Helping Teachers Handle the Flood of Data in Online Student Discussions

E-discussion tools provide students with the opportunity not only to learn about the topic under discussion but to acquire argumentation and collaboration skills and to engage in analytic thinking. However, too often, ediscussions are not fruitful and moderation is needed. We describe our approach, which employs intelligent data analysis techniques, to support teachers as they moderate multiple simultaneous discussions. We have generated six machine-learned classifiers for detecting potentially important discussion characteristics, such as a “reasoned claim” and an “argument-counterargument” sequence. All of our classifiers have achieved satisfactory Kappa values and are integrated in an online classification system. We hypothesize how a teacher might use this information by means of two authentic e-discussion examples. Finally, we discuss ways to bootstrap from these fine-grained classifications to the analysis of more complex patterns of interaction.