Exploring Data Analysis Techniques in Undergraduate Student Evaluation Exams in the Brazilian Context

Abstract

Abstract Accreditation and continuous assessments are crucial for ensuring quality and standards in higher education. In Brazil, the federal government also conducts an annual student assessment called Enade. This paper presents a scoping review that identifies and discusses the techniques employed in analyzing Enade data and implementing diagnostic actions to monitor the necessary competencies of graduates. The research encompassed 32 articles covering machine learning (ML), statistical techniques, and system development dedicated to the Enade exam. ML articles primarily focused on analyzing factors that impact student scores, utilizing classification and clustering approaches. Descriptive statistics emerged as the most commonly used technique in articles focusing on statistical techniques. The identified systems primarily revolved around exam administration and result analysis, with only one article exploring the implementation of gamification.

Keywords

• accreditation assessments
• Enade
• scoping review
• statistical techniques
• machine learning techniques