Making Placement Decisions More Transparent: How K -Means Helped Teacher Education in Indonesia

This article tells the story of how a classic statistical method—K-Means clustering—was applied in Indonesian teacher education to make student teaching placements more transparent and better aligned with student readiness. In Indonesia, preservice teachers are required to complete a period of Field Experience Practice (FEP), during which they are assigned to partner schools to conduct supervised classroom teaching. Because partner schools vary substantially in resources and mentoring quality, determining appropriate placements can be challenging. By analyzing the grades of 30 students in three compulsory courses, we grouped them into categories of “Pass,” “Considered,” and “Repeat.” The clustering revealed not only who was ready for practice but also the hidden majority of borderline students in need of additional preparation. Beyond the technical results, this case illustrates how statistics can support transparent decision-making in teacher education.