Analytics inside the classroom: an undergraduate thesis uses data mining to decide where to tune TBL
Allan Jefrey Pereira Nobre defended at FCTE/UnB an undergraduate thesis applying Learning Analytics and Educational Data Mining to support pedagogical decisions in Team-Based Learning courses.
Brasília, May 28, 2021
Active methodologies gain courses — and gain classroom questions. How to tell whether groups are actually learning? Where should the material be redosed? Which student is at risk of falling behind? The undergraduate thesis defended in May 2021 by Allan Jefrey Pereira Nobre steps in and proposes to turn Team-Based Learning activity data into pedagogical decisions — advised by Prof. Cristiane Soares Ramos .
The theoretical base is twofold: Learning Analytics — collecting and interpreting learning data to improve the process — and Educational Data Mining, which brings statistical and machine-learning tools to uncover patterns in educational data. By combining the two lenses, the author discusses how a teacher can move from subjective perception (“I think they got it”) to evidence-based observation.
The work does not treat TBL as a black box — it treats it as an instrumented system, where well-chosen collection points feed everyday decisions: which teams to call for additional discussion, where to adjust the pre-reading, when to change the difficulty level of a question.
The full text — written in Portuguese — is available at the UnB Undergraduate Theses Digital Library.
About CEDIS: The Center for Studies, Development, and Innovation in Software (CEDIS), linked to the University of Brasília, researches and develops innovative software solutions, with presence in Learning Analytics .