Título: Improvements on Learning Analytics approaches to minimize undergraduate evasion.

Data: 29/09/2017 Horário: 16h Local: Sala de Seminários - Bloco 952 - Campus do Pici

Resumo:

We present some improvements on Learning Analytic approaches to the student dropout problem on universities. At first, we propose a data mining technique that evaluates a curriculum’s structure based on academic data collected from Computer Science students from 2005 to 2016. The results are visualized in a user-friendly tool, which allows for contrast and comparison between the official structure and the structure found based on the data. Also, we propose a prediction strategy based on the classification with reject option paradigm. In such strategy, our method classifies students into dropout prone or non-dropout prone classes and may also reject classifying students when the algorithm does not provide a reliable prediction. The rejected students are the ones that could be classified into either class, and so are probably the ones with more chances of success when subjected to personalized intervention activities. In the proposed method, the reject zone can be adjusted so that the number of rejected students can meet the available workforce of the educational institution.

Banca:

• Profª. Drª. Emanuele Marques dos Santos (MDCC/UFC - Orientadora)
• Prof. Dr. João Paulo Pordeus Gomes (MDCC/UFC - Coorientador)
• Prof. Dr. Creto Augusto Vidal (MDCC/UFC)
• Prof. Dr. Joaquim Bento Cavalcante Neto (MDCC/UFC)