This document discusses creating a compelling learning experience through personalized and data-driven education. The goals are to improve student outcomes, understand individual learners, develop evidence-based models of learning, and create a personalized learning environment. Two learners, Dorothy and Frank, are presented as examples of how experiences should be tailored. Challenges of the approach are discussed, as well as building a flexible platform to deliver personalized guidance and continuous improvement based on learner data. The status is that research is ongoing into building such a system based on learner-centric principles.

1. The Science of Education:
Creating a Compelling Learning Experience
Dr. Angie McQuaig, Director of Data Innovation
Product Strategy & Development, Apollo Group

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Our Goals
Improve student learning outcomes & increase success in
achievement of career goals
Understand learners to individualize pathways
Develop a model of “learning and instruction rooted in a firm
empirical basis”
Use evidence from data-informed insights for continuous
improvement
Build a learner-centric environment
–great experience
–killer content
–personalized guidance
 Inspired by Ann Brown, educational researcher, UC Berkeley

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Learner-Centric Approach
Dorothy Frank

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Learner-Centric Approach
Currently school secretary
Seeking teaching degree
Strong student overall
Devours books
Motivated and devotes
adequate time to study
Struggles with writing
Demonstrates leadership
in classDorothy

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Learner-Centric Approach
Police officer
Aspires to be a HS
football coach
Volunteers at local YMCA
2.5 GPA in HS, but
motivated by football
English is 2nd language
Struggling to find time to
complete assignments
Frank

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Dorothy Frank
Should Dorothy and Frank have the same learning experience?

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Challenges
– What are our learners’ goals?
– How do we ascertain prior knowledge and skills?
– How do we model learners?
– What are the possible sets of experiences (instructional strategies,
content, assessment, interactions) available?
– What kinds of personalized guidance should we provide?
– How can we know that the guidance is effective?
– How do we understand learners’ intervention needs and create
specific guidance?
– How do teachers and intelligent agents work together effectively to
address the student needs?
– How do we measure success?
– How do we do this all cost-effectively?

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Individualized Learning Platform (ILP) Project
An open, extensible system with user options
Collaborative & social learning environment
‘Peda/andragogy-neutral’ system
Content innovations
–Get learner to ‘aha moment’ and the right Bloom’s level
Adaptive Learning Engine (ALE)
–Adaptive-Pathways construction using learner data + domain ontologies +
machine learning
–Learner Genome Project
Data-informed guidance

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Core Principles
We selected the following memes as a starter set …
Learning is a personal journey
–“What we share in common makes us human. How we differ
makes us individuals.” ~Carol Ann Tomlinson
Learning is collaborative & social
–“Real-life learning inevitably takes place in a social context,
one such setting being the classroom” ~Ann Brown
 Use evidence-based guidance where possible
–““There are many hypotheses in science which are wrong.
That's perfectly all right; they're the apertures to finding out
what's right. Science is a self-correcting process. To be
accepted, new ideas must survive the most rigorous
standards of evidence and scrutiny.” ~Dr. Carl Sagan

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Core Principles
We selected the following memes as a starter set …
Learning should be active & engaging
–“If you hold a cat by the tail you learn things you cannot learn
any other way.” ~Mark Twain
Content should be contextual and relevant
–“Learning in context is paying attention to the interaction and
intersection among people, tools, and context within a
learning situation.” ~Catherine Hansman

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Individualized Learning Environment Model
Individualized
Learning Platform
Contributions from/to
learning theories &
instructional strategies
Learner “DNA”
Teacher “DNA”
Classroom ethos
Curriculum
Intelligence Engine
(Dissemination)
Goal Completion
Satisfaction
Inputs
Outputs
Assessments (of the learning environment)
Model derived from Ann Brown’s Design Experimentation – our goal is to
put this into practice.

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Learning
Behaviors Insights
Learning
Experiences
Apollo ILP
Modeling Learners
Learner Genome Project
Learner Perception Data
Model Learners
Search for Evidence
Rules Engine
Ontologies
Machine Learning
Peda-/Andragogy-Neutral
Personalized Guidance
Dynamic Interventions
Choice and Ownership
“Ed Cloud”
BI/Hadoop Projects
Academic Analytics
Adaptive
Learning
Engine
ILP
Classroom
Technology strategy
Concepts
Strategies
Projects

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Learner Genome Project
We aim to deeply know our learners
–Demographics
–Domain-specific knowledge and skills
•Pre-requisite or composite knowledge and skills
–Behaviors and interactions in the system
–‘Cognitive DNA’
•Conative attributes
– Motivation, resistance and anxiety
•Cognitive attributes
– Metacognition, working memory, reasoning ability, modality strengths,
information literacy, etc.
•Affective attributes
– Openness, responses to interactions, emotional stability

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Platform Construction
There is no ‘one-size’ fit all solution – so a technology
platform must be:
–Highly flexible: support a variety of experiences, content
modalities, etc.
–Highly open & extensible: extend easily using 3rd-party
applications, etc.
–Highly-engaging: supporting real and relevant experiences,
content, etc.
–Data-driven: measures all interactions, move the data into
data marts as well as real-time data analytics systems, and
creates usable signals out of data
–Machine-driven: categorizes incoming students based on
learner attributes across a number of dimensions (cognitive,
affective, conative) and looks for patterns in data

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Platform Construction
–Science-driven: looks for existence of causal models
mapping learner attributes to known outcomes & measured
signals
–Evidence-driven: uses models, understands what variables
can be manipulated to create the right outcome, & continues
to evolve the system based on this knowledge becoming real
–Human-driven: combines machine insights and teacher
insights to build specific pathways for a student
–Scalable: at costs that are not prohibitive

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Translation to Platform Features
Principle Foundations Product/Platform Goals
Learning is a personal journey  Modeling the learner
 Understanding learner goals
 Understanding learner attributes
 Individualized Pathways
 Direct & Inferred measurements
 Diagnostics
 Signals (from learner, faculty, system)
 Assessments
 Remediation Plan
 Choices (content, learning tools,
assessments, …)
Learning is Collaborative and Social  Teams, Cohorts, Peers
 Different types of networks
 Interaction tools
 Communication tools
 Networks

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Translation to Platform Features
Principle Foundations Product/Platform Goals
Guidance should be Evidence-based  Empirical evidence from long-running
and statistically-significant measurements
 Signals collected from interaction
between Individuals X Materials X Tools
X Networks
 Clustering, Classifications
 Predictive Models
 Recommendations
 Insights
Learning should be Active & Engaging  Pull & Push vs. Push only
 Developing critical learning skills
 Curriculum Construction
 Assessment Construction (as a way of
reinforcing critical learning skills)
 Design of learning activities
Content should be Contextual and Relevant  Understanding Student Goals  Design of curriculum content that can be
mapped to student goals
 Sourcing & curating content from a
variety of source for freshness and
relevance
 Selection of content experiences that map
to student goals

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Challenges
Technology scale (for our size)
–Open source, mainstream web 2.0 techniques for data
handling & machine learning (ex. Hadoop)
Cost
–Infrastructure, content, …
Resistance to change
Human vs. machine perception
Gatekeepers (Institutional inertia, HLC, …)

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What all of this means
Resting on the core tenets:
–Learning is a personal journey
–Learning is collaborative and social
–Seek evidence-based guidance
–Learning should be active and engaging
–Content must relevant and contextual
Online learning is entering a new era
–Learning is personalized for an optimal experience
–Continuous improvement from examination of data
–Flexible, powerful platforms adapt based on results

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Our Status today
Exploring the literature
Tapping into the precedents of other industries
Building a flexible platform
Collaborating with scholars and practitioners who are interested
in this endeavor

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Questions?

22. Angie.McQuaig@ApolloGrp.edu
Thank you