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:
- Could connect with the learning goals and understand what success looks like
- Were able to evidence and self-assess their learning progress
- Could use academic language to discuss their learning
- Seek specific support from the teacher
- Felt challenged in the lesson
- Felt cognitively and emotionally about their learning
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.