Covert sex trafficking networks are increasingly using information and communication technologies (ICTs) to extend their operations. There is a need for systematic research and methods for the study of technology facilitated sex trafficking. This study examined how publicly available information can be used to uncover covert sex trafficking networks. The intent was to transform the types of data available in online advertisements into meaningful information that can be used to disrupt this activity. Content analysis was used to identify important data fields in online escort advertisement that presented virtual indicators of sex trafficking, and social network analysis methods were applied to identify provider networks.

It is of high relevance to the LAK community to explore blended learning scenarios where students can interact at diverse digital and physical learning spaces. This workshop aims to gather the sub-community of LAK researchers, learning scientists and researchers from other communities, interested in ubiquitous, mobile and/or face-to-face learning analytics. An overarching concern is how to integrate and coordinate learning analytics to provide continued support to learning across digital and physical spaces. The goals of the workshop are to share approaches and identify a set of guidelines to design and connect Learning Analytics solutions according to the pedagogical needs and contextual constraints to provide support across digital and physical learning spaces.

Learning in social settings is a complex phenomenon that involves multiple processes at individual and collective levels of agency. Thus, a richer understanding of learning in socio-technical networks will be furthered by analytic methods that can move between and coordinate analyses of individual, small group and network level phenomena. This paper outlines Traces, an analytic framework designed to address these and other needs, and gives examples of the framework's practical utility using data from the Tapped In educator professional network. The Traces framework identifies observable contingencies between events and uses these to build more abstract models of interaction and ties represented as graphs. Applications are illustrated to identification of sessions and key participants in the sessions, relations between sessions as mediated by participants, and longer-term participant roles.

The focus of this workshop is on the potential benefits and challenges of using specific computational methods to analyze interactions in networked learning environments, particularly with respect to integrating multiple analytic approaches towards understanding learning at multiple levels of agency, from individual to collective. The workshop is designed for researchers interested in analytical studies of collaborative and networked learning in socio-technical networks, using data-intensive computational methods of analysis (including social-network analysis, log-file analysis, information extraction and data mining). The workshop may also be of interest to pedagogical professionals and educational decision makers who want to evaluate the potential of learning analytics techniques to better inform their decisions regarding learning in technology-rich environments.

Learning Analytics, an emerging field concerned with analyzing the vast data "given off" by learners in technology supported settings to inform educational theory and practice, has from its inception taken a multidisciplinary approach that integrates studies of learning with technological capabilities. In this introduction to the Proceedings of the Third International Learning Analytics & Knowledge Conference, we discuss how Learning Analytics must function in the "middle space" where learning and analytic concerns meet. Dialogue in this middle space involves diverse stakeholders from multiple disciplines with various conceptions of the agency and nature of learning. We hold that a singularly unified field is not possible nor even desirable if we are to leverage the potential of this diversity, but progress is possible if we support "productive multivocality" between the diverse voices involved, facilitated by appropriate use of boundary objects. We summarize the submitted papers and contents of these Proceedings to characterize the voices and topics involved in the multivocal discourse of Learning Analytics.

Digital environments for networked learning and professional networks may not comprise one "community:" identification of clusters of affiliated groups of participants that potentially constitute embedded communities is an empirical matter, and one of interest to managers of large learning and professional networks. Also, these socio-technical networks are typically multi-mediated, in that they offer multiple means of participation, each with their own interactional affordances. Different communities may be using the multiple media in different ways. We have developed an analytic framework for extracting events from log files and representing interaction and affiliations at different granularities as needed for analysis. In this paper we show how bimodal networks of actors and media artifacts can be constructed in which directed arcs relate actors to the artifacts they read, write or edit, and how the resulting graphs can be used to detect community structures that extend across different media. We illustrate these ideas with a study that characterizes community structure within the Tapped In network of educational professionals, and how the associations between members of this network are distributed across media (chat rooms, discussion forums and file sharing).

This paper describes the rationale behind a workshop on using data-intensive computational methods of analysis for empirical-analytical studies of collaborative and networked learning, with a particular focus on how learning takes place in the technically-mediated interplay between individual, small group and collective levels of agency. This workshop is primarily designed for researchers interested in empirical-analytical studies using data-intensive computational methods of analysis (including social-network analysis, log-file analysis, data mining, video analysis).

Multi-user virtual environments (MUVEs) allow many users to explore the environment and interact with other users as they learn new content and share their knowledge with others. The semi-synchronous communicative interaction within these learning environments is typically text-based Internet relay chat (IRC). IRC data is stored in the form of chatlogs and can generate a large volume of data, posing a difficulty for researchers looking to evaluate learning in the interaction by analyzing and interpreting the patterns of communication structure and related content. This paper describes procedures for the measurement and visualization of chat-based communicative interaction in MUVEs. Methods are offered for structural analysis via social networks, and content analysis via semantic networks. Measuring and visualizing social and semantic networks allows for a window into the structure of learning communities, and also provides for a large cache of analytics to explore individual learning outcomes and group interaction in any virtual interaction. A case study on a learning based MUVE, SRI's Tapped-In community, is used to elaborate analytic methods.

Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential patterns of interaction. Transitive closure of these associograms creates sociograms, to which existing network analytic techniques may be applied, yielding aggregate results that can then be interpreted by reference to the other levels of analysis. We discuss how the analytic hierarchy bridges between levels of analysis and theory.

This paper reports an empirical study comparing the roles of representations in supporting face to face versus distance collaborative learning. findings indicate that while face to face collaborators primarily use knowledge representations to summarize verbal reasoning, distributed collaborators also use knowledge representations to propose ideas before they are discussed, particularly ideas supported by the categorical affordances of the knowledge representation medium. The work suggests that designers of online learning environments should support fluid crosstalk between multiple representations, because online discussions will be distributed across all available representations.

Pink is a system that supports threaded discussions about artifacts (such as source code or security bulletins) by making it easy for users to refer to and annotate parts of the artifacts. Pink supports multiple types of artifacts and annotation patterns while maintaining appropriate separation of content and view through a three-tier architecture. This system is implemented as server-side scripts and operates with any standard WWW browser.

Four architectures for computer supported collaborative learning systems are analyzed using the model-view-controller design pattern and compared from the standpoints of coupling between activities of the users and suitability for educational use, as well as network load and ease of implementation. The architectures are illustrated with examples from the developmental history of Belvedere, an environment for collaborative construction of knowledge representations during problem solving. A hybrid architecture that supports model-level coupling is shown to provide the best design tradeoffs.

The Learning Object Metadata (LOM) is an emerging standard for organizing descriptions of digital or nondigital entities used to support learning. The descriptions include educational, legal, and technical characteristics of these resources. The article describes an application of the LOM to the construction of a database of resources available to public schools in Hawaii and reports on issues encountered, focusing on structural issues such as dependencies between elements. The paper illustrates why the development of metadata formats cannot be divorced from an understanding of educational context.