A 2022 study by M. Yağcı, titled “Educational data mining: prediction of students’ academic achievement using machine learning algorithms,” explored how well student outcomes could be predicted using only academic records. The researcher focused on undergraduates at a Turkish state university, all enrolled in the mandatory “Turkish Language-I” course during fall 2019-2020. The dataset included 1,854 students, with only their midterm exam scores, Faculty, and Department used as predictors—no demographic or socioeconomic details.

Using these inputs, several machine learning classification models were tested to predict whether students would pass, fail, or achieve certain grades on their final exam. The models performed well, showing that early academic indicators and departmental context could help identify students who might be at risk, allowing educators to intervene before problems escalate.

What Educational Data Mining Reveals

EDM offers several key benefits:

- Early risk detection: By analyzing patterns in early assessments (like midterm scores), EDM helps flag students who may need additional support.
- Pattern discovery: It uncovers relationships between factors such as course enrollment, academic department, and student performance that may not be obvious otherwise.
- Data-driven decision making: Institutions can use these insights to allocate resources, guide interventions, and inform policies based on actual trends.
- Scalability: EDM leverages digital records, making it possible to analyze and monitor large groups of students across entire institutions rather than just small samples.

Limits of Educational Data Mining

Despite its strengths, EDM has notable limitations:

- Correlation vs. causation: EDM shows associations (e.g., midterm scores linked to final grades) but can’t prove that changing one factor will directly alter outcomes.
- Incomplete understanding: Important influences like motivation, family circumstances, or emotional well-being are often missing from the data, limiting the explanations EDM can provide.
- Context dependency: Models tailored to a specific university or course may not be transferable to other settings, as patterns can vary widely.
- Ethics and bias: If the data underrepresents certain groups or overlooks others, predictions may reinforce existing inequalities. Student privacy is also a major concern.
- Measurement gaps: Qualities like creativity, teamwork, and resilience often aren’t captured in digital records, so EDM might overlook these critical aspects.
- Prediction isn’t action: Identifying at-risk students is only the first step; effective interventions are required to actually improve outcomes.

Summary

Yağcı’s study demonstrates how educational data mining can help predict student achievement and enable proactive support. While EDM is a valuable tool for identifying risks and patterns through available academic data, it does not explain underlying causes, may miss essential factors, and does not guarantee that interventions will be successful. Responsible use of EDM requires attention to privacy, bias, and the specific educational context.