In my previous article, our fictional startup founder Keith continued his quest to discover a solution that will increase our national high school graduation rate from just over 80% in 2014 to 90%+ by the year 2020. He narrowed the team’s focus to four key elements of the problem and with the assistance of his cognitive computing partner Herbie, the team was able to establish a strong Enterprise Data Architecture (EDA) for the project. All of this puts them in a great position to take the next step towards bringing a solid Minimum Viable Product (MVP) to market: Hypothesis Development & Testing.
An MVP is a concise summary of the smallest possible features that will work as a stand-alone product while still solving the core problem and demonstrating the product’s value. As this is our final destination for this phase of customer discovery we have a few questions we need to answer in the context of the graduation problem before we get there:
- What is the long-term product vision for the solution?; and
- What are the initial product features & benefits? (Why should people use or buy it?)
According to GradNation, the high school graduation rate, as measured by the Averaged Freshman Graduation Rate, increased from 71.7 % in 2001 to 81 % in 2012. While we are making great progress, minorities, low-income and the handicapped continue to lag behind. Our ability to reach our goal of 90%+ by 2020 will depend almost entirely on our ability to increase our performance in these three demographics. Under the new criteria set by the Every Student Succeeds Act (ESSA), low-graduation-rate high schools (dropout factories) are defined as schools that enrol 100 or more students and have graduation rates of 67 % or less.The Every Student Succeeds Act addresses the problem of high school dropout factories Click To Tweet
ESSA mandates that these schools use evidence-based reforms that correlate with measurable improvements. There is an endless combination of factors that could account for why these students are dropping out. The only way to be sure is to scientifically test each and every possible theory and to record each and every outcome. This is an extremely time-consuming and resource intensive activity that is often skipped due to either lack of resources or lack of manpower. Herbie can bring his entire cognitive computing infrastructure to bear for Keith in order to alleviate the need he would normally have for these resources. Lower costs and higher profits will be a direct result.
Herbie utilizes some of the latest machine learning algorithms available (i.e. Linear Regression, Logistic Regression, k-Means, Support Vector Machines, Random Forests, Matrix Factorization/ SVD, Gradient Boosted Decision Trees / Machine Naïve Bayes, Artificial Neural Networks, Expectation Maximization etc.) to develop and test the veracity of hundreds and thousands of hypotheses. There are 3 types of predictions typically sought from using these algorithms and they include:
- Binary classification which is used to predict the answer to a Yes/No question;
- Multiclass classification predicts the correct category from a list; and
- Regression predicts the value of a numeric variable.
The standard scientific method of putting forward a testable hypothesis that the team will use is as follows:
“If _____[I do this] _____, then _____[this]_____ will happen.”
For example, “If we assign an adult tutor to an at-risk student the very first time they score below average in a core ‘subject’ test area, then we expect that student to experience a ‘letter grade’ increase over the course of a school term”. This is one of many educated guesses that will need to be made in order to perform the necessary experiments to turn these guesses into verified facts so that real-life decisions can be made in real-time.Asking the “right” questions is critical if we want cognitive computing to give us the right answers Click To Tweet
The goal of standardizing a specific scientific hypothesis testing methodology is to ensure both data and results integrity throughout the analytical model construction process. Herbie will leverage these models to simulate and test countless variations on Keith’s hypotheses until acceptable patterns of causation begin to emerge. This will allow the team to quickly identify complex business problems that he and his team can profitably address.
Achieving a 90% graduation rate by 2020 will require focus on five key areas:
- Closing the opportunity gap for low-income students;
- Tackling big city ‘dropout factory’ challenges;
- Making handicapped students part of the solution;
- Focusing on ‘urban’ California; and
- Accelerating the lagging graduation rates for young men of color.
My next article will discuss how Herbie will help Keith the best approach to gaining unique insight into these challenges and how that insight will affect their product vision.Cognitive Computing can help close opportunity gaps for impoverished students Click To Tweet
Do you see how extensive hypothesis testing very early in the startup development process can drive long-term success? Can you see the potential time and cost savings that a cognitive computing system could offer a founder?
I look forward to hearing your feedback. Stayed tuned for more on this story.