1. Decision Based Learning
Richard Swan & Ken Plummer
Center for Teaching & Learning
Brigham Young University

2. What is Decision Based Learning?
Decision-Based Learning organizes instruction around the decisions an expert would make to solve problems in the domain.
These decisions are interconnected forming an Expert Decision Model.

3. Example of an Expert-Decision Model
A real-world problem
A network of interconnected expert decisions
Methods, techniques, or steps to perform something
Leads to solving each problem within this domain

4. Here is an example from an intermediate statistics course:
As learners take problems through the decision model, they are taught the relevant concepts / theories required to make each decision along the way.
Concepts and theories are presented in a just-in-time / just-enough fashion in order to make the next decision.
Here is an example from an intermediate statistics course:
Students are presented with real-world problems and then introduced to the decisions an expert makes to determine how to solve it.

5. Basic Statistics Problem
You have been asked to determine if older drivers drive on average faster than younger drivers.
Samples: Older 33, Younger 38
Distributions: Both Skewed
They are then presented with a high level first decision that an expert might make.
Students are presented with a problem they have not seen before.
Decide if the problem is inferential or descriptive in nature
Just in time/Just Enough Instruction
Inferential
Descriptive
And make their choice
And make their choice
Students See key word(s)
They are given just-in-time / just-enough instruction to make this decision.
Students See key word(s)
Decide if the problem deals with difference, relationship, independence, or goodness of Fit
Just in time/Just Enough Instruction
Students See key word(s)
Students See key word(s)
Relationships
Difference
Independence
 Goodness of Fit
And make their choice
And make their choice
Decide if a parametric or a non-parametric method should be used
Just in time/Just Enough Instruction
Students See key word(s)
Parametric
Non-parametric
And make their choice
Decide if one or two or more dependent variables are present
Just in time/Just Enough Instruction
1 Dependent Variable
2+ Dependent Variables
And make their choice
Students See key word(s)
After seeing this learning module the student notices there is a clue in this problem that let’s them know that this is an inferential problem
Decide if zero, one, or two or more independent variables are present
Just in time/Just Enough Instruction
0 Independent Variables
1 Independent Variables
2+ Independent Variables
Decide if two, or three or more levels are present
Just in time/Just Enough Instruction
2 levels
3+ levels
Independent Samples
t-test
They have now framed the problem correctly and are ready to solve it
One-Way ANOVA
Factorial ANOVA

6. A slight modification to the problem creates a different pathway

7. Basic Statistics Problem
You have been asked to determine if older drivers drive on average faster than younger drivers.
Samples: Older 33, Younger 38
Distributions: Both Skewed
Decide if the problem is inferential or descriptive in nature
Inferential
Descriptive
Decide if the problem deals with difference, relationship, independence, or goodness of Fit
Relationships
Difference
Independence
 Goodness of Fit
Decide if a parametric or a non-parametric method should be used
Parametric
Non-parametric
Decide if one or two or more dependent variables are present
1 Dependent Variable
2+ Dependent Variables
After seeing this learning module the student notices there is a clue in this problem that let’s them know that this is an inferential problem
Decide if zero, one, or two or more independent variables are present
0 Independent Variables
1 Independent Variables
2+ Independent Variables
Decide if two, or three or more levels are present
2 levels
3+ levels
Independent Samples
t-test
One-Way ANOVA
Factorial ANOVA

8. Basic Statistics Problem
You have been asked to determine who drives faster on average younger, middle age, or senior citizens.
Samples: young 33, mid 38, sen 43
Distributions: All normal
Decide if the problem is inferential or descriptive in nature
Inferential
Descriptive
Decide if the problem deals with difference, relationship, independence, or goodness of Fit
Relationships
Difference
Independence
 Goodness of Fit
Decide if a parametric or a non-parametric method should be used
Parametric
Non-parametric
Decide if one or two or more dependent variables are present
1 Dependent Variable
2+ Dependent Variables
Make the path way that is correct red
Decide if zero, one, or two or more independent variables are present
0 Independent Variables
1 Independent Variables
2+ Independent Variables
Decide if two, or three or more levels are present
2 levels
3+ levels
Independent Samples
t-test
Independent Samples
t-test
One-Way ANOVA
Factorial ANOVA

9. Basic Statistics Problem
You have been asked to determine who drives faster on average younger or older drivers. Determine also if car type (new or old) has an effect on driving speed.
Samples: Younger / new car = 34
Younger / old car = 55
Older / new car = 23
Older / old car = 32
Distributions: All normal
Decide if the problem is inferential or descriptive in nature
Inferential
Descriptive
Decide if the problem deals with difference, relationship, independence, or goodness of Fit
Relationships
Difference
Independence
 Goodness of Fit
Decide if a parametric or a non-parametric method should be used
Parametric
Non-parametric
Decide if one or two or more dependent variables are present
1 Dependent Variable
2+ Dependent Variables
Make the path way that is correct red
Decide if zero, one, or two or more independent variables are present
0 Independent Variables
1 Independent Variables
2+ Independent Variables
Decide if two, or three or more levels are present
2 levels
3+ levels
Independent Samples
t-test
Independent Samples
t-test
One-Way ANOVA
Factorial ANOVA

10. Basic Statistics Problem
You have been asked to determine who drives faster on average younger or older drivers.
Samples:
Younger = 23
Older = 22
Distributions: All skewed
Decide if the problem is inferential or descriptive in nature
Inferential
Descriptive
Decide if the problem deals with difference, relationship, independence, or goodness of Fit
Relationships
Difference
Independence
 Goodness of Fit
Decide if a parametric or a non-parametric method should be used
Parametric
Non-parametric
Decide if one or two or more dependent variables are present
Kruskal-Wallis Test
1 Dependent Variable
2+ Dependent Variables
Make the path way that is correct red
Decide if zero, one, or two or more independent variables are present
0 Independent Variables
1 Independent Variables
2+ Independent Variables
Decide if two, or three or more levels are present
2 levels
3+ levels
Independent Samples
t-test
Independent Samples
t-test
One-Way ANOVA
Factorial ANOVA

11. It’s not just for statistics -

12. Decide what philosophical movement is evident in this piece of art
Just in time/Just Enough Instruction
Romanticism
Realism
Post-Modernism
Vanguardism
Decide what romantic themes are evident
Just in time/Just Enough Instruction
Democracy
Power
Individuality
Liberty
Decide how that power is depicted
What is the big question? What is the next question if they choose a particular question.
What is important about this slide
Just in time/Just Enough Instruction
Metaphorically
Allegorically
 Other
Describe this art to a layperson using relevant concepts you’ve learned

13. Decision Based Learning (DBL) Pedagogy
The goal of DBL pedagogy is to help students develop,
first, a schematic structure of the problem space in a course,
second, problem solving skills that fit within that structure, and
third, conceptual / theoretical understanding that inform decisions within that structure.
Note – it is in this final instructional phase where learner knowledge becomes adaptable, flexible, and creative.

14. Theoretical Notions that Support Decision Based Learning

15. A general goal of education and training
A general goal of education and training is to help novices develop expertise (mastery, competence).

16. What does expertise look like?
Experts’ knowledge cannot be reduced to sets of isolated facts or propositions, but reflects contexts of applicability—that is the knowledge is “conditionalized.”
Bransford, Cocking & Brown, 1999
Knowledge can be categorized as—
Conceptual—“What” and “Why” i.e., concepts, ideas, theories, etc.
Procedural— “How to” i.e., procedures, equations, tools, etc.
Conditional— “When,” “Under what conditions” are conceptual and procedural knowledge relevant.
Experts notice features and meaningful patterns not noticed by novices.
Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of the subject matter.
Experts’ knowledge cannot be reduced to sets of isolated facts of propositions but, instead reflects contexts of applicability. That it he knowledge is “conditionalized” on a set of circumstances.
Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.
Experts have varying levels of flexibility in their approach to new situations.
Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.

17. What does expertise look like?
Experts notice features and meaningful patterns not noticed by novices.
Expert knowledge is organized in a way that reflects deep understanding.
Bransford, Cocking & Brown, 1999
Knowledge is organized into Schemas.
(Some schemas are more useful than others.)

18. What does expertise look like?
Experts retrieve knowledge with little attentional effort.
Experts struggle conveying elements of their expertise to novices.
Bransford, Cocking & Brown, 1999
Well-developed, familiar knowledge achieves a degree of Automaticity.
Automaticity incurs an inability to explain or to parse knowledge or performance
Experts notice features and meaningful patterns not noticed by novices.
Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of the subject matter.
Experts’ knowledge cannot be reduced to sets of isolated facts of propositions but, instead reflects contexts of applicability. That it he knowledge is “conditionalized” on a set of circumstances.
Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.
Experts have varying levels of flexibility in their approach to new situations.
Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.

19. What does expertise look like?
Experts have varying levels of flexibility in their approach to new situations.
Bransford, Cocking & Brown, 1999
Knowledge can be applied to known and analogous situations (Functional Expertise) or can be used creatively (or generated) to apply to novel situations (Effectual Expertise)
See Hatano and Inagaki, 1986
Experts notice features and meaningful patterns not noticed by novices.
Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of the subject matter.
Experts’ knowledge cannot be reduced to sets of isolated facts of propositions but, instead reflects contexts of applicability. That it he knowledge is “conditionalized” on a set of circumstances.
Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.
Experts have varying levels of flexibility in their approach to new situations.
Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.

20. Other Observations
Conditional knowledge and schema are learned tacitly through experience and have become automatic.
Automaticity obscures expert knowledge; thus, the expert is not consciously aware of these elements.
Voss, Vesonder, & Spilich, 1980; Chi, 2006
Consequently, most instruction focuses on acquisition of conceptual and procedural knowledge with little or no attention paid to conditional knowledge and schema-building.
Bransford, Cocking & Brown, 1999; Gobet, 2005

21. Summary Experts have acquired Conditional Knowledge
Experts have developed robust, refined Schemas
Experts have achieved a high degree of Automaticity
Expertise ranges from Functional to Adaptive
Experts may be unable to explain their expertise
Experts notice features and meaningful patterns not noticed by novices.
Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of the subject matter.
Experts’ knowledge cannot be reduced to sets of isolated facts of propositions but, instead reflects contexts of applicability. That it he knowledge is “conditionalized” on a set of circumstances.
Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.
Experts have varying levels of flexibility in their approach to new situations.
Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.

22. Our premises
Expertise develops from Novice to Functional to Effectual Expertise.
Conditional and Procedural schemas are the basis of Functional Expertise.
Conceptual schema-building is essential to Effectual (or adaptive) Expertise.
Ken

23. Therefore…
Conditional knowledge and schema-building are largely absent, but can be included explicitly in instruction.
Learning conditionally and building schema should be primary learning activities.
Conceptual knowledge is provided Just-in-Time, Just Enough to inform the situational need.
Emphasis shifts to conceptual schema-building as learners demonstrate conditional and procedural fluency.
Ken

24. Additional Observations
Learners practice using the decision model as they solve hundreds of problems until they internalize the map.
The decision model functions as a scaffold that fades away to help students develop greater mastery.
The following is an example of the problem pathways within a decision map for an intermediate statistics course:

25. Single-Sample Wilcoxon Test
Reporting the results for a
Single-Sample Wilcoxon Test

26. Reporting the results for a
Mann-Whitney U Test

27. Paired Sample Wilcoxon Test
Reporting the results for a
Paired Sample Wilcoxon Test

28. Reporting the results for a
Friedman Test

29. Reporting the results for a
Single-Sample z-test

30. Reporting the results for a
Two-Sample z-test

31. Reporting the results for a
Single Sample t-test

32. Independent Samples t-test
Reporting the results for a
Independent Samples t-test

33. Reporting the results for a
Paired Samples t-test

34. One-Way Analysis of Variance
Reporting the results for a
One-Way Analysis of Variance

35. One-Way Analysis of Covariance
Reporting the results for a
One-Way Analysis of Covariance

36. Repeated-Measures Analysis of Covariance
Reporting the results for a
Repeated-Measures Analysis of Covariance

37. Factorial Analysis of Variance
Reporting the results for a
Factorial Analysis of Variance

38. Split-Plot Analysis of Variance
Reporting the results for a
Split-Plot Analysis of Variance

39. Reporting the results for a
Phi-Coefficient

40. Reporting the results for a
Point-Biserial

41. Kendall’s Tau Rank-Ordered Test
Reporting the results for a
Kendall’s Tau Rank-Ordered Test

42. Spearman’s Rho Rank-Ordered Test
Reporting the results for a
Spearman’s Rho Rank-Ordered Test

43. Pearson-Product Moment Correlation
Reporting the results for a
Pearson-Product Moment Correlation

44. Reporting the results for a
Partial Correlation

45. Single-Linear Regression
Reporting the results for a
Single-Linear Regression

46. Multiple-Linear Regression
Reporting the results for a
Multiple-Linear Regression

47. Pearson Correlation Test of Independence
Reporting the results for a
Pearson Correlation Test of Independence

48. Chi-Square Test of Independence
Reporting the results for a
Chi-Square Test of Independence

49. Kendall’s Tau Test of Independence
Reporting the results for a
Kendall’s Tau Test of Independence

50. Spearman’s Rho Test of Independence
Reporting the results for a
Spearman’s Rho Test of Independence

51. Chi-Square Test of Goodness of Fit
Reporting the results for a
Chi-Square Test of Goodness of Fit

52. Reporting the results for a
Descriptive Mean

53. Reporting the results for a
Descriptive Median

54. Reporting the results for a
Descriptive Mode

55. Descriptive Inter-quartile Range
Reporting the results for a
Descriptive Inter-quartile Range

56. Descriptive Inter-quartile Range
Reporting the results for a
Descriptive Inter-quartile Range

57. Reporting the results for a
Descriptive Range

58. Reporting the results for a
Descriptive Skew

59. Reporting the results for a
Descriptive Kurtosis

60. As noted, students learn concepts along the way as needed to make decisions

61. Question?
How would textbook material fit within an Expert Decision Model?

62. Typical Textbook Table of Contents
Chapter
Topic 1
Describing & Exploring Data 2
The Normal Distribution 3
Sampling Distributions & Hypothesis Testing 4
Basic Concepts of Probability 5
Categorical Data and Chi Square 6
Hypothesis Tests Applied to Means 7
Correlation & Regression 8
Simple Analysis of Variance 9
Factorial Analysis of Variance 10
Repeated-Measures Designs 11
Multiple-Linear Regression 12
Analysis of Covariance 13
Nonparametric Approaches to Data

64. The Process at a Glance
The Expert Decision Model (EDM) is the organizing principle.
Students learn how to frame problems by walking real-world problems through decision-points of the EDM.
With conditional fluency, students learn how to perform actions or procedures to address the problem.
With functional fluency, students learn to describe the problem-space conceptually.

65. Scaffolding through Decision-based Learning Software
DBL Software provides practice and scaffolding.
Eventual removal of the software “encourages” internalization of the schema.

66. Contact – ken_plummer@byu.edu
Questions?
Contact –