Intelligent assistance for teachers in collaborative e-learning environments

Casamayor, Agustin; Amandi, Analía; Campo, Marcelo

doi:10.1016/j.compedu.2009.05.025

Cited by 84 publications

(40 citation statements)

References 13 publications

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“…Casamayor et al (2009) proposed assistance for tutors in collaborative e-learning environments. After student participation had been assessed, the rule-based assistant warned about conflictive situations to tutors, where tutor intervention might be necessary.…”

Section: Collaborative Learning Improvementsmentioning

confidence: 99%

A visual recommender tool in a collaborative learning experience

Anaya

Luque

Peinado

2016

Expert Systems with Applications

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Section: Collaborative Learning Improvementsmentioning

confidence: 99%

A visual recommender tool in a collaborative learning experience

Anaya

Luque

Peinado

2016

Expert Systems with Applications

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“…Also, the teacher could also communicate with students about deadlines, strategies, and task progress and thus also affect how group members regulate the task. Clearly, the role of the teacher in CSCL environments could be investigated further (Casamayor et al 2009). …”

Section: Group Performancementioning

confidence: 99%

Task-related and social regulation during online collaborative learning

Janssen

Erkens

Kirschner

et al. 2010

Metacognition Learning

141

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This study investigated how students collaborate in a CSCL environment and how this collaboration affects group performance. To answer these questions, the collaborative process of 101 groups of secondary education students when working on a historical inquiry task was analyzed. Our analyses show that group members devote most of their efforts to regulation of task-related activities. For example, by formulating plans or strategies or monitoring task progress. Group members also engaged in social activities often (e.g., disclosing personal information, joking). Less attention was paid to exchange of task-related information (e.g., asking task-related questions) and regulation of social activities (e.g., planning and monitoring the collaboration). Exploratory factor analysis was used to identify the interrelationships between the different collaborative activities. This analysis showed that collaborative activities can be grouped in four broad categories: discussion of information, regulation of task-related activities, regulation of social activities, and social activities. These activities were then used to predict group performance using multiple regression analysis. No effect of discussion of information and regulation of taskrelated activities on group performance were found. Regulation of social activities positively affected group performance, whereas social interaction negatively affected group performance. As in this study no inferences could be made about the causal relation between collaboration and performance, future research should attempt to focus on this relationship, for example by investigating more closely how different individual and group factors affect collaboration and group performance.

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“…Esto permite, la ejecución de un análisis tanto para detectar y caracterizar desequilibrios en la dinámica de los equipos, como para corregir y mejorar el proceso de trabajo, por medio de nuevos instrumentos de asistencia o mejorando los existentes. Adicionalmente, se han desarrollado asistentes inteligentes que, en el marco de una plataforma colaborativa y en base a una estrategia de trabajo [6], o a las interacciones del grupo [7], llevan a cabo la detección de conflictos en la dinámica grupal y alerta al supervisor de equipo para una rápida intervención, sugiriendo un plan de acciones correctivas. Por otro lado, el empleo de plataformas de videojuegos en las áreas de aprendizaje y análisis de características cuenta con numerosos precedentes [8], [9], [10], [11], [12], [13], [14].…”

Section: Introductionunclassified

Untitled

Berdún¹,

Serrano²,

Armentano³

2018

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De "discusión táctica" en juego de colaboración a "conductas": enfoque de clasificación en etapas. Abstract The analysis of group dynamics is extremely useful for understanding and predicting the performance of teamwork's, since in this context, collaboration problems can naturally arise. Artificial intelligence, and specially machine learning techniques, enables automating the observation process and the analysis of groups of users who use an online collaborative platform. Among the online collaborative platforms available, games are an attractive alternative for all audiences that enable capturing the players' behavior by observing their social interactions, while engaging them in a pleasant activity. In this paper, we present experimental results of classifying observed conversations in an online game to collaborative behaviors, guided by the Interaction Process Analysis, a theory for categorizing social interactions. The proposed automation of the classification process can be used to assist teachers or team leaders to detect alterations in the balance of group reactions and to improve their performance by indicating actions to improve the balance.Resumen El análisis de la dinámica de grupos es extremadamente útil para entender y predecir el desempeño de equipos de trabajos, puesto que en este contexto, pueden surgir naturalmente problemas de colaboración. La inteligencia artificial, y especialmente las técnicas de aprendizaje de máquina, permite automatizar el proceso de observación y análisis de grupos de usuarios que utilizan una plataforma colaborativa en línea. Entre las diversas plataformas colaborativas en línea, los juegos son una alternativa apta para todo público y permiten capturar el comportamiento de los jugadores mediante la observación de sus interacciones sociales, al mismo tiempo que los involucra en una actividad agradable. En este trabajo se presentan resultados experimentales de clasificación de conversaciones observadas en un juego en línea a conductas de colaboración, guiados por una teoría de categorización de interacciones sociales específica. La automatización propuesta se puede utilizar para asistir a profesores o líderes de equipo a detectar alteraciones en el balance de las reacciones grupales y permitirá mejorar el desempeño al indicar acciones para mejorar el equilibrio.

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Intelligent assistance for teachers in collaborative e-learning environments

Cited by 84 publications

References 13 publications

A visual recommender tool in a collaborative learning experience

A visual recommender tool in a collaborative learning experience

Task-related and social regulation during online collaborative learning

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