Northridge.
1 Competency-based HR Management. 2 Competency-based HR Management : A Framework.
Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall) Chapter 12: Artificial Intelligence and Expert Systems.
Preparing students for the global economy. PLTW is preparing students for the global economy through its world-class STEM curriculum, high quality professional.
Peter Varhol Solutions Evangelist
Unit 5: Hypothesis Testing
Using The Kolbe™ System
Robert W. Lingard California State University, Northridge
Robert W. Lingard California State University, Northridge
Analyzing Reliability and Validity in Outcomes Assessment Part 1
Robert W. Lingard California State University, Northridge
Robert W. Lingard California State University, Northridge
Collecting and Interpreting Quantitative Data – Introduction (Part 1)
Analyzing Reliability and Validity in Outcomes Assessment
A Process for the Direct Assessment of Program Learning Outcomes Based on the Principles and Practices of Software Engineering Robert W. Lingard California.
Collecting and Interpreting Quantitative Data
Presentation transcript:

October 23, 2004FIE 2004 WIP: Using the Kolbe Conative Index ™ for Improving Retention of Computer Science Students Robert Lingard Elizabeth Berry Brenda Timmerman California State University, Northridge

October 23, 2004 FIE Overview The Retention Problem What Does the Kolbe Conative Index ™ Measure? Results From Previous Studies With Upper Division Students? Current Studies With Freshmen? Some Observations Conclusions and Recommendations

October 23, 2004 FIE The Retention Problem At CSUN fewer than 20% of students who decide to major in Computer Science as freshmen complete the program. Many universities report that the graduation rate in Computer Science is the lowest, or near the lowest, of all majors. Improving retention requires understanding the reasons for the high drop out rates.

October 23, 2004 FIE The Kolbe Concept ® What It Is... – It identifies the instincts that drive ones NATURAL behaviors – it describes “MO” (Modus Operandi) – it focuses on strengths -- how to help people be more productive and effective – it is universal – it is equal (unbiased)

October 23, 2004 FIE The Kolbe Concept ® What It Is Not... – about how smart someone is – about social style – about personality – about right or wrong – there is no good or bad

October 23, 2004 FIE The Kolbe Instinctive Talents Fact Finder Follow Thru Quick Start Implementor

October 23, 2004 FIE The “Fact Finder” Probes Asks Questions Weighs Pros and Cons Collects Data and Establishes Priorities before Making a Decision

October 23, 2004 FIE The “Follow Thru” Individual Seeks Structure Makes Schedules Needs a Sense of Order and Plans Ahead

October 23, 2004 FIE The “Quick Start” Innovates Takes Risks Improvises Plays Hunches When Asked to Give a Presentation, Comfortably Ad Libs

October 23, 2004 FIE The “Implementor” Uses Space and Materials Builds and Constructs Uses Hands-on Equipment with Ease Creates Handcrafted Models Insists on Quality Materials

October 23, 2004 FIE Sample “Kolbe” Results

October 23, 2004 FIE Kolbe and Teamwork The Kolbe index is used by many companies to form effective teams by ensuring teams contain a balance of Kolbe talents. Previous results at CSUN have shown a statistically significant correlation between team synergy as measure by Kolbe and performance on software engineering projects. However, synergistic student teams are hard to create due to a lack of certain Kolbe talents.

October 23, 2004 FIE Kolbe Profile of Upper Division Computer Science Students

October 23, 2004 FIE Kolbe Profile of Marketing and Management Graduates

October 23, 2004 FIE Kolbe Profile Comparisons - CS vs. Marketing & Management Students

October 23, 2004 FIE Kolbe Profile Comparisons CS Upper Division vs. Freshmen

October 23, 2004 FIE Observations There are statistically significant difference between freshmen and upper division computer science majors. Freshmen, as a group, have more “implementor” and less “fact finder” tendencies. The profiles of upper division students are much closer to those of the instructors.

October 23, 2004 FIE Conclusions It looks like the students leaving the program are the “implementors”. The students who succeed in the program seem to be those whose Kolbe profiles are most like those of the instructors. There might be benefits to the program, and to the students, if ways could be found to retain some of these students

October 23, 2004 FIE Recommendations for Future Research Using the Kolbe index to measure incoming freshmen should be continued in order to create a larger sample. Attempts should be made to follow these students throughout their academic careers to identify the Kolbe scores of the students actually dropping out of the program. The learning approaches used should be studied to see whether they could be augmented to benefit the “implementors”.