This article offers a vision for technology supported collaborative and discussionbased learning at scale. It begins with historical work in the area of tutorial dialogue systems. It traces the history of that area of the field of Artificial Intelligence in Education as it has made an impact on the field of Computer-Supported Collaborative Learning through the creation of forms of dynamic support for collaborative learning, and makes an argument for the importance of advances in the field of Language Technologies for this work. In particular, this support has been enabled by an integration of text mining and conversational agents to form a novel type of micro-script support for productive discussion processes. This research from the early part of the century has paved the way for emerging technologies that support discussion-based learning at scale in Massive Open Online Courses (MOOCs). In the next 25 years, we expect to see this early, emerging work in MOOC contexts grow into ubiquitously available social learning approaches in free online learning environments like MOOCs, or what comes next in the online learning space. These ambitious social learning approaches include Problem Based Learning, Team Project Based Learning, and Collaborative Reflection. We expect to see the capability of drawing in and effectively supporting learners of all walks of life, especially impacting currently under-served learners. To that end, we describe the current exploratory efforts to deploy technology supported collaborative and discussion-based learning in MOOCs and offer a vision for work going forward into the next decade, where we envision learning communities and open

We present DKPro TC, a framework for supervised learning experiments on textual data. The main goal of DKPro TC is to enable researchers to focus on the actual research task behind the learning problem and let the framework handle the rest. It enables rapid prototyping of experiments by relying on an easy-to-use workflow engine and standardized document preprocessing based on the Apache Unstructured Information Management Architecture (Ferrucci and Lally, 2004). It ships with standard feature extraction modules, while at the same time allowing the user to add customized extractors. The extensive reporting and logging facilities make DKPro TC experiments fully replicable.

Abstract.A major limitation of the current generation of MOOCs is a lack of opportunity for students to make use of each other as resources. Analyses of attrition and learning in MOOCs both point to the importance of social engagement for motivational support and overcoming difficulties with material and course procedures. In this paper we evaluate an intervention that makes synchronous collaboration opportunities available to students in an edX MOOC. We have implemented a Lobby program that students can access via a live link at any time. Upon entering the Lobby, they are matched with other students that are logged in to it. Once matched, they are provided with a link to a chat room where they can work with their partner students on a synchronous collaboration activity, supported by a conversational computer agent. Results of a survival model in which we control for level of effort suggest that having experienced a collaborative chat is associated with a slow down in the rate of attrition over time by a factor of two. We discuss implications for design, limitations of the current study, and directions for future research.

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Many interactional archetypes from outside of learning contexts are being adapted and widely used for online learning environments without consideration for some of the side effects relevant to learner outcomes. Of particular concern is the effectiveness of help exchange in these learning environments. To address this need, this article explores how the reputation system features of up/downvoting, badges, and displayed expertise impact student helper selection in a peer help exchange system within a MOOC discussion forum. We draw from Expectancy Value Theory for Help Sources as a theoretical framework for positioning the work. Results from our field experiment show that up/downvoting has a negative impact on help seeking which is mitigated by the positive effect of Help Giver badges. The mechanism behind these results are then explored in a survey experiment investigating reputation systems' impact on students' expectancies, values, and costs for a help source.