A Glimpse Under the Hood of Verso’s Supervised Machine Learning

It is critical to the process of learning that students have the same understanding as the teacher in terms of what is going on in any lesson and what they should be learning as a result of doing. Without this critical insight, students become confined in a world of completion and compliance. It is important that the teacher articulates learning goals in language that is accessible to all students, and that they are referred to frequently, and used by students to monitor and advance their own learning.

The capacity of students to meaningfully reflect on their learning journey hinges on their connection with the learning goal. It is essential that we invest time in building connections with the how, what, why (verb, noun, context) of the lesson and develop a shared understanding of what students need to be able to do or share in order to demonstrate that they have been successful.

With this in mind, Verso uses data from over 4.3 million student responses to constantly inform the development of a supervised machine learning algorithm, designed to automatically code and display student reflection data in a way that helps teachers to instantly connect student feedback with their contextual expertise in order to meet the needs of individual students and gain insight into the precision and impact of their practice.

The following video explains the key elements that Verso’s algorithm looks for when coding student written responses, and shares how the real power of the teacher dashboard as a catalyst for change is only fully realized when it is viewed through the lens of each teacher’s context, experience and expertise.