DCLA14: Second International Workshop on Discourse-Centric Learning Analytics Rebecca Ferguson 1 , Anna De Liddo 2 , Denise Whitelock 1 , Maarten de Laat 3 , Simon Buckingham Shum 2 The Open University UK 1 Institute of Educational Technology & 2 Knowledge Media Institute Walton Hall, Milton Keynes, MK7 6AA, UK firstname.surname@open.ac.uk 3 Open Universiteit NL LooK 6401 DL Heerlen The Netherlands maarten.delaat@ou.nl ABSTRACT The first international workshop on discourse-centric learning analytics (DCLA) took place at LAK13 in Leuven, Belgium. That workshop succeeded in its aim of catalysing ideas and building community connections between those working in this field of social learning analytics. It also proposed a mission statement for DCLA: to devise and validate analytics that look beyond surface measures in order to quantify linguistic proxies for deeper learning. This year, the focus of the second international DCLA workshop, like that of LAK14, is on the intersection of learning analytics research, theory and practice. Once researchers have developed and validated discourse-centric analytics, how can these be successfully deployed at scale to support learning? Categories and Subject Descriptors K.3.1 [Computers and Education]: Computer Uses in Education. General Terms Design. Keywords Learning Analytics; Social Learning Analytics; Dialogue; Discourse; Deliberation; Argumentation; Visualisation 1. WORKSHOP CONCEPT The success of the 1st International Workshop on Discourse- Centric Learning Analytics [1] demonstrated that an important class of learning analytic is emerging that is concerned with the use of discourse to support learning and teaching. These analytics are being developed at the intersection of fields including automated assessment, learning dynamics, deliberation platforms, and computational linguistics. What moves these developments into the category of learning analytics, as opposed to research that sits in any of the above categories, is their use or potential to generate actionable intelligence specifically in the context of learning. This may include the development of information displays that help learners and educators to understand significant discourse patterns and to reflect on learning dialogue, or tools that support interventions to improve discourse for learning. Unlike other analytics, which take measures such as engagement, attention and test scores as proxies for learning, discourse-centric analytics offer researchers from different traditions the potential to focus on the quality of the learning process, using (largely written) language as a lens on the learners’ minds. Cognitive constructivists approach this work from the perspective that engaging in online dialogue encourages learners to make explicit what they have stored in their memories, while social constructivists may be focused on the ways in which dialogue promotes the collaborative process during which meaning is negotiated and knowledge co-constructed [2]. Dialogue can reveal engagement with the ideas of others, the development of reasoning, shifts in understanding and the ways in which learners relate new ideas to personal understanding [3]. Studies have shown that, in a variety of contexts, educational success and failure can be related to the quality of learners’ dialogue [4, 5]. From an educator’s perspective, written discourse is one way of gaining insights into deeper learning and the higher order qualities associated with it, including critical thinking, argumentation and mastery of complex ideas. These skills are difficult to master and are a focus for assessment, particularly in the field of higher education. Discourse-centric learning analytics offer the possibility of using students’ written drafts to generate feedback that will help them to reflect upon and improve the ways in which they express their understanding [6]. The use of these analytics is not confined to the formal learning sector; they can also be applied to informal learning, where learners set their own goals and select their own methods of achieving them. For example, learners can use an Evidence Hub to make sense of the written ideas of a community. Algorithms can be used to identify key issues, ideas and evidence and to support the development of a reflective community of practice by making it clear where people disagree and why [7]. Potential benefits and applications of discourse-centric analytics are clear, yet their development and deployment face challenges. This workshop addresses two of these in particular: fragmentation of the field and barriers to adoption. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third- party components of this work must be honored. For all other uses, contact the Owner/Author. Copyright is held by the owner/author(s). LAK '14, Mar 24-28 2014, Indianapolis, IN, USA ACM 978-1-4503-2664-3/14/03. http://dx.doi.org/10.1145/2567574.2567631