This research aims to support collaborative distance learners by demonstrating a new way to analyze online knowledge sharing interactions. Our approach applies Hidden Markov Models and Multidimensional Scaling to analyze and assess sequences of coded online student interaction. These analysis techniques were used to train a system to dynamically recognize (1) when students are having trouble learning the new concepts they share with each other, and (2) why they are having trouble. The results of this research may assist an instructor or intelligent coach in understanding and mediat-ing situations in which groups of students collaborate to share their knowledge
A Computational Approach to Analyzing Online Knowledge Sharing Interaction
Soller, Amy Lynne;
2003-01-01
Abstract
This research aims to support collaborative distance learners by demonstrating a new way to analyze online knowledge sharing interactions. Our approach applies Hidden Markov Models and Multidimensional Scaling to analyze and assess sequences of coded online student interaction. These analysis techniques were used to train a system to dynamically recognize (1) when students are having trouble learning the new concepts they share with each other, and (2) why they are having trouble. The results of this research may assist an instructor or intelligent coach in understanding and mediat-ing situations in which groups of students collaborate to share their knowledgeI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
