Verso uses Machine Learning (ML) to code student-voice from the classroom to help teachers understand their impact and support student engagement.
Verso Learning has used the data from over 4.3 million student responses to inform the development of a supervised machine learning algorithm. This unique application has been 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 understand their impact and meet the needs of individual students.
The supervised machine learning reads each of the student responses in the Verso Check-in and displays the extent to which individual students:
Teachers around the world are using the Verso dashboards to instantly identify the individual needs of their students and inform rapid adjustments to practice. Using this data in PLCs, teachers are developing a shared understanding of what works in the classroom, setting professional goals and measuring the impact of their practice.
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.
Working in partnership with social studies learning specialists in South Carolina, we were given the task of supporting students in using high level contextual vocabulary when writing, demonstrating understanding or discussing their learning.
The district wanted to use this strategy as part of a broader initiative to build learning power in students through meaningful collaboration, and to support the transition from quantitative to qualitative learning, where understanding was prioritized over “knowing.”
We started by combining tried and tested strategies to create a sequential learning journey where, rather than merely knowing what essential vocabulary meant, students were able to build connections between key terminology to demonstrate a deeper level of understanding.
The Process
Collaborative concept mapping
Step 1. Teachers used Verso “Connect the Dots” templates to create vocabulary tickets.
Groups of students were then asked to place these on a sheet of chart paper and write down everything they associated with each keyword. Each student was given a different coloured pen, not just to provide a level of accountability, but so the teacher could see who had shared each idea and as she walked around each group, could gain insight into individual understanding and use questioning to encourage students to dig deeper.
Step 2. Once complete, students were asked to think about how each of these keywords were connected. The time had come to “connect the dots” by drawing lines between ideas that they thought were related to each other. For every line drawn, a brief explanation of the connection had to be added. (Note: Stages 1 and 2 can also be for collaborative note taking or revision)
Step 3. Now that students had surfaced their collective understanding, the teacher revealed the question she would like individual students to be able to answer in order to demonstrate their understanding.
Question: “Use evidence from your research to explain the economic, political, and social factors which lead up to the American Revolution”.
Step 4. Students were required to use a new sheet of chart paper to reorganize the vocabulary cards sequentially to create a framework for their written responses. At this point, students were asked to remove any cards they thought were not essential to answering the question but they had to be able to explain why. Each group was given blank tickets so they could add other vocabulary that they considered essential.
Once complete, students used the “three stray, one stay” learning walk approach to see how other groups had elected to organize. The person staying behind in each group was tasked to walk and talk visitors through both their thought process and rationale for arranging the vocabulary cards in the way that they did, and in doing so, share their approach to answering the question set by their teacher.
This video captures one student’s explanation to the visiting group.
On returning to their tables, students were invited to review and possibly rethink their group’s layout based on what they had seen and heard on the learning walk.
Now every student was ready to write.
Independent writing
For the written assignment, the teacher wrote the question into a Verso Collaborative Activity using a template from the Verso Library to save time. She then added all of the contextual (Tier 3) vocabulary that the students had been manipulating to develop their response to the question. She then used the Tier 2 vocabulary box to add any academic language students might need to use to develop their explanations
Step 5
Each student used their group’s vocabulary cards to develop a written response to the set question.
Once complete, students were able to view each other’s responses and the teacher could see the vocabulary that each student used highlighted in their responses, along with a word cloud showing the use of key vocabulary by the whole class.
This was used by the teachers to generate rich discussion about the reasons why some terms were more widely used than others.
Step 6: Peer Review
Finally, students were asked to use the following rubric to assess their own response, before using it to evaluate at least one other student’s work. They had to first state the level they believed each respondent was working at, offering evidence and reasons to support this assessment, and then use their vocabulary maps to make recommendations that would bump their response up to the next level.
Verso’s peer anonymity allowed students to focus on the ideas rather than the individual.
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