Carlson School Teaching Services

Active Learning and Learning Preferences

How to Utilize the Learning Cycle Roadmap to Advance Classroom Excellence

Dr. S. HuchendorfFounder & Director - PACE* Program – *Program for the Advancement of Classroom ExcellenceOperations & Management Sciences DepartmentCarlson School of Management, University of MinnesotaFebruary 18, 2010

Abbreviated Outline• Teaching Philosophy / Role of Instructor• CORE - Dimensions of Teaching Excellence• Chain of Learning Experiences

– Before the class – Readiness Assurance– During the class – Four Stage Model for Adult Learning

• Learning preferences and learning cycle roadmap– After the class – Case studies, projects

• Assessment – Formative / Summative• Example using Intro to Regression AnalysisActive Learning Applications from Carlson Faculty

Kevin Linderman, John Molloy, Jay Lipe

Improve the Efficiency of Total Learning

C

O

R

E

Chain of Learning Experiences

Knowledge / Comprehend

Readiness Assurance

Apply / Analyze

Learning Preferences

Learning Cycle Roadmap

Synthesize / Evaluate

Why

What

How

What If

What

Visual / Analytic / Words

Total Learning

Before Class

During Class

After Class

Formative Assessment – Rich feedback loop during the learning process

Summative Assessment – Measure level of knowledge

E

X

A

M

Teaching Philosophy / Role of Instructor

Learning is an Active Process• What can students say and do?Educational Production Function Model• Student learning produced = f (quantity and

quality of inputs into the students’ production function)– Learning experiences created by instructor

• In the classroom Student as Producer– Influence students’ time allocation decisions

Dimensions of Teaching Excellence

CORE

• Clarity – Clear, unambiguous, correctly ‘anchored’ examples that resonate

and are relevant to the audience

• Organization – Course design

– Every class session

• Rapport – Develop connections, Authenticity, Credibility

• Enthusiasm – The material is the greatest thing since sliced bread

Chain of Learning Experiences

Stair Steps of Learning

Knowledge &Comprehension

Apply &Analyze

Evaluate &Synthesize

• Creating Learning Experiences

• Three Levels of Learning – Bloom’s Taxonomy

Before Class During Class After Class

Before the Class - Readiness Assurance

• Basic Assumption - Assume students won’t read ahead– Competing uses of time

• How to Influence student time allocation decisions– Points towards final grade

• Lower levels-Bloom’s Taxonomy: Knowledge, Comprehension – Menu of Choices– IFAT – Scratch-off Quizzes

• Two attempts – Individual, Team– Moodle - Online Quizzes

• Two attempts, 10 M/C questions, 30 minute delay between attempts• Develop Reflective Learners - Diagnostic / Gap Analysis

• Class sessions are more efficient– Students are ready to learn!

Readiness Assurance

During the Class – Learning Cycle Roadmap

• How many learning styles?– Do we provide learning experiences for each?

• Kolb’s Four Stage Model of Adult Learning• Learning Preferences

– Taking information in– How you use information– Actionable – use during class sessions

• Two messages students must receive– You are interested in their learning– You are excited about the material

Four Stage Model for Adult Learning

Learning Preferences

Learning Preferences

Learning Preferences

Learning Preferences

• Which learning preference is the best?• If an instructor does not know about

learning preferences, which is best?• Which one do we teach to?• Actionable

– Develop learning experiences for each class session

• How do we create Total Learning?

Learning Cycle Roadmap• Why – Reflector

– Anticipatory Set / The Hook. Why is the material important? How does it fit into the big picture?

• What - Theorist– Provide the detailed information, the underlying theory, formulas,

videos, demonstrations, simulations, guest speakers– Bring information into long-term memory (encoding)– Provide information 1) visual, 2) analytical, 3) descriptives (words)

• How – Pragmatist– How are the concepts applied? What are the steps?

• What If – Activist– Utilize the knowledge elsewhere– ALT-CATs, Case Studies, Projects, Exercises

• Example – Introduction to Regression Analysis

Why Regression Analysis?Widely utilized statistical technique in business - Tool for

causal analysis – Measures Rate of Response• What is the impact on sales if advertising is increased by $4

M per year?• What is the effect on retail sales customer satisfaction if

check-out time is reduced by 25%?• Each additional hour of training has what type of impact on

percent defects?• Spending $4,500 on kitchen remodeling increases the selling

price of a 4-BR single-family house by how much?• What is the effect on final exam scores by utilizing

additional Readiness Assurance Quizzes?• What factors influence driving distance?

What – Bivariate Regression

Equation of a line (exact relation) Y = mX + b or Y = a + bX

Equation of a ‘best-fitting’ line in a scatterdiagram (probabilistic relation)

Deterministic component + random error component

Population Regression equation Sample Regression equation

| 0 1i y x i i iY X

0 1i i iY b b X e

Bivariate Regression

Definition of ‘best-fit’ The line that minimizes the sum of the prediction errors

squared = actual value = predicted value (from the estimated regression line) Prediction error (residual) = Find the values of that minimize the sum of

the prediction errors squared Utilize differential calculus

OR graphically – explain to upper management…..

ˆ( )i iY Y

iY

iY

0 1b and b

Best-fitting Line?

Bivariate (X)

Biv

ariate

(Y)

12108642

12

10

8

6

4

2

0

Best-Fitting Line in a Scatterplot? How large is each Squared Error?

• Draw in the ‘best-fitting’ line by hand.

• What criteria would you use to draw it in?

Best-fitting Line?

Bivariate (X)

Biv

ariate

(Y)

12108642

12

10

8

6

4

2

0

Best-Fitting Line in a Scatterplot? How large is each Error?

• Is this the line that fits the data the ‘best’?

Least Squares Criterion

Bivariate (X)

Biv

ariate

(Y)

12108642

12

10

8

6

4

2

0

S 2.31706R-Sq 72.4%R-Sq(adj) 68.4%

Fitted Line PlotBivariate(Y) = - 0.729 + 1.070 Bivariate (X)

Least Squares Criterion• Minimize the sum of the squared vertical deviations away from

the line• w/r to the choice variables

• Solve simultaneously for the choice variables (b0 and b1)

2ˆmin ( )i iY Y2

0

ˆ( )0i iY Y

b

2

1

ˆ( )0i iY Y

b

Bivariate Regression

• Many ways to mathematically express the solution to the minimization problem

0 1b Y b X

11

cov( , )( ' ) '

SSxy X Yb X X X Y

SSxx Sx

Business Application• The Cold-As-Ice Refrigerator Manufacturing

Company• Y - weekly sales per retail outlet (thous of $s)• Y = f(X’s):

– Avg price of the product (in dollars) – including promotions and discounts

– Population – number of buyers in the market (in thousands)– Dincome – Disposable Income (in hundreds of dollars)– HouseStarts – number of housing starts in the market area

• n = 60 retail outlets

Minitab Regression Output

Multiple RegressionThe Questioning Process of Data Analysis

1) What percent of the variation in the dependent variable is explained by the independent variables?

2) Does the model have significant explanatory power?3) How large is the effect of each independent variable on

the dependent variable? 4) Which independent variables have a significant influence

on the dependent variable?5) What is the predicted value of the dependent variable

given levels of the independent variables?6) Do the Classical Assumptions regarding the behavior of

the error term hold?

Coefficient of Determination – Visual Learners

1) What percent of the variation in the dependent variable is explained by the independent variables?

• R2 - Measure of explanatory power = explained variation / total variation

Total Variation = Total Sum of Squares

• SST =(This is the numerator of the standard deviation of Y)

Explained Variation = Variation in Y explained by the X’s

• SSR =

Unexplained Variation = Variation in Y that cannot be explained by the X’s

• SSE =

Coefficient of Determination – Analytic Learners

2ˆ( )iY Y

2ˆ( )iY Y2

Re ( )22

ˆ( )exp var

var ( )gression Model i

Total i

SSR Y Ylained iationR

total iation SST Y Y

Coefficient of Determination – Descriptives Learners

The Questioning Process of Data Analysis1) What percent of the variation in the dependent

variable is explained by the independent variables?

R2 = .896533

89.6533% of the total variation in weekly sales per outlet of refrigerators can be explained by the variations in the independent variables: average price, population, disposable income and housing starts

How: F-test of the Significance of the Overall Regression

2) Does the model have significant explanatory power? Conduct the F-test for the significance of the overall regression Test whether or not the model has significant explanatory power Ho: all regression slope coefficients are jointly equal to 0 Ha: not all jointly equal to 0

Restate with R2: 1) Ho: R2 = 0 vs Ha: R2 > 0 (use for assignments & exams) 2) α = .01 3) F-calc = MSRegression/MSresidual 4) df numerator = k = 4, df denominator = n-k-1 = 60-4-1 = 55 Reject Ho if F-calc > 3.65 (at 60 df)

5) F-calc = 5951.47 / 49.95 = 119.1429

• 5)

• 6) Reject Ho• 7) At the .01 level we have strong enough evidence to reject Ho

that the model has no significant explanatory power in favor of Ha that the model has significant explanatory power that is, changes in average price, population, disposable income and housing starts explains a significant portion of the variation in weekly sales.

Critical Calculated

Test stat F-crit = 3.65 F-calc = 119.1429

Probability = .01 p-value < .0001

F-test of the Significance of the Overall Regression

What If - ALT-CAT 13.2 Significance of Overall Regression

Create Total Learning

Learning Cycle Roadmap

Answers the questions of the Four Stage Model of Adult Learning

• Why

• What

• How

• What If

Types of Assessment• Formative Assessment Develop Reflective Learners

– Provide rich feedback loop improve the learning process– Collaborative, cooperative – Immediate feedback– Conduct a ‘diagnostic’. What was correct / incorrect, where are the gaps,

how to improve– Stair step up from lower levels of Bloom’s Taxonomy

• Summative Assessment– What is the attained level of knowledge – Not collaborative, not cooperative– Differentiate between students– Establish grades

• Not every assessment is at the highest levels of Bloom’s Taxonomy

ALT-CATs: Active Learning Techniques – Classroom Assessment Techniques

Active Learning Techniques (ALTs)

• Problem-based learning• Think-pair-share• Write-pair-share• One-minute paper• Application cards• Fishbowl technique• Two-column method• Buzz groups• Shared brainstorming• Focused listing

Classroom Assessment Techniques (CATs)

• Informal Assessment• Student questions, visual clues,

scan• Minute Write• Muddiest Point• Background Knowledge Probe• Invented Dialogues• Direct Paraphrasing• Misconception/Preconception

Check

ALT-CAT 13.5 Multiple RegressionLost Calls (%)The abandon rate of a call center is a critical variable in influencing customer

satisfaction. A high abandon rate indicates that customer calls are not getting their questions answered in a timely fashion resulting in high frustration levels. Management has established a target of no more than 10% abandoned calls. There are several key variables that impact the abandon rate of the call center. In the empirical regression model, these variables include the following:

– Wait time (in seconds) as measured from the first ring of the customer call until the call is answered by a Customer Service Representative

– System response time (in seconds). Length of time it takes the system to respond to a request for information

– Number of Customer Service Reps logged onto the system– Volume of calls (thousands of calls)

• Therefore, Lost calls = f(Wait time, sysresponse, CSRs, Callvol)Sample Data• The data consists of 50 different observations of lost calls(%) recorded at 10 minute

intervals throughout a day starting at 8:00 am. This data provides a ‘snapshot’ of abandon rate throughout the day at ten minute intervals.

ALT-CAT 13.5 Multiple Regression1) Analyze the relationship – identify the 4 types of information

WaitTime(sec)

Lost

Calls

(%)

403020100

30

25

20

15

10

5

Scatterplot of LostCalls(% ) vs WaitTime(sec)

ALT-CAT 13.5 Multiple Regression2) Analyze the correlation matrix. Test the significance of

the strength of the linear associations at the .01 level. Show all steps.

ALT-CAT 13.5 Multiple Regression • Analyze with the Questioning Process

ALT-CAT 13.8 Theoretical Regression Model

1. Develop a business application and select a dependent variable of interest – e.g., sales, profitability, defects, cycle time, customer satisfaction, etc.

2. Build a theoretical regression model explaining the variability of the dependent variable. Think of the model as Y = f(X1, X2, X3, …, Xk). The independent variables should be the key causal variables, whether or not the variables can be perfectly measured.

3. What is the expected sign of each independent variable? Justify your choice.

ALT-CAT 13.9 Create Exam Question• Create an exam question (and answer key) to test

knowledge of the questioning process of regression analysis1) What percent of the variation in the dependent variable is

explained by the independent variables?2) Does the model have significant explanatory power?3) How large is the effect of each independent variable on the

dependent variable? 4) Which independent variables have a significant influence on

the dependent variable?5) What is the predicted value of the dependent variable given

levels of the independent variables?6) Do the Classical Assumptions regarding the behavior of the

error term hold?

Reading List• Angelo, T.A., and Cross, K.P. (1993). Classroom assessment techniques, 2nd Ed., San Francisco: Jossey-Bass. • Atherton, J. (2002). The experiential learning cycle. http://www.dmu.ac.uk/jamesz/learning/experien.htm.• Barr, R.B., and Tagg, J. (1995). “From teaching to learning – A new paradigm for undergraduate education”. Change Magazine. Accessed

online at: http://www.kccd.cc.ca.us/kh/from_teaching_to_learning%20Barr%20Summary.htm • Bonwell, C.C., and Eison, J.A. (1996). Active learning: Creating excitement in the classroom. http://www.ntlf.com .• BusinessBalls (2001). Kolb learning styles. http://www.businessballs.com/Kolblearningstyles.htm • Eggen, P., and Kauchak, D. (2004). Educational Psychology: Windows on Classrooms, 6th ed. Upper Saddle River, NJ: Pearson

Education, Inc. • Fardouly, Niki (1998). Learning Styles and Experiential Learning [online]. Available at: http://www.fbe.unsw.edu.au • Foundation Coalition (2005). Active/Cooperative Learning (ACL). http://www.foundationcoalition.org• Galbraith, Michael W. (ed.) (2004). Adult Learning Methods – A Guide for Effective Instruction, 3rd edition. Malabar, FL: Krieger

Publishing Company. • Honey, P., & Mumford, A. (1992). The manual of learning styles. Berkshire, England: Honey, Ardingly House.• Knowles, Malcolm S., Holton III, Elwood, and Swanson, Richard A. (1998). The Adult Learner, 5th edition. Woburn MA: Butterworth –

Heinemann Publishing.• Kolb, David A. & Boyatzis, R.E., and Mainemelis, C. (2000). Experiential learning theory: Previous research and new directions. In R.J.

Sternberg & L.F. Zhang (Eds.), Perspectives on cognitive, learning, and thinking styles. NJ: Lawrence Erlbaum.• Kolb, David A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice-Hall.

• Kolb, David A. (2000). Learning style inventory. Boston: McBer. • McCarthy, B. (1987). The 4MAT System. IL: Excel Inc. • McKeachie, W.J. (2002). McKeachie’s Teaching Tips, 11th ed. Boston, MA: Houghton Mifflin Co.• Pickles, Tim. (2004). Experiential learning…on the web. http://reviewing.co.uk/research/experiential.learning.htm • Wingert, D. (August 2003). Presenting Content: Lively & Practical Approaches. A Presentation Handout for Teaching Enrichment Series:

August 2003, Center for Teaching and Learning Services, Office of Human Resources, University of Minnesota.• Wingert, D. (August 2002). Designing Effective Class Sessions. A Presentation Handout for Teaching Enrichment Series: August 2002,

Center for Teaching and Learning Services, Office of Human Resources, University of Minnesota.• World Wide Learn (2005). Learning Styles. http://www.worldwidelearn.com/elearning/learningstyles.

Improve the Efficiency of Total Learning

C

O

R

E

Chain of Learning Experiences

Knowledge / Comprehend

Readiness Assurance

Apply / Analyze

Learning Preferences

Learning Cycle Roadmap

Synthesize / Evaluate

Why

What

How

What If

What

Visual / Analytic / Words

Total Learning

Before Class

During Class

After Class

Formative Assessment – Rich feedback loop during the learning process

Summative Assessment – Measure level of knowledge

E

X

A

M

Carlson School Teaching Services

Active Learning and Learning Preferences

How to Utilize the Learning Cycle Roadmap to Advance Classroom Excellence

Dr. S. HuchendorfFounder & Director - PACE* Program – *Program for the Advancement of Classroom ExcellenceOperations & Management Sciences DepartmentCarlson School of Management, University of MinnesotaFebruary 18, 2010