Learning in the 21st century means thinking in complex and collaborative ways that are situated in a real world context. This tutorial will convene a community of researchers who are examining (or interested in examining) complex thinking using epistemic network analysis (ENA). Originally designed to assess epistemic frames—collections of skills, knowledge, identities, values, and ways of making decisions—in virtual game environments, ENA is now being used broadly to quantify the structure of connections that constitute complex thinking in large-scale datasets that record discourse (chat, email, and actions) in logfiles of many kinds. Patterns of connections between elements of discourse are one important feature of action in any domain, and ENA can help researchers quantify and visualize the development of such connections over time. The goal of this pre-conference tutorial is to explore the use of ENA in diverse contexts, including log files, video game data, classroom teacher discourse, interview transcripts, and neuroscience imaging. The tutorial (1) introduce new users to this method, (2) provide further training and insight for those already using ENA, and (3) develop a broader community of users and, as a result, create opportunities for the advancement and improvement of ENA.
ENA has been developed as a tool that models the connected understanding that characterizes complex learning. In ENA, discourse is coded for the presence of key elements in the domain. For any two elements, the strength of their association in an epistemic network is computed based on the frequency of their co-occurrence in discourse—where discourse in this sense refers to recorded activity, whether verbal utterances or other actions. Using this technique, we can quantify the epistemological network of a person or group, and the evolution of such a network over time. Changes in networks (and a possible convergence towards an ideal configuration) can be measured by calculating the distance between individual networks. ENA is available free online, and participants will be able to create accounts and perform analyses with their own or sample data during the tutorial. For more information about ENA, visit http://epistemicgames.org/ena/.
We expect that participants in this tutorial will leave with a theoretical and practical understanding of ENA and as result will be able to apply the method to their future work.A similar tutorial was offered at the Computer Supported Collaborative Learning conference this past summer and was well-received. Since then additional features have been added to the ENA toolkit. Participants are encouraged (though not required) to bring their own data to conduct preliminary analyses during the tutorial.
9:00 AM – 9:30 AMIntroductions9:30 AM – 11:00 AMDownloading softwareGetting started tutorialExamples of ENA in epistemic games from an expert userCoffee break included11:00 AM – 12:00 PMExample of ENA application our novice user groupLUNCH1:00 PM – 3:00 PMParticipants run ENA on their data with assistance from expert facilitatorsCoffee break included3:00 PM – 3:30 PMPresentations from novices on findings with ENA3:30 PM – 4:00 PMFinal discussion, including feedback from users
Participants are required to bring their own laptops and are strongly encouraged to bring their own datasets for this interactive tutorial. However, data sources will also be provided. Datasets should be .csv, .xls, or .txt files that contain meta data (some sort of identifying information such as participant id numbers, gender, timestamp, etc.) and coded data (discourse that is coded for elements of interest) in columns. Please email Wesley Collier with questions/concerns about potential datasets. To register for the tutorial, please email Wesley Collier (email@example.com) and provide the following information:
- Abstract (200 words max) describing research interests, datasets, and how ENA may be a useful method for current analyses
- Registration Deadline July 1, 2014
- Tutorial Date July 1, 2014, Half day tutorial
- Please email Wesley Collier (firstname.lastname@example.org) with abstract (200 words max)
- David Williamson Shaffer, Professor of Learning Sciences, UW-Madison (email@example.com)
- Golnaz Arastoopour, Graduate Student of Learning Sciences, UW-Madison (firstname.lastname@example.org)
- Chandra Orrill, Assistant professor of STEM Education, UMass Dartmouth (email@example.com)
- Wesley Collier, Graduate Student of Learning Sciences, UW-Madison (firstname.lastname@example.org)