Cognitive Systems Product Vision

Cognitive Computing Systems: Want to build a great MVP? Define your Product Vision

In my last post I discussed why it was important for Keith and his team to focus on establishing a repeatable and scientifically sound method of hypothesis development and testing, and reasons why this was a critical step along the path of building an effective Minimum Viable Product (“MVP”). Now it’s time for Keith and his team to formally agree and declare their product vision for solving the graduation problem in America. This step is focused on developing a solid shortlist of product features that will be shared with the product development team.

Fictional startup founder Keith and Herbie, his interactive cognitive computing system, will now dig even deeper to investigate specific aspects of the graduation problem. GradNation Research has shown that there are four factors known to predict or exacerbate dropping out. They are as follows:

  1. Chronic Absenteeism. Missing more than 10% of the school year for any reason is an early indicator of potential dropout. Often associated with lower academic performance, this can be seen as early as first grade;
  2. Lack of Early Intervention. Middle grades are pivotal years that either set a student on the path to high school, college and a career, or a path to disengagement and low achievement in key subjects;
  3. Lack of System Visibility. There are more than six million people between the ages of 18 and 24 who currently are not in school, in possession of a high school diploma or working. These young people cannot be forgotten, and need access to pathways to education, employment and opportunities to take on the jobs of the future; and
  4. Lack of role models. Success in life cannot just come from a classroom education. Students need to develop additional skills, such as self-awareness, self-control, collaboration and conflict resolution. Young people need public, private and nonprofit agencies to work together to provide them with access to positive role models, not just adults, but also by giving them the opportunity to learn from their peers.
Missing more than 10% of a school year as early as 1st grade is likely to lead to dropping out Click To Tweet

On the surface, it is reasonable to assume that the effects of these challenges can be minimized or negated with a proactive and preventative approach. Educators should be able to predict and have sight of the small problems at-risk students are having long before they blossom into big problems.

Keith is exploring the development of iterative predictive models that will give schools the ability to identify these at-risk students very early in their school experience so that proactive actions can be taken to put them back on course. Herbie will leverage his cognitive computing infrastructure to create an effective attribute management program (this defines the data that will be used for analysis). This program will support the construction of industry standard predictive models. These models will feed an executive level scorecard that educators will use to actively and proactively manage the progress of at-risk students.

When it comes to predicting when a student is at-risk of dropping out, the sooner the better Click To Tweet

If having access to effective predictive analytics to an educator represents a major part of Keith’s product vision, then being able to actively provide that same educator with industry best practice advice as to what they should do next to address an identified problem is equally important. What specific actions should an educator take once an at-risk student has been identified?

Being notified of a student problem is not enough. What should an educator do? Click To Tweet

For example, assume that Herbie has built a predictive model that informs an educator that an individual student is falling behind in math skills. Knowing that the next math class will be even more challenging, Herbie executes a predictive model, (the high school dropout propensity score), that calculates the likelihood that the identified student will succeed in their next grade level. Depending on the results, Herbie will proactively send out a threat level notification to the proper educational stakeholder (i.e. principal, teacher or parent).

A realtime high school dropout propensity score would transform education Click To Tweet

Once the proper notifications have been seen, Herbie moves from predictive mode to prescriptive. The ability to offer real-time prescriptive advice in the context of the problem at hand is the biggest value that a cognitive computing system like Herbie can provide to an educator. Prescriptive analytics along with natural language processing is an integral part of the solution and as such will play an equally important role in the makeup of the MVP.

Real-time advice to an educator via prescriptive analytics is a key success factor Click To Tweet

Chronic absenteeism, the lack of early intervention, poor system visibility and lack of good role models are factors that are highly predictive of high school student dropout rates. I believe that a cognitive computing system working in tandem with educators is a key element to solving the graduation problem. Do you share my vision regarding the role that cognitive computing systems can play in significantly speeding up the process of identifying at-risk students, as well as offering consistent real-time advice to all stakeholders involved?

Let me know what you think.

The following two tabs change content below.

Harold Morris

Intellectually curious high technology futurist committed to building a global community of cognitive business systems thought leaders that enable creative problem solving on a community, government, corporate and global level. Global Business Market Development Expert with current interests in APAC, Africa, Canada, LATAM, Brazil, and India. Challenger sales expert in Decision Management Systems that leverage BIG DATA, Descriptive, Predictive and Prescriptive Analytics alongside Industry Domain Expertise to drive cross industry bottom-line results.
0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply