Introduction to the Shadow netWorkspace#8482;

Gottdenker, Joshua S.; Remidez, Herbert; Hong, Ran-Young; Yoon, So‐Yeon; Amelung, Chris; Musser, Dale; Laffey, James M.

doi:10.3115/1658616.1658703

Cited by 1 publication

(1 citation statement)

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“…Some of the systems used for teaching and learning are: SNS (Gottdenker et al, 2002), a web-based CSCL environment to support the implementation of a learning community, wherein teachers, parents and students use tools for representing, organizing and sharing knowledge; DEGREE (Barros and Verdejo, 2000), which supports the accomplishment of a variety of learning tasks in small groups of students in a asynchronous way as well as their evaluation; and Kükäkükä (Suthers and Xu, 2002), which supports referencing, manipulating and discussing about external artifacts (sketches, pictures and objects) involving students in online learning. In (McLaren and Sewall, 2006), the authors present a methodology to learn from the results obtained by the students in order to improve the collaborative learning system.…”

Section: : Computer-supported Collaborative Learning (Cscl)mentioning

confidence: 99%

The impact of learning styles on student grouping for collaborative learning: a case study

Alfonseca

Carro

Martín

et al. 2006

User Model User-Adap Inter

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract.Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of Computer Science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.

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Section: : Computer-supported Collaborative Learning (Cscl)mentioning

confidence: 99%