Predictive Learning Analytics

Predictive Learning Analytics (PLA) is a branch of Learning Analytics that applies statistical models and machine learning to student data to forecast future academic outcomes, such as course failure or dropout risk. Its implementation is governed by AI risk management standards like ISO/IEC 23894:2023 and management systems like ISO/IEC 42001. As it processes sensitive student data, it must comply with data protection regulations like GDPR, ensuring principles of lawfulness, fairness, and transparency are upheld. In enterprise risk management, PLA is both a tool to mitigate student attrition risk and a source of operational risk, including algorithmic bias and privacy breaches, requiring a robust governance framework.

Applying PLA in an educational institution involves three key steps. 1) **Risk Identification & Governance:** Aligning with the NIST AI RMF 'Govern' function, define the model's scope and establish a data governance framework compliant with privacy laws (e.g., GDPR's purpose limitation). 2) **Model Development & Bias Mitigation:** Following guidance from ISO/IEC TR 24027:2021, integrate fairness metrics during development to detect and correct biases against specific student groups. 3) **Monitoring & Response:** Implement continuous monitoring of model accuracy and fairness post-deployment, as required by ISO/IEC 42001 for AI lifecycle management. This structured approach can reduce student dropout rates by 5-15% and improve regulatory compliance.

Taiwanese educational institutions face three main challenges. 1) **Data Silos & Quality:** Student information across different systems is often fragmented. The solution is to establish a central data warehouse and a data governance committee. 2) **Regulatory & Ethical Ambiguity:** Navigating Taiwan's Personal Information Protection Act (PIPA) and defining algorithmic fairness is complex. The solution is to conduct a Data Protection Impact Assessment (DPIA) and form an AI ethics committee guided by the NIST AI RMF. 3) **Talent Gap:** A shortage of in-house data science and AI risk expertise. The solution is to partner with external consultants to implement an ISO/IEC 42001-compliant AI management system and train internal teams.

Winners Consulting specializes in Predictive Learning Analytics for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact