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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Demystifying Ed-Tech Algorithms</title>
<meta name="description" content="Demystifying Ed-Tech Algorithms">
<meta name="author" content="Kris Shaffer, Ph.D.">
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<body>
<div class="reveal">
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<section data-background="images/bletchley.jpg">
<h1>Demystifying<br/>Ed-Tech Algorithms</h1>
<br/>
<h3>Kris Shaffer, Ph.D.<br/>Digital Pedagogy Lab | Yonder AI</h3>
<p>
<small><a href="https://pushpullfork.com">pushpullfork.com</a><br/><br/>view presentation (with live links) at <a href="https://kshaffer.github.io/edtechalgorithms">kshaffer.github.io/edtechalgorithms</a><br/>fork presentation at <a href="http://github.com/kshaffer/edtechalgorithms">github.com/kshaffer/edtechalgorithms</a></small>
</p>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
<p style="font-size: 1.3em">kshaffer.github.io/edtechalgorithms</p>
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
## what constitutes 'adaptive'<br/>learning?
## is algorithmic pedagogy 'personalized'?
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
## Data science is data science.
<img src="images/cookies.jpg" style="max-height: 450px"/>
source: [Hada del lago (Flickr)](https://www.flickr.com/photos/hada_del_lago/5958764969/) CC BY-NC-ND
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
## More data beats<br/>a better algorithm.
<img src="images/infographic.jpg" style="max-height: 450px"/>
source: [Terry Freedman](https://www.flickr.com/photos/terryfreedman/15697590540/)
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# machine learning:<br/>a crash course
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
Machine learning involves the use of computers to perform an algorithmic analysis of data in order to discover relationships in that data, often with the goal of making predictions about future data.
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
Gain a more nuanced understanding of relationships within the data, and make better predictions about future outcomes, than could be done by a human with pencil, paper, and a calculator.
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# classification
## categorical data
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# regression
## continuous data
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# supervised learning
Both data and outcomes are known.
Algorithm searches for relationships.
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# unsupervised learning
Data is known.
Both outcomes and relationships are emergent.
</script>
</section>
<!--
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# simple algorithms
Comparing two data parameters of equal length
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# Correlation
comparing two streams of continuous data
Ex.: Is there a relationship between number of dogs owned and annual vacuum filter purchases?
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# Chi-Squared Test
comparing two streams of categorical data
Ex.: Is there a relationship between political party membership and religious affiliation?
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# ANoVA (Analysis of Variance)
comparing a stream of categorical data with a stream of continuous data
Ex.: Is there a relationship between house architecture (ranch, colonial, brownstone, tudor, etc.) and selling price?
</script>
</section>
-->
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# supervised regression algorithms
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# linear regression
What is the relationship between contributing factor(s) and a *continuous data* result?
Ex.: What is the impact of home size and age on sale price?
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# linear regression
<img src="images/linear_regression.png" style="max-height: 450px"/>
<small>source: <a href="http://blog.rocapal.org/?p=312">Playing with machine learning: Linear Regression</a></small>
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# logistic regression
What is the relationship between contributing factor(s) and a *categorical* result?
Ex.: Do calorie intake and fat intake contribute to heart attacks?
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# supervised classification algorithms
How do specific features influence classification? How can that information be used to classify new observations?
Ex.: Voice recognition, OCR, face detection.
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# K-nearest neighbor
<img src="images/knn.png" style="max-height: 450px"/>
source: [Classification of Hand-written Digits](https://bdewilde.github.io/blog/blogger/2012/10/26/classification-of-hand-written-digits-3/)
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# decision tree
<img src="images/dt_titanic.png" style="max-height: 450px"/>
source: [Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning)
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# random forest
<img src="images/rf.jpg" style="max-height: 450px"/>
source: [Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic](http://file.scirp.org/Html/6-9101686_31887.htm)
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# neural networks
<img src="images/nn.png" style="max-height: 450px"/>
source: [Extreme Tech](http://www.extremetech.com/extreme/215170-artificial-neural-networks-are-changing-the-world-what-are-they)
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# unsupervised classification algorithms
Given a known set of features, what kinds of classifications emerge from the data?
Ex.: Recommendation systems.
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# topic modeling
<img src="images/lda.png" style="max-height: 450px"/>
source: [Topic Modeling and Network Analysis](http://www.scottbot.net/HIAL/index.html@p=221.html)
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# cluster analysis
<img src="images/clusters.png" style="max-height: 450px"/>
source: [Comparing Players Using Cluster Analysis](http://pena.lt/y/2014/02/10/comparing-players-using-cluster-analysis/)
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# collaborative filtering
Filling in missing data for one user based on data from other similar users.
OR
Creating one big super-profile for all users in a cluster.
</script>
</section>
<!--
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# sequential modeling
Given an event or sequence of events, what is the likelihood that a particular event will happen *next*?
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# markov models
<img src="images/mm.jpg" style="max-height: 450px"/>
source: [Stack Overflow](https://stackoverflow.com/questions/16852591/how-to-visualize-a-hidden-markov-model-in-python)
</script>
</section>
-->
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# exploration
[Knewton Adaptive Learning: Building the world’s most powerful education recommendation engine](https://cdn.tc-library.org/Edlab/Knewton-adaptive-learning-white-paper-1.pdf)
Mark anything that sounds like statistics, machine learning, or algorithms.
- Is it classification or regression?
- Is it supervised or unsupervised?
- What are the data sources/features?
- What are the predicted outputs?
- What is the pedagogical intention?
</script>
</section>
<section data-markdown data-background="images/bletchley.jpg">
<script type="text/template">
# going deeper
What are some algorithms or data analysis examples from your own educational practice? (or your institutions?)
What about your current pedagogy is (quasi-)algorithmic? What is the pedagogical intention?
</script>
</section>
<section data-background="images/bletchley.jpg">
<h1>Demystifying<br/>Ed-Tech Algorithms</h1>
<br/>
<h3>Kris Shaffer, Ph.D.<br/>Digital Pedagogy Lab | Yonder AI</h3>
<p>
<small><a href="https://pushpullfork.com">pushpullfork.com</a><br/><br/>view presentation (with live links) at <a href="https://kshaffer.github.io/edtechalgorithms">kshaffer.github.io/edtechalgorithms</a><br/>fork presentation at <a href="http://github.com/kshaffer/edtechalgorithms">github.com/kshaffer/edtechalgorithms</a></small>
</p>
</section>
</div>
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