Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Flodoard of Rheims and the Historiography of the Tenth-Century West

Flodoard of Rheims is one of the most important authors of tenth-century Europe, and the only contemporary historian to document the momentous struggles between kings and nobles in Francia in the wake of the demise of the Carolingian Empire. Flodoard’s era stands at the center of major historiographical debates concerning the nature of political and social change and the origins of European institutions. Yet, despite his singularity, his substantial histories have received little attention from scholars examining the profound transformations of the period. Exploring this discrepancy, this article offers an overview of Flodoard’s career and reviews how his histories have been invoked in some of the great scholarly debates about tenth-century Europe. It further proposes to recontextualize Flodoard and to reread his histories from the bottom up in order to gain a subtler understanding of how one contemporary perceived and represented the dramatic events and changes taking place around him

Enhancing Scientific Reasoning and Discussion with Conversational Agents

This paper investigates the use of conversational agents to scaffold online collaborative learning discussions through an approach called academically productive talk (APT). In contrast to past work on dynamic support for collaborative learning, which has involved using agents to elevate the conceptual depth of collaborative discussion by leading students in groups through directed lines of reasoning, this APT-based approach lets students follow their own lines of reasoning and promotes productive practices such as explanation of reasoning and refinement of ideas. Two forms of support are contrasted, namely, Revoicing support and Feedback support. The study provides evidence that Revoicing support resulted in significantly more intensive reasoning exchange between students in the chat and significantly more learning during the chat than when that form of support was absent. Another form of support, namely, Feedback support increased expression of reasoning while marginally decreasing the intensity of the interaction between students and did not affect learning