Detecting and Solving Negative Situations in Real CSCL Experiences with a Role-Based Interaction Analysis Approach

  1. Marcos-García, José Antonio 1
  2. Martínez-Monés, Alejandra 1
  3. Dimitriadis, Yannis 1
  4. Anguita-Martínez, Rocío 1
  5. Ruiz-Requies, Inés 1
  6. Rubia-Avi, Bartolomé 1
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Book:
Intelligent Collaborative e-Learning Systems and Applications

ISSN: 1860-949X 1860-9503

ISBN: 9783642040009

Year of publication: 2009

Pages: 129-146

Type: Book chapter

DOI: 10.1007/978-3-642-04001-6_9 DIALNET GOOGLE SCHOLAR

Metrics

Cited by

  • Scopus Cited by: 7 (24-01-2024)
  • Dimensions Cited by: 8 (19-02-2024)

SCImago Journal Rank

  • Year 2009
  • SJR Journal Impact: 0.165
  • Best Quartile: Q4
  • Area: Artificial Intelligence Quartile: Q4 Rank in area: 230/561

Dimensions

(Data updated as of 19-02-2024)
  • Total citations: 8
  • Recent citations (2 years): 0
  • Field Citation Ratio (FCR): 1.87

Abstract

Collaborative learning has a number of potential benefits, which do not always occur, partially due to the difficulties that students and teachers have to establish good social interaction patterns. These interaction patterns depend on the roles assumed by participants in the learning process. In real practice, teachers need support to be able to detect these emergent roles and undesired interaction patterns, especially if collaboration is mediated by computers, and thus is not directly observable by humans. Interaction analysis (IA) methods and tools are adequate to support the regulation of the collaborative activities, using the analysis results to provide adequate feedback to the different participants in their specific roles. We have proposed a role-based approach supported by a tool called Role-AdaptIA to detect and help to solve problematic situations in authentic computer supported collaborative learning (CSCL) scenarios. Role-AdaptIA is an adaptive interaction analysis tool based on a theoretical framework for the description of roles. The framework permits to define and characterize the roles to take into account in a given situation. Based on this information, Role-AdaptIA automatically detects role changes during the development of the collaborative experiences and warns the teacher about these changes. With this advice, the teacher is able to regulate the collaboration, providing support to the students to improve their interaction patterns. This chapter presents four examples of how Role-AdaptIA was used by teachers in order to regulate collaboration, detecting and solving undesired collaborative situations in several University courses where we have applied CSCL methods during the last years.

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