Academic analytics como apoio ao sucesso na graduação: uma revisão sistemática da literatura / Academic analytics to support undergraduate success: a systematic review of the literature

A análise de dados é uma atividade essencial em diversas áreas do conhecimento. Em especial, na área de educação, também denominada academic analytics, pode-se utilizar dados acadêmicos para verificar o perfil dos estudantes e propor estratégias que determinem diferentes fatores, como o sucesso na graduação, a probabilidade de evasão/retenção ou a predição da nota do estudante em avaliações específicas. Buscando entender as diferentes estratégias  de análise de dados utilizadas na validação de técnicas com tipos de dados acadêmicos, este trabalho está sendo proposto. A pesquisa realizada trata-se de uma revisão sistemática da literatura, que contou com um conjunto de passos para ser validada. Inicialmente, foi realizada uma busca em bases de dados, acerca de trabalhos relacionados à área pretendida. Um total de 78 trabalhos foram obtidos. Destes, 18 deles foram aprovados para extração dos dados, considerando critérios de inclusão e exclusão. Dos trabalhos elencados para responder às questões de pesquisa, identificou-se que a principal característica investigada está relacionada com o sucesso dos estudantes na graduação

Utilizing Institutional Data for Curriculum Enhancement to Improve Student Success in Undergraduate Computing Programs

Student success is one of the widely discussed topics in post-secondary institutions and is measured in terms of the graduation and retention rates of programs. The goal of an educational institution is to achieve maximum student success and, hence, high graduation and retention rates. There are multiple studies on factors affecting student success. One of the factors that contributes to student success is the program curriculum. Unfortunately, the traditional program curricula at many higher education institutions were developed with a belief or assumption that all students possess equal expertise, skills, and follow a similar learning path. The traditional curricular development process neglects some specifics related to the characteristics of transfer and the First Time In College (FTIC) students and their time to graduation. The purpose of this research was to explore the relationship between the traditional program curricula and student degree mobility patterns to measure student success of transfer and FTIC students enrolled in Computer Science, Information Technology, and Computer Engineering undergraduate academic programs as well as how those relationships assist in the development and reform processes of curricula. This study was designed to understand the various aspects of program curricula, such as impacts of a program-specific factor, prerequisite, and post-requisite course requirements on time to graduation. This study leads to the development of Adaptive Curriculum Refinement, a novel approach based on institutional data analytics to assist higher education curriculum designers in the data-driven development of new curricula and data-driven revision of existing ones. The results of this study suggest a direct relationship between the curricular stringency and student time to graduation, whereas stringency was inversely related to the credit accumulation. The program-specific factor in the curriculum directly affects students\u27 time to graduation. This study is significant because the results and the development of Adaptive Curriculum Refinement could inform higher education policymakers and assist curriculum designers about the need to reform program curricula based on a data-driven and evidence-based approach to improve student success