Explore the many ways Cypher can help your organization grow from status quo and turn data into a growth asset.
Educational Resources on Technology, Race, Education and Justice –
LEARN:
- What is big data?
- Why Big Data in Education Matters?
- How has big data impact equity and justice in education?
- How has big data in education been used against equity and justice?
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Working Paper: Big Data in Education
Download the working paper for comprehensive content or enjoy my shorten version in my Briefly about my Brief series.
How do the values of Social Emotional Learning and Culturally Responsive Leadership Align?
Black Matter(s) | Culture Matter(s)
“Cultures, whether silenced or monologistic, whether repressed or repressing, seek meaning in the language and images available to them.”
Toni Morrison – BLACK MATTER(S) Essay, The Source of Self-Regard
Curious about our Equity Labs and the problems were solving?
Learn how they can transform your organizational culture by | increase psychological safety among staff, empower collective leadership, and roadmapping new normals for success.
Thought Leadership + Meditations
My thoughts on the The Missing Year Project, discussing family dynamics, Georgetown University, and sowing seeds for dreams.
My thoughts the Radical Imagination and propelling yourself towards what lies right in front of you.
Streamable Content
How to start a career in Data Science, Business Analysis, and Information System Management |
This course helps those seeking to enter the field of Data Science, Business Analysis, and Information System Management to gain a high level understanding of the field. This includes: What is the industry like? What kind of projects/work do professionals do? How has the industry evolved? What are some of the most common interview questions? What is the career path for me in this industry, etc. |
Full Udemy Course | Or Email us directly for the full video file + a consultation.
Check out the Media Gallery for more streamable content.
More on building data literacy & bias, how algorithms shape our lives, and the role of the common good.
Quick Definitions in Big Data & Education
| Term | Definitions | Example |
| Learning Management System | a computer software used to deliver education sources & lesson, and organize learning information (grades, attendance, etc.) | Metaphorically it’s the engine in a car. Powers the car, but is usefulness is tied a series of other machines & human beings. |
| Clickstream Data | the process of tracking, reporting and delivery of educational courses, training programs | They’re footprints you leave behind when you search (walk through) a website (room). |
| Big Data | concretely it represents the computational analysis of extremely large datasets to uncover patterns and trends, particularly around human behavior. | When Uber pool connects you with a driver and other passengers who are going to the same general area & similar ratings |
| Algorithm | a detailed step-by-step instruction set or formula for solving a problem or completing a task. | A meal recipe is a non-math based algorithm |
Citations for Policy Brief Big Data in Education – 2019
Anderson, C. 2008. “The End of Theory: The Data Deluge Makes the Scientific Method
Obsolete.” Wired. http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory/.
Atila Abdulkadiroglu, Parag A. Pathak, and Alvin E. Roth, “The New York City High School
Match,” American Economic Review 95 (2) (2005): 364-–367, available at
https://seii.mit.edu/wp-content/uploads/2011/12/Paper-New-York-City-High-School-Math.pdf.
“Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of
Data.” Data-Pop Alliance White Paper Series. Data-Pop Alliance (Harvard Humanitarian
Initiative, MIT Media Lab and Overseas Development Institute) and Internews. September 2015
Broussard, M. (2014, July 15). Why Poor Schools Can’t Win at Standardized Testing. The
Atlantic. Retrieved June 12, 2019, from
https://www.theatlantic.com/education/archive/2014/07/why-poor-schools-cant-winat-
standardized-testing/374287/
Broussard, M. (2019). Artificial unintelligence: How computers misunderstand the world.
Cambridge, MA: The MIT Press.
Capatosto, Kelly (2017). “Foretelling the Future A Critical Perspective on the Use of Predictive
Analytics in Child Welfare.” Kirwan Institute Research Report. Retrieved from
Click to access ki-predictive-analytics.pdf
Daniel, B. K. (2015). Big Data and analytics in higher education: opportunities and
challenges. British Journal of Educational Technology, 46, 904–920. doi:10.1111/bjet.12230
Daniel, B. (2017) Big Data and data science: A critical review of issues for educational research.
Br. J. Educ. Technol. 2017.
David Gillborn, Paul Warmington & Sean Demack (2018) QuantCrit: education, policy, ‘Big Data’
and principles for a critical race theory of statistics, Race Ethnicity and Education, 21:2, 158-179,
DOI: 10.1080/13613324.2017.1377417
Dede, C., Ho, A., & Mitros, P. (2016). Big Data analysis in higher education: promises and
pitfalls. EDUCAUSE review August 2016 (pp. 8–9). Retrieved September 1, 2016,
from http://er.educause.edu/articles/2016/8/big-data-analysis-in-higher-education-promisesand-
pitfalls
Dougherty, Shaun M., Joshua Samuel Goodman, Darryl V. Hill, Erica G. Litke, and Lindsay
Coleman Page. 2014. Middle School Math Acceleration and Equitable Access to 8th Grade
Algebra: Evidence from the Wake County Public School System. HKS Faculty Research
Working Paper, Harvard University.
Herold, B. (2014, May 2). InBloom’s Collapse Shines Spotlight on Data-Sharing Challenges.
Retrieved June 9, 2019, from https://www.studentprivacymatters.org/newsclips/inbloomspecific-
newsclips/
Kalil, T. (2012, March 29). Big Data is a Big Deal. Retrieved June 11, 2019, from
https://obamawhitehouse.archives.gov/blog/2012/03/29/big-data-big-deal
IMPROVING OUTCOMES FOR KIDS & FAMILIES Beyond Predictive Analytics & Data Sharing
Policy Brief by IN EQUALITY / Stop the Cradle to Prison Algorithm Coalition KEY, 2019.
KSTP. (2019, January 28). St. Paul, Ramsey County, school officials dissolve joint powers
agreement. ABC Eye Witness News 5. Retrieved June 10, 2019, from https://kstp.com/news/stpaul-
ramsey-county-school-officials-dissolve-joint-powers-agreement/5225446/
Linda Darling-Hammond (2007) Race, inequality and educational accountability: the irony of ‘No
Child Left Behind’,Race Ethnicity and Education, 10:3, 245-
260, DOI: 10.1080/13613320701503207
Mayer-Schonberger, V., and K. Cukier. 2013. Big Data: A Revolution That Will Transform How
We Live, Work and Think. London: Hodder & Stoughton. Kindle Edition
McNeel, B. (2018, December 11). Dallas Hits on Successful School Turnaround Model With ACE,
but It Comes at a Steep Price. Could a Wider Expansion Across Texas Now Be Its Best Bet to
Survive? The 74. Retrieved June 8, 2019, from https://www.the74million.org/article/dallas-hitson-
successful-school-turnaround-model-with-ace-but-it-comes-at-a-steep-price-could-a-widerexpansion-
across-texas-now-be-its-best-bet-to-survive/
Melisizwe, T. (2019, January 29). Coalition to Stop the Cradle to Prison Algorithm Celebrates
Hard-Won Victory with the Dissolution of Problematic Data-Sharing Agreement. Retrieved June
9, 2019, from https://dignityinschools.org/coalition-to-stop-the-cradle-to-prison-algorithmcelebrates-
hard-won-victory-with-the-dissolution-of-problematic-data-sharing-agreement/
Naughton, J. (2016, June 26). Even algorithms are biased against black men. The Guardian.
Retrieved June 11, 2019, from
https://www.theguardian.com/commentisfree/2016/jun/26/algorithms-racial-bias-offendersflorida
ONeil, C. (2018). Weapons of math destruction: How big data increases inequality and threatens
democracy. London: Penguin Books. doi:https://doi.org/10.1111/newe.12047
Office of Transformation and Innovation: Courtney Rogers Contributors: Angie Gaylord &
Cecilia Oakeley 2018
Press Office City of New York (2018, May). Mayor de Blasio Announces First-In-Nation Task
Force To Examine Automated Decision Systems Used By The City. Retrieved July 28, 2019, from
https://www1.nyc.gov/office-of-the-mayor/news/251-18/mayor-de-blasio-first-in-nation-taskforce-
examine-automated-decision-systems-used-by
Saunders, J., Hunt, P., & Hollywood, J. S. (2016). Predictions put into practice: A quasiexperimental
evaluation of Chicago’s predictive policing pilot. Journal of Experimental
Criminology, 12(3), 347-371. doi:10.1007/s11292-016-9272-0
Tullis, T. (2014, December). How Game Theory Helped Improve New York City’s High School
Application Process. The New York Times. Retrieved August 1, 2019, from
https://www.nytimes.com/2014/12/07/nyregion/how-game-theory-helped-improve-new-yorkcity-
high-school-application-process.html
Wang, Y. (2016). Big Opportunities and Big Concerns
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