1. The Science of Education:
Creating a Compelling Learning Experience
Dr. Angie McQuaig, Director of Data Innovation
Product Strategy & Development, Apollo Group
2. 2
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
4. 4
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
5. 5
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
7. 7
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?
8. 8
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
9. 9
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
10. 10
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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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
14. 14
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
Who we are, Apollo Group, UoPx, Adam Honea
Faculty, university leadership, technologists, more then one?
How many offer online or hybrid courses?
Ballroom, cloudy outside
The next innovation in higher education is a cloud-based EaaS (education as a service) platform, a learning platform that learns.
In this talk, we will peek under the hood to see the core tenets of this platform and how they translate into personalized learning experiences.
Dorothy is a single mom of two teenagers seeking a nursing degree. She’s a strong student generally, devours books for pleasure, comprehends well, and performs well on tests.
FILL IN!
Other questions???
What do we mean by open? Easily extensible (a la FB and others), experiences selectable by students/faculty
On bullet 2, the challenge is to recreate social setting in a class
Education decision-making often occurs in the context of three variables: evidence, values, and resources. The majority of education decisions have been based on values and resources. As education challenges grow and resources fail to keep pace, decisions must be based on a systematic appraisal of the best evidence available in the context of prevailing values and available resources. More than ever, decisions must be informed and smart.
On bullet 2, the challenge is to recreate social setting in a class
Education decision-making often occurs in the context of three variables: evidence, values, and resources. The majority of education decisions have been based on values and resources. As education challenges grow and resources fail to keep pace, decisions must be based on a systematic appraisal of the best evidence available in the context of prevailing values and available resources. More than ever, decisions must be informed and smart.
A key point to make here (from Ann Brown) is this: classroom life is synergistic: in research, aspects of it that are often treated independently (such as teacher training, curriculum selection, testing and so forth), actually form part of a system whole. Just as it is impossible to change one aspect of the system without perturbing others, so to it is difficult (or incomplete) to study any one aspect independent from the whole operating system