info:eu-repo/semantics/article
Detecting conflicts in collaborative learning through the valence change of atomic interactions
Fecha
2021-11Registro en:
Lescano, Germán Ezequiel; Torres Jimenez, José; Costaguta, Rosanna Nieves; Amandi, Analia Adriana; Lara Álvarez, Carlos; Detecting conflicts in collaborative learning through the valence change of atomic interactions; Elsevier; Expert Systems with Applications; 183; 11-2021; 1-9
0957-4174
CONICET Digital
CONICET
Autor
Lescano, Germán Ezequiel
Torres Jimenez, José
Costaguta, Rosanna Nieves
Amandi, Analia Adriana
Lara Álvarez, Carlos
Resumen
Naturally, every collaboration will bring conflicts that can affect the performance of a team. The earlier a conflict is detected and managed in a collaborative group, the better. Detecting and tracking conflicts in Computer-Supported Collaborative Learning (CSCL) is laborious work. If the teacher does it, the intervention may be out of time. Although written dialogues in groups having a conflict reveal the increment of negative emotions in comparison to non-conflict dialogues, a classifier that only uses statistics of the valence of consecutive messages in a window of the talk shows poor performance. This paper proposes to use features based on the valence change between a message and its response. In this way the algorithm focuses in the kind of interaction. We study different implementations of the bootstrap aggregating technique to detect conflicts. Results obtained show the viability of the proposed approach.