Peer Support to Facilitate Knowledge Sharing on Complex Tasks

This thesis explores how to design a peer support system to facilitate self-organized knowledge sharing in non-formal learning environments, in particular when learners work on complex tasks. The peer support system aims to replace two teacher-led didactic arrangements: selecting a tutor at the initial stage, and guidance during the interaction process (Dillenbourg, 1999; Topping, 1996). Such a system has previously been developed by Van Rosmalen (2008) and De Bakker (2010) and has been tentatively used to facilitate knowledge sharing on content-related questions. In this thesis, we would like to find out how to further improve the design of this peer support system, especially to facilitate knowledge sharing on complex tasks. Since little pedagogical theory is available to inform the design of our peer support system, this thesis attempts to apply cognitive load theory (Sweller, Van Merriënboer, & Paas, 1998; Van Merriënboer & Sweller, 2005) that informs instructional designs in classroom settings to the design of our peer support system in Learning Networks.

ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

In this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides indepth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests.