Anúncios
Seminários/datas previstos
18 fevereiro 2019, 14:21 • Cláudia Rita Ribeiro Coelho Nunes Philippart
Neste momento estão confirmados os seguintes oradores:
25/2/2019 (Anna Carolina Couto, INESC-PT e CEMAT)
06/03/2019 (Ana Bianco e Graciela Boente, Univ. Buenos Aires)
18/03/2019 (Manuela Souto Miranda, CIDMA, Universidade de Aveiro)
01/04/2019 (João Xavier, ISR e IST)
Brevemente serão divulgados os restantes seminários.
Seminário no dia 25 de Fevereiro
15 fevereiro 2019, 15:22 • Cláudia Rita Ribeiro Coelho Nunes Philippart
Informam-se os alunos que no próximo dia 25 de Fevereiro vai decorrer o 1º seminário, às 11h, na sala 3.10, pela Dra Anna Carolina Couto.
Resumo: We propose a comprehensive Learning Analytics methodology to investigate the level of understanding students achieve in the learning process. The goals of such methodology are:
1) To identify topics in which students experience difficulties on;2) To assess whether these difficulties are recurrent along semesters;3) To decide if there are conceptual associations between topics in which students experiencedifficulties on; and, more generally,4) To discover statistically significant groups of topics in which students show similar performance.
The proposed methodology uses statistics and data visualization techniques to address the first and the second goals, frequent itemset mining to tackle the third goal, and biclustering is proposed to find relationships within educational data, revealing meaningful and statistically significant patterns of students’ performance. We illustrate the application of the methodology to a Computer Science course.
1) To identify topics in which students experience difficulties on;2) To assess whether these difficulties are recurrent along semesters;3) To decide if there are conceptual associations between topics in which students experiencedifficulties on; and, more generally,4) To discover statistically significant groups of topics in which students show similar performance.
The proposed methodology uses statistics and data visualization techniques to address the first and the second goals, frequent itemset mining to tackle the third goal, and biclustering is proposed to find relationships within educational data, revealing meaningful and statistically significant patterns of students’ performance. We illustrate the application of the methodology to a Computer Science course.
Até breve!