What is the role of predictive analytics in identifying at-risk students, and which tools are mentioned for this purpose?

Predictive analytics anticipates academic outcomes so educators can proactively support students before they fail. It identifies at-risk students by analyzing performance, attendance, and engagement data, enabling data-driven, customized interventions in real time. Tools mentioned are BrightBytes and Altitude Learning for student performance monitoring, and Starfish Retention Solutions specifically for identifying at-risk students.

The role of predictive analytics in identifying at-risk students is to anticipate academic outcomes and enable proactive support and intervention. It works by examining data such as student performance, attendance, and engagement to spot pupils who may be at risk of falling behind, and then helps teachers create assistance plans that increase students' chances of success. According to the text, BrightBytes and Altitude Learning are AI-based programs that analyze these data to produce insights for teachers. Starfish Retention Solutions is also described as a predictive analytics tool that identifies students who are at risk of more academic difficulties than others, doing so in real time and allowing instructors to enable customized, data-driven interventions. Earlier identification through predictive analytics also supports targeted measures such as extra tutoring or resource packets.