Predictive analytics uses AI to analyse historical and real-time student data to identify patterns and forecast future outcomes, such as academic performance, retention, or potential struggles.
How it Works:
Data Collection: Gathers data points like attendance, engagement with online platforms, assignment submission rates, previous grades, and demographic information.
Pattern Recognition: AI algorithms identify correlations and trends that indicate a student might be at risk. For example, a sudden drop in online activity combined with missed assignments might flag a student.
Risk Flagging: The system can then alert educators or advisors to students who are predicted to be at risk of failing, dropping out, or struggling with a particular topic.
Benefits:
Early Intervention: Allows educators to intervene before a student completely disengages or falls significantly behind.
Targeted Support: Enables schools to direct resources and support (e.g., tutoring, counselling) to students who most need it.
Improved Outcomes: Ultimately aims to improve student retention and academic success rates.
Ethical Consideration: This area is particularly sensitive due to the risk of "labelling" students based on predictions and potential biases in the data used for training. Human oversight is absolutely essential.
Checkpoint
If an AI predicts a student might struggle, what actions could educators take based on this information?
What are the ethical concerns regarding 'labelling' students based on AI predictions?