The effects of using student knowledge estimation in computer programming on sensor-free models of emotion confusion detection

• Tiago R. Kautzmann University of Vale do Rio dos Sinos
• Gabriel de O. Ramos University of Vale do Rio dos Sinos
• Patrícia A. Jaques Federal University of Paraná https://orcid.org/0000-0002-2933-1052

DOI: https://doi.org/10.5753/cbie_estendido.2022.226387

Abstract

Detecting student confusion allows the computational learning environment to perform actions that help students regulate their confusion and benefit from it. The Thesis research presents a hypothesis, justified in cognitive theories of emotions, that using data on student knowledge estimates can improve the performance of sensor-free models of student confusion detection in computer programming tasks. Several machine learning models were trained with data samples collected from 62 students, during five months, in programming classes. The results presented positive evidence that support the hypothesis. The research presents scenarios where the approach is more advantageous.

Keywords: Computer programming, Confusion, Emotion detection, Sensor-free model

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