As I discussed in my last article, with Herbie’s assistance Keith and his team determined that being able to proactively predict the likelihood that an at-risk student is going to dropout of high school would be a key component of their product vision. The other half of the equation involves the actions that an educator and the relevant stakeholders should take once a specific problem with an at-risk student has been identified. This involves a brief discussion about the science of prescriptive analytics and how Keith and his team plan to leverage this capability to help complete their product vision.
As a cognitive computing system, Herbie was instrumental in developing the analytical models (called the High School Dropout Propensity Score) needed for early identification of at-risk students. He also took the next step by leveraging prescriptive analytics to provide educators and relevant stakeholders an interactive voice that would offer them iterative advice on how to set the student back on track. This article will define prescriptive analytics, as well as the role it will play as part of our Minimum Viable Product.
Prescriptive analytics builds upon descriptive analytics and predictive analytics; it predicts what will happen, when it will happen, why it will happen and what the best course of action is to optimize outcomes and reduce risk. These solutions combine predictive models, deployment options, localized rules, scoring and optimization techniques to form a powerful foundation for decision management.Prescriptive analytics predicts what, when & why something will happen and the best course of… Click To Tweet
As a follow-on to the extensive hypothesis development and testing being continually performed by Herbie, he will begin to use those simulated outcomes to develop a prescriptive list of actions that should be taken when those “conditions” occur again in the real world. Machine learning will allow Herbie to build a comprehensive library of real-time actionable “prescriptions” that will directly impact how effectively and efficiently educators and relevant stakeholders serve their at-risk students.Machine learning can build a comprehensive library of real-time actionable “prescriptions” Click To Tweet
In the process of building this prescriptive library, Herbie and the team will focus on learning the characteristics, attributes and behaviors of the following demographics:
- Low income students: The link between low income and low academic performance is strong, but research shows it is solvable. Among non-low income students, 40 states are above the national average graduation rate of 80%. However, among low-income students, 41 states are below the national average. The good news is states with narrow achievement gaps between low-income and non-low income students appear to be those with the most robust interventions in place to counteract the effects of poverty;
- Big city dwellers: While there are nearly 200 fewer dropout factories in urban areas in 2012 than in 2002, more than half of those remaining are located in large urban areas. Most big cities with high concentrations of low-income students still have graduation rates in the 60s, with a few even in the 50s;
- Students with disabilities: The national average graduation rate for students with disabilities is 20 percentage points lower than the overall national average. While graduation rates for these students varies greatly by state, these students represent 13% of all students. Without gains nationwide, a 90% graduation rate cannot be reached;
- The state of California: As the most populous and most diverse state, California needs to be the focus of national attention and work. With the highest poverty rate in the country, a median household income 20% higher than the nation’s, and a population that is 61% non-Anglo, California is key to reaching the 90% graduation rate nationally, but it also remains a laboratory of innovation in education reform. California has 14% of the nation’s total students, and 20% of the country’s low-income student cohort. The school age population is 52% Latino and 12% Asian/Pacific Islander, with a poverty rate among school age children of 63%; and
- Young men of color: Despite gains made by all students of color over the past six years, young men of color continue to lag behind other subgroups of students. In a sub-set of Midwestern and Southern states, which educate a large percentage of African American students, graduation rates for African American males remain in the upper 50s and low 60s.
It is important to think of prescriptive analytics as one of the many successful outcomes possible with a well-designed cognitive computing system. I believe that the real value that Herbie offers the founding team is a single-minded focus on finding better approaches to solving the designated problem based on real data received from real-life attempts to solve the problem with real customers. The only way to reduce bias is to minimize the programmer’s pre-conceived notions of the problem from the equation.Cognitive systems offer founders a way to solve their problem based on real data from real… Click To Tweet
The ideal process should look like this:
- The founding team presents Herbie a problem in the form of a question;
- Herbie understands the context of the question and performs the necessary analytics to formulate an answer;
- The founding team considers the answer and makes a decision as a team;
- The founding team tells Herbie their decision. Herbie then tracks and records the results and impact of that decision; and
- Based on this data, Herbie learns lessons and adjusts his approach to thinking so that the team will achieve better results and outcomes next time.
With Herbie’s help, Keith and his team have enough information to put forward their first product vision. Their first draft sounds something like this:
- The United States used to be number one for high school graduation, but times have changed. In 2009, the U.S. ranked 21st out of 26 OECD countries when it came to high school graduation rate according to Andreas Schleicher, Deputy Director for Education for the Organization for Economic Co-operation and Development (OECD);
- Portugal and Slovenia were tied for first in the rankings, Japan and Finland hold the number two spot, and the Czech Republic ranks 17th;
- We believe that we can build a cognitive computing system that acts both as an early warning system for students at-risk of dropping out of high school and as a virtual education “doctor” capable of diagnosing in real-time the reasons why a student is likely to dropout, and then be able to tell all relevant stakeholders what specific actions they could and should take to help them qualify for graduation;
- Millions of students, parents, educators and third party stakeholders will be able to collaborate at a level that was not possible before; and
- By proactively communicating and executing best practice strategies for reducing dropout rates through the medium of their choice in real-time, we can leverage the entire global community to make sure that every child has a 100% chance of graduating.
This is just an example of the many different ways that Keith and his team could have drafted their product vision, but the documentation process is a crucial first step. It is important during these early stages that everyone agrees on a specific product vision so that the product development team has as clear a mandate as possible before building the MVP.We can leverage the entire global community to make sure that every child has a 100% chance of… Click To Tweet
Now that the team has established a certain level of clarity on the product vision, it is now time to move forward with formally defining the product features and benefits. We will cover that in my next article.
Do you understand the power of prescriptive analytics and how it might be utilized to make sure that the right action is taken at the right time? Do you agree that focusing on the five demographic areas listed above will have the biggest impact on increasing our graduation rates?
Thanks in advance for your continuing feedback.
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