1) Being data-driven in college counseling means using analytics and data-driven research to help achieve counseling goals like increasing college attendance rates. 2) Analytics involves tracking metrics and key performance indicators to measure progress towards goals. Data can come from student information systems or college planning tools. 3) Data-driven research involves asking questions about interventions, collecting relevant data, analyzing relationships between variables, and applying findings to improve outcomes. Sample projects compare outcomes before and after implementing a new curriculum.

1. Data-Driven College Counseling
Michael Discenza
Senior Data Scientist - SchooLinks
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, @ RUN Ads (Digital ad targeting)
● Currently Data Science @ SchooLinks
About You?

3. Setting the Stage:
1) Story: What is a counselor’s
job?
2) Dunbar’s Number

4. What should you take away?
1) How being data driven counseling can help you in
counseling
2) Process/Framework to follow
3) Exposure and working knowledge of more advanced
techniques

5. What are your goals?
● Examples from folks that we work with:
○ Increase the number of students who have meaningful
post graduation plans
○ Increase college going rate
○ Close achievement gap in your school/district
○ College retention/completion (K-16 Accountability)
○ Better college fit: Increase percentage getting into top 3
college choices

6. What does being data-driven mean?
● In the business world:“Big data”
● How can we break that down?
a. Analytics: Providing unprecedented visibility into processes and
allows us to target interventions
b. Data-Driven Research: Powering research to uncover uncover
patterns that we don’t always spot on our own

7. 0) Recordkeeping + memory aids: What you’re already doing
1) Analytics: Surfacing Analytics and data to indicate progress
2) Research: Learning new things about the process to help guide
your approaches
What does this mean for you?

8. 1.1 Analytics to Quantify Goals and Progress
● Goals
● Goal Metrics
○ Outcomes (matriculation, retention)
● KPI - intermediate/progress tracking
○ Process metrics (setting up for success… completion date of
applications)
○ FAFSA Completion
○ PSAT/SAT/ACT, etc completion rates
○ College going mindset
○ Behavioral Analytics
(Operationalization)
(Operationalization)

9. ● Best Practices:
○ Pay attention to ID space -know what your key is
○ Understand the data generating process
● FERPA?
1.2 Getting Data
Use Existing:
○ SIS data - grades, attendance,
participation -> CSV export
○ College tools such as SchooLinks
and Naviance, National College
Clearinghouse data
Collect your own:
○ Structured
activities/curriculum exposure
(what they do)
○ Surveys/Questionnaires (what
they say they do and what
they think)

10. 1.4 What you do with that data
● Put it into regular reports, better yet dashboards
● Use it to suggest interventions and how to target
your efforts
○ Accountability for students - in well organized
data you can’t hide
○ Accountability for counselors - prioritization

11. 2.1 Data Driven
Research

12. 2.1 Plan
● Background research to make sure your question is a good one
for primary research:
○ can’t be more efficiently answered by reading it in a book or
elsewhere
● Make sure it will yield actionable insights
● Ensure data access/you will be able to complete the research
● List assumptions for validity

13. 2.2 Experimenting + Gathering Data
● Two main types of data:
○ Outcome data (dependent variable) - college going rate,
students who were accepted into their top 3 choices
(combination of the KPI and the goal metrics we talked about
before)
○ Treatment data (independent variable) - curriculum they used,
programs/extracurricular at schools, sentiment as reported by
surveys

14. 2.3 Preparing Data
● Combining data from different data sets: ID space (key)
○ Vlookup (Excel, Google sheets, Apple Numbers)
○ Joins - SQL, python, etc.
● Messy/missing data, outliers - what to include and not to
include?
● Visualizing data

15. 2.4 Analyzing Data
● All about the relationship between the treatment data and the
outcome data.
● Conditional probability is the most complicated math you’ll
need and most of these dynamics are really early visualized with
graphs
● You can do all of this in excel
Background: ASCA’s Making Data Work has a good review of
percentages/probability, etc focused on giving counselors the
background to do this work

16. 2.5 What to do with your findings:
● Apply them yourself
● Share them - if they’re worthwhile for you, they’re probably
worthwhile for the rest of your dept (ideally “generalizable”)
● Communicate them - for larger adoption across a Department
or funding
○ Graphs, writing, speaking
○ Keep it simple

17. 3.1 Sample Project
Question: Does the new college planning self-study curriculum we used
over the past two years have an impact 4-year the college matriculation
rate?
Data Assets: SIS contains information about a student and whether they
matriculated to college. We also have separate records of the curriculum
students used (A - the original or B- the new one)
Assumptions?

18. 3.2 Sample Data Prep

19. 3.3 Sample Data Analysis
Students who had B achieved success at
33% whereas A achieved success at
22%
0 1
A 111 33
B 81 41
Two-way table:

20. 4.0 Advanced Techniques
Concepts you should be aware of:
● Regression
● Classification
● Multivariate Analysis
● Causal Analysis
● Statistical Confidence (p values)
● Machine Learning
Goal: know how these are useful

21. Classification
Determining the class or group of a case
● Most common use case is binary
classification
● Many different statistical methods
(“families of models” can be used)
Example:
Predicted whether a student will fill out FAFSA for
by a certain date based on academic performance

22. 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
Example:
Predicting number of AP classes by house of
extracurricular activity per week

23. Multivariate Analysis
Incorporating more than one independent
variable, still only one response variable
● For classification and regression
● Think about the effect of one variable
controlling for all others
http://metabolomicsplatform.com/projects/gc-ms/
Example:
Predicted whether a student will fill out FAFSA for by a
certain date based on academic performance,
demographics, and survey data

24. Causal Analysis
Most of statistics is about correlation, sometimes
we want to have more evidence of causation- this is
a set of techniques that allows us to understand
causal connections
● Simplest technique is Propensity Score
Matching (PSM)
● Simulates random assignment to treatment
groups

25. P-values
Quantifying how certain you are that
your finding is a real finding
● Probability of seeing a result by
mere chance
● Dependent on sample size and
variability of you data
http://uk.cochrane.org/news/key-statistical-result-i
nterpretation-p-value-plain-english

26. Machine Learning
Representing all of the useful
knowledge/patterns in data and throwing
out the rest
● All about predictive accuracy vs.
statistics which is more about
assembling knowledge of the
underlying patterns that we study
● Supervised vs. Unsupervised Learning
● Many different methodologies:
decisions trees, bayesian learning,
deep learning, clustering, expectation
maximization

27. Additional Resources

28. Concluding Remarks
● Data skills - more about logic, domain knowledge, and posing
good questions rather than hard technical skills
● Get 80% of the way there with conditional probability
● Use tools to automate workflow and save time
● Ask questions... of your data, your vendors, colleagues, the
internet

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