Analyzing Peer Influence in Ethical Judgment: Collaborative Ranking in a Case-Based Scenario

Claudio Álvarez*, Gustavo Zurita, Andrés Carvallo

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Peer influence is how an individual's beliefs, actions, and choices can be influenced by the opinions and behaviors of their peers. Peer influence can affect the moral behavior of individuals. In this study, we analyze peer influence in the context of case-based learning activity in ethics education. To conduct this type of activity, we introduce EthicRankings, a groupware environment that enables students to analyze an ethical case and reason about it by ranking the actors involved according to some ethical criteria. A study with a sample of 64 engineering students was conducted at a Latin American university to analyze peer influence from a dual standpoint in an activity comprising an individual response phase followed by a collaborative phase with anonymous chat interaction. Firstly, we determine how likely a student is to change their rankings in the collaborative phase when observing their peers’ rankings and interacting with them anonymously. Secondly, we compare positive, neutral, and negative sentiment variations in students’ written justifications for rankings before and after collaborating. Results show that students are highly likely to change their responses in the collaborative phase if their responses differ significantly from their peers’ in the individual phase. Also, sentiments in written ranking justifications vary in ways consistent with changes in ranking. The pedagogical implications of these findings are discussed.

Idioma originalInglés
Título de la publicación alojadaCollaboration Technologies and Social Computing - 29th International Conference, CollabTech 2023, Proceedings
EditoresHideyuki Takada, D. Moritz Marutschke, Claudio Alvarez, Tomoo Inoue, Yugo Hayashi, Davinia Hernandez-Leo
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas19-35
Número de páginas17
ISBN (versión impresa)9783031421402
DOI
EstadoPublicada - 2023
EventoCollaboration Technologies and Social Computing 29th International Conference, CollabTech 2023 - Osaka, Japón
Duración: 29 ago. 20231 sep. 2023

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen14199 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

ConferenciaCollaboration Technologies and Social Computing 29th International Conference, CollabTech 2023
País/TerritorioJapón
CiudadOsaka
Período29/08/231/09/23

Nota bibliográfica

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

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