In this presentation, we are going to uncover 1) why there's so much hype about AI/Machine Learning (and what these things really are) 2) Whirlwind tour of machine learning/statistics techniques and what they mean for counselors 3) Optimism for what the future brings - data as your friend rather than something to be managed.

1. Data-Driven College Counseling
How AI will change the way you
help students Succeed
Michael Discenza
Chief Data Scientist
SchooLinks | A personalized college and career readiness solution

2. About Me
● Statistics B.A. + Statistics + Machine Learning M.A. @ Columbia
● Data & Accountability Team @ Success Academies Charter
Network in NYC
● Data Science @JPMorgan -predicting company life cycle events
● Machine Learning @ RUN Ads - targeting online ads to millions
of people
● Currently Data Science @ SchooLinks
About You?

3. Setting the Stage:
● Story: Counselors as ‘data
processors’?!
● Dunbar’s Number

4. What should you take away?
1) Why there’s so much hype about AI/Machine
Learning (and what these things really are)
2) Whirlwind tour of machine learning/statistics
techniques and what they mean for you
3) Optimism for what the future brings - data as your
friend rather than something to be managed

5. 1
AI, Machine Learning,
and the Hype

6. What is Artificial Intelligence?
“The science and engineering of making intelligent machines, especially
intelligent computer programs. “
Yes, but what is intelligence?
“...the ability to achieve goals in the world. Varying kinds and degrees of
intelligence occur in people, many animals and some machines.”
http://www-formal.stanford.edu/jmc/whatisai/node1.html

7. Very Brief History of AI
1) Initial Hype - “Perceptron”
+ early advances
(1950s/60s)
2) AI Winter - cooling off of
funding and advances
3) Knowledge Engineering
4) Internet age - 1990s to
today, shift back to Data
Where are we now?
https://appliedgo.net/perceptron/

8. What is Machine Learning (ML)?
How we “do” AI in the 21st Century -- all my own definitions here
1) Learning definition: Using computer programs/mathematical
techniques to “learn” about the world and distill insights from
data, make them actionable for machines (and humans)
2) Compression definition: Taking a lot of data, extracting the
useful insights and then throwing out the rest of the data.
Example - self driving cars

9. What kind of AI are we going to talk about?
We are talking about emerging toolsets/approaches that:
1) Streamline the workflow of counselors by augmenting their intelligence with
regard to particular tasks (Counselor-Computer Symbiosis)
2) Automate decisions that are “safely automatable” and would require too much
manual work to be feasible (curriculum personalization, recommendation
engines)
We are not talking about- General Artificial
Intelligence
More long term discussion. Will certainly
change counseling (and everything else)

10. Some things to keep in mind
● Non-general machine learning systems are already better than
humans in a lot of things. Examples:
○ Loan underwriting
○ Essay grading
● Counseling is not just about predicting + deciding, it’s also about
motivating and connecting with humans (the non “data processor” part)
● Computers in general are pretty dumb- you can also be part of the
project of training them. You can encode your own knowledge and
have a part in building the tools you use

11. What does this mean for you?
● You have new toolset to:
a. Get comfortable with
b. Figure out how to use to your advantage
● Counselors are Cyborgs

12. 2
Understanding the
methods and how they
will help you

13. Prepare for Math
…well, don’t worry not too much
1) Concepts: Understand basic concepts - building blocks
2) Applications: Explore specific applications that combine these
concepts to help us solve practical problems
a) General task
b) How the math behind the technique works
c) Relevant concrete example

14. Data
● We do a lot of pre-processing/re organizing to show you these graphs and
insights
● When we talk about data, we can just think of it as spreadsheets or tables
● Just think of each row as individual case with an outcome and a series of
predictors (columns)

15. Data Collection
Schools are data rich environments - and we’re collecting more data
1) Third Party data from SIS, historical data, etc
2) Interactive Activities/Curriculum designed to surface
information
3) Behavioral Analytics

16. Unsupervised Learning
● Finding Groupings and relationships between data points
● Particularly useful in many dimensions

17. Supervised Learning:
Classification
Supervised learning where we try to find
the class or group of a case
● Most common use case is binary
classification
● Many different statistical methods
(“families of models”) can be used
● Outcome is the probability that a case
falls in a certain group
https://www.linkedin.com/pulse/support-vector-machine-srinivas-kulkarni/
Trees: “Recursive Partitioning”
Logistic Regression
Support Vector
Machines

18. Supervised Learning: Regression
Predicting continuous outcomes or
the average response/score for an
individual with x characteristics
● The way it works is we try to
identify the slope, average change
in y for a 1 unit change in x
● We can do this for linear and
nonlinear relationships

19. Model Training and Model Testing
● In supervised learning, we learn
models (which we can think of as
series of rules) from “labeled”
data
● Then we test our models on
other data to understand
whether it is reasonable to use
the output in the real world
○ We can accurately predict
labels of data that we use
specially for evaluation

20. Model Training + Algorithms
● An algorithm is just a recipe - an instruction set
● Here, learning algorithms figure out the rules we need
● Many use computers to make many calculations and try different
options to find the “best fit”- “iterative”
● Some algorithms are “greedy” and require more data, others can work
well with less data

21. Keep track of your goals through this
● Increase college going rate
● Close achievement gap in your school/district
● College retention/completion (K-16 Accountability)
● Better college fit
● Increase the number of students who have meaningful
post graduation plans

22. Outcome Prediction
Task Definition:
We have past information about
events that either occurred for past
students or did not binary
outcomes)
We want to use past data to help us
figure out the probability of the
event occurring for future students
How we do it:
Use any one of the previous classification
techniques that we discussed.
Take into consideration many different
dimensions and learn the optimal “rule set”
for each of these variables that when
combined together accurately predicts the
outcome

23. Outcome Prediction (continued)
Example:
“My Chances” - Automated Admissions decision
Predication

24. Outcome Prediction (continued)
Raw data is used to automatically classify these schools into buckets so
you can easily see if a student is applying to the number and type of
schools at a glance:

25. Intervention Optimization
Task Definition:
Identifying the series and
sequence of actions
(“interventions”) that one
should take to induce a
desired outcome.
This technique actually
comes from online ad
optimization
How we do it:
Collect data about the
timing, method, and
effect of interventions
leading to an outcome
in the past, build a
classification model to
understand the
relationship between
events and use it to
Example:
FAFSA completion
We have records of the students that
received in class training, got messages
from counselors, etc
We can tell how each incremental touch
point increases probability of student
finishing FAFSA by deadline
Suggest when is the best time for
counselors to send message to reach
student online

26. Latent Sentiment Analysis
Task Definition:
We used observed data we’ve
collected on/about subjects to
understand their latent
feelings and motivation for
making decisions and taking
actions.
How we do it:
Stage 1: Unsupervised algorithms to
understand hidden patterns in data
Stage 2: Work with counselors to
understand how findings can be useful
in their practice
Stage 3: Design better data collection
mechanisms and automated,
supervised classification algorithms
that use this data to identify a
sentiment group for students
Example:
“College Focus” gives counselors
insights into a particular student’s
motivations for going to college.
Use behavioral analytics from the
site to control for “self-reported
bias”
Provides suggestions for
counselor interaction
Cold
Cough
Runny
Nose
Sneeze

27. Causal Analysis
Task Definition:
Isolating the impact of a particular action on
an outcome or change of an outcome.
Differentiating between correlation and
causation
If we look at data around student success, we
see a lot of relationships like the one on the
right, we need to use algorithms to pull out
real causal effects to help
counselors/students make decisions that
really do impact outcomes

28. How we do it:
Propensity Score Matching is a technique that
we use to artificially create two “statistically
equal groups” that each received different
treatments
Example:
Comparing retention of different college
initiations:
Where would you send a student who got into both?
University A reported graduation rate: 45%
Causal Analysis (continued)
University B reported graduation rate: 67 %
In this cohort of equally
drop-out predisposed
students we could see
the reversal of
performance! That would
make us reconsider our
actions
(probability of graduation)

29. Content Personalization
Task Definition:
We want to use the
information that we know
about students to show them
the most relevant content
and make sure that their
curriculum is optimized for
their excitement
How we do it:
Use classification models to classify
information as relevant or interesting
for individual students
Example:

30. Recommendation Engines
Task Definition:
We have a number of “items”
we want users to check out
and we want to make sure we
have the best chance of
assigning them the ones that
they’ll like
“Netflix Algorithm”
How we do it:
Multiple methods:
Bayesian Probabilistic Matrix
Factorization
Graph Counting
Example:
“College Matches” - schools that
fit a student’s interests and
long-term goals

31. Long Term Vision for these tools
● With better tools and training, counselors can better help their
students achieve their
● That is becoming more important because of economic change
What I believe and why I’m working at Schoolinks
● Finding insights to drive human capital formation and unlock
success for students
● What we talked about here is the tip of the iceberg

32. Concluding Remarks
● Tools are your friend
○ leverage other people’s code/techniques to help you
achieve your goals
● You don’t have to understand the methods to benefit, but you
do want to be comfortable with them
● Ask questions!!!

33. Questions?
Contact Info:
mike@schoolinks.com
SchooLinks | A personalized college and career readiness solution