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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
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!
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
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
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
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 13.8 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