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John T. Behrens,Ph.D.

Pearson Center for Digital Data, Analytics, & Adaptive Learning 12 September, 2012

Pearson Research

Agenda

Pearson Research & Innovation Network

Foci of the Center

Digital Data • Games & New Genre • Big Data and Data Policy Analytics • Communication • Visualization • Data Mining & Statistics Adaptive Learning • Modeling • Recommendation • Adaptation

Three Epistemic Frames of the Center

Digitally Motivated

• Not just re-doing, but re thinking

Multi disciplinary

• Statistics, Psychology, Computing, Policy, HCI, etc

ECD centric

• Comprehensive framework and language • Extensible

Four goals

How does a small center have big impact?

Idea 1

Consider the data presented by Anscombe (1973):

• Y-mean = 7.5, sd=2.03

• X-mean = 9, sd = 3.3

• Slope = .5 with intercept of 3 • Correlation of r = .83

• Picture the data in your mind (or otherwise if you philosophically object to “ mind ” )

If you think the data look like this, then you are right

or, if you think the data look like this, then you are right

All of these patterns have the same algebraic summaries, but dramatically different data patterns!

Conclusion 1

• Algebraic summaries lie, so we need to use graphics!

Now, consider this small set of numbers which we may see in quiz scores.

• 1,1,2,2,3,3,4,4,5,5,5,5,6,6,6,6,6,6,7,7,7,7, 8,8,9,9,10,10,11,11 • Lets see how graphics can help.

Here is a histogram of the data:

Notice the structure of slight skew that we could not see in the listing of data.

Bin width = 2

Some more histograms of the same data

Bin width = 1

Even more histograms of the same data

Bin width = 2, intervals start at 1.

Bin width = 1.5, intervals start at 1.

Which is the real picture of the data?

Evolving Communication Framework

Communication Measurement Argument Data • Perception • Language of graphics • Background of audience • Intended Message or range • Cultural / Background variation • Nature of Scaling • Measurement Error • Sampling Error • Sampling Scheme • CAT Method • Test Blue Print • Amount of data • Grainsize of data

Idea 2

A general adaptivity loop

Look at Profile and choose Activity If A and B then activity = Z Give Activity& Collect WP 100 90 80 70 60 50 40 30 40 50 X1 X2 X3 X4 Xn Combine observations & Update Profile Copyright © 2012 Pearson Education, Inc. or its affiliates. Al rights reserved.

Identify features & make observations If A and B then X1 = 1 If C then X2 = “P” 23

CAT (in item paradigm)

Look at Profile and choose Activity

(To Maximize Test Information)

Give Item & Collect Answer Identify correctness make score If A and B then X1 = 1 If C then X2 = “P” & 24

The Practice Tutor

Look at Profile and choose Activity (To move in ZPD) 100 90 80 70 60 50 40 30 40 50 X1 X2 X3 X4 Xn Combine observations Update Profile & Copyright © 2012 Pearson Education, Inc. or its affiliates. Al rights reserved.

Give Activity & Collect Work Identify features make observation & If A and B then X1 = 1 If C then X2 = “P” 25

The Game

Look at Profile and choose Activity (To Maximize Motivation) Give Activity & Collect Work 100 90 80 70 60 50 40 30 40 50 X1 X2 X3 X4 Xn Combine observations Update Profile & Copyright © 2012 Pearson Education, Inc. or its affiliates. Al rights reserved.

Identify features make observation If A and B then X1 = 1 If C then X2 = “P” & 26

Wouldn’t it be interesting To work at a company that did all these things?

Your Life in the Digital Ocean

Look at Profile and choose Activity (To Maximize Whatever is needed) Give Activity & Collect Work 100 90 80 70 60 50 40 30 40 50 X1 X2 X3 X4 Xn Combine observations Update Profile & Copyright © 2012 Pearson Education, Inc. or its affiliates. Al rights reserved.

Identify features make observation If A and B then X1 = 1 If C then X2 = “P” & 28

Digital Desert Disconnected intrusions Small samples of data Special intrusive systems to get data Lack of data requires special focused inputs Absence of data requires inferential stretch Data scarcity leads to small sample science (e.g models of expertise) “Exam” ignorant of your state Data outside classroom not even considered

Digital Ocean Ongoing ubiquitous data Dramatically large and ubiquitous Data built into daily activity “Items” no longer exist Availability of data lessons need for inference Data ocean leads to improved understanding of detailed mechanisms & rules (automated automated scoring) Activity starts with access to previous history Data is data no matter where it is

And what will this do for current concepts and boundaries?

• Curriculum and Assessment?

• Games and instruction?

• Games and assessment?

• Formal and informal?

• Formative and Summative?

And perhaps more important…

Autopsy Check up Heart Monitor

Say the summary here

Thanks!