2014-2015 Events

Annual Theme: Learning Analytics

Do counts of digital traces count for learning? (Annual Theme Series)

Friday Apr 17, 2015

Dragan Gašević, School of Education at the University of Edinburgh
Talk Abstract. The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new field learning analytics and mobilized the education sector to embrace the use of data for decision-making. This talk will first introduce the field of learning analytics and touch on lessons learned from some well-known case studies. The talk will then identify critical challenges that require immediate attention in order for learning analytics to make a sustainable impact on research and practice of learning and teaching. The talk will conclude in discussing a set of milestones selected as critical for the maturation of the field of learning analytics. The most important take away from the talk will be that learning analytics are about learning and that computational aspects of learning analytics need to be integrated deeply with educational research and practice.

Exploratory Thematic Analysis for Historical Newspaper Archives (Quantitative Humanities Series)

Wednesday Apr 15, 2015

Lauren Klein, School of Literature, Media, and Communication, Georgia Institute of Technology
Talk Abstract. How do humanities scholars make sense of new or otherwise unfamiliar archives? Is there a role for computational text analysis in the process of sensemaking? In this talk, I propose that topic modeling, when conceived as a process of thematic exploration, can provide a new entry-point into the sensemaking process. I will present new research from the Georgia Tech Digital Humanities Lab on a software tool called TOME: Interactive TOpic Model and MEtadata Visualization, designed to support the exploratory thematic analysis of digitized archival collections. TOME is centered around a set of visualizations intended to facilitate the interpretation of the topic model and its incorporation into extant humanities research practices. In contrast to other topic model browsers, which present the model on its own terms, ours is informed by the process of conducting early-stage humanities research. This talk will thus also demonstrate the conceptual conversions--in terms of both design and process-- that interdisciplinary collaboration necessarily entails. In making these conversions explicit, and exploring the implications of their successes and failures, my collaborators and I take up the call, as voiced by Johanna Drucker (2011), to resist the “intellectual Trojan horse” of visualization. We seek to model a new mode of interdisciplinary inquiry, one that brings the methodological emphasis of the digital humanities to bear on the practices of humanities research and computer science alike.

Workshop: Data Scraping for Non-Programmers

Monday Apr 13, 2015

This workshop was designed to provide an introduction to web scraping for non-programmers. Scraping is the method by which researchers collect large online data quickly and efficiently. We used a free point-and-click software program called OutWit Hub to explore this method. We began by explaining what scraping is and how it works. Along the way, we learned how to navigate around a website’s structure, identify the information we need, and extract and output it as data. We also explored some of the common misconceptions and legal implications of scraping copyrighted content. Finally, we got hands-on experience with this powerful tool. By the end of this workshop, participants were able to use this method on their own. Led by Trent Ryan

DataFest 2015

Apr 10 - 12, 2015

A weekend-long data analysis competition hosted by the Institute of Quantitative Theory and Methods and developed by the American Statistical Association. We give you the data set; you draw the interesting insights.
Event Details

Is ‘Big Data’ a Cause or an Effect? Filtering Signals from Noise (Special Lecture Series)

Wednesday Apr 8, 2015

Krishna Rupanagunta, Mu Sigma Inc.
Talk Abstract. The speed and complexity of business problems are accelerating like never before. We believe the companies that will succeed in a world of increasing change are the ones who are able to benefit from this change. Decision Sciences and Big Data Analytics provide a powerful opportunity for companies to turn the complexity of their business environments to their advantage. This lecture will discuss how companies are adopting a new ‘Art of Problem Solving’ – and what you can expect from this vastly growing arena.
This event was co-sponsored by QTM and the Emory Data Analytics Club

Hail to the Data: What We're Learning from Learning Analytics (Annual Theme Series)

Wednesday Apr 8, 2015

Timothy McKay, Arthur F. Thurnau Professor of Physics, University of Michigan
Talk Abstract. At the University of Michigan today, many interactions among teachers and students are mediated by technology. Students use clickers in class, do homework online, write and revise papers and project in the cloud, and produce video of presentations. This 'digital exhaust' gives us unprecedented opportunities to understand teaching and learning and improve student success. A new field of Learning Analytics is emerging to take advantage of this opportunity. Professor Timothy McKay’s presentation will introduce this topic using examples from a variety of local projects.

Neural Computations Underlying Acoustic Communication in Drosophila (Collective Computation in Biological Communication, Neural Dynamics, & Behavior Series)

Wednesday April 1, 2015

ala Murthy, Princeton Neuroscience Institute, Department of Molecular Biology, Princeton University
Talk Abstract. This talk addresses the goal of our research: to discover fundamental principles about sensory perception, sensorimotor integration, and the generation of behavior. To make these discoveries, we focus primarily on the acoustic communication system of Drosophila. Similar to other animals, flies produce and process patterned sounds during their mating ritual: males generate songs via wing vibration, while females arbitrate mating decisions. I will discuss how our studies, using quantitative behavior, in vivo electrophysiology, computational modeling, and genetic tools, address the neural mechanisms underlying both the production and perception of dynamic courtship songs in Drosophila.

Southeast Education Data Symposium

Friday Feb 20, 2015

The Southeast Educational Data Symposium (SEEDS) brought together administrators, researchers, and instructors to share how they are making use of educational data to foster student success, and to generate opportunities for ongoing collaboration in the Southeast region. The day’s schedule included a morning keynote, delivered by Carolyn Rosé (Carnegie Mellon University), followed by four panel discussions and lunch-time roundtables.
Event Details.

Decision Making in Animal Groups (Collective Computation in Biological Communication, Neural Dynamics, & Behavior Series)

Wednesday Feb 18, 2015

Gonzalo de Polavieja, Champalimaud Neuroscience Programme, Champalimaud Foundation

part 1

part 2

Talk Abstract. I talk about a theoretical approach to collective decisions that works well across species. I also take the opportunity to present idTracker (www.idtracker.es), software that analyses video to identify each individual in a group. This identification is used for tracking without propagation of mistakes, thus obtaining large amounts of high quality data. I end my talk with applications of our models to understand aggregation in adverse conditions, how humans make estimations in groups, and how we can improve them

Towards Long-Term and Actionable Prediction of Student: Outcomes Using Automated Detectors of Engagement and Affect (Annual Theme Series)

Friday Feb 9, 2015

Ryan Baker, The Teachers College, Columbia University
Talk Abstract. In recent years, researchers have been able to model an increasing range of aspects of student interaction with online learning environments, including affect, meta-cognition, robust learning, and engagement. In this talk, I discuss how automated detectors of engagement and learning can be used in prediction of long-term student outcomes, illustrating this with examples of how affect, engagement, and learning during middle school use of educational software can support prediction of student long-term success, including end-of-year learning, decisions about whether to attend college, and even what major a student chooses. These predictive models can in turn support inference about what factors make a specific student at- risk for poorer learning or lower long-term engagement in learning.

Maximally Informative Behaviors Implemented by Simple Neural Circuits (Collective Computation in Biological Communication, Neural Dynamics, & Behavior Series)

Wednesday Jan 14, 2015

Tatyana Sharpee, Computational Neurobiology Laboratory, Salk Institute for Biological Studies
Talk Abstract. In this talk, I show that the foraging patterns of a small nematode, C. elegans, can be accurately described by theories of maximally informative search strategies. Further it is possible to design environmental conditions for C. elegans where worm foraging patterns follow maximally informative search strategies that are in direct contrast to chemotaxis predictions. in order to perform a maximally informative search, animals technically need to maintain a full mental map for the likelihood distribution of food throughout the environment. However, my colleagues and I find that this search can be approximated well (under conditions of our experiments) with a simple drift-diffusion model. The corresponding neural implementation within the C. elegans neural circuits will be discussed.

Workshop: Data Curation

Monday Jan 12, 2015

Instructors from the Inter-University Consortium for Political and Social Research (ICPSR) Data Archives partnered with the Emory Center for Digital Scholarship (ECDS) and the Institute for Quantitative Theory and Methods (QTM) to host a Data Curation Workshop in the Spring of 2015. The workshop aimed to give researchers hands-on experience curating quantitative datasets for long-term access and preservation in accordance with federal funding agency mandates and requirements from journals and publishers.

Statistical Inference on Networks of Spiking Neurons (Collective Computation in Biological Communication, Neural Dynamics, & Behavior Series)

Tuesday Dec 2, 2014

Sara A. Solla, Department of Physiology & Department of Physics and Astronomy, Northwestern University
Talk Abstract. Coupling large numbers of relatively simple elements often results in networks with complex computational abilities. Examples abound in biological systems - from genetic to neural networks, from metabolic networks to immune systems, from networks of proteins to networks of economic and social agents. Recent and continuing increases in the experimental ability to simultaneously track the dynamics of many constituent elements within these networks present a challenge to theorists: to provide conceptual frameworks and develop mathematical and numerical tools for the analysis of such vast data. The subject poses great challenges, as the systems of interest are noisy and the available information is incomplete.

For the specific case of neural activity, Generalized Linear Models provide a useful framework for a systematic description. The formulation of these models is based on the exponential family of probability distributions; the Bernoulli and Poisson distributions are relevant to the case of stochastic spiking. In this approach, the time-dependent activity of each individual neuron is modeled in terms of experimentally accessible correlates: preceding patterns of activity of this neuron and other monitored neurons in the network, inputs provided through various sensory modalities or by other brain areas, and outputs such as muscle activity or motor responses. Model parameters are fit to maximize the likelihood of the observed firing statistics; smoothness and sparseness constraints can be incorporated via regularization techniques. When applied to neural data, this modeling approach provides a powerful tool for mapping the spatiotemporal receptive fields of individual neurons, characterizing network connectivity through pairwise interactions, and monitoring synaptic plasticity.

Graduate Student Meet N' Greet: Multi-level Modeling

Monday Dec 1, 2014

A Gradate Student Meet N' Greet that emphasized hierarchical and multilevel modeling strategies to understand relationships in data. Speakers:  Chris Martin (Sociology), Andrew Pierce (Political Science), Ashley Moraguez (Political Science).
Event Program

Life’s Information Hierarchy (Collective Computation in Biological Communication, Neural Dynamics, & Behavior Series)

Wednesday Nov 19, 2014

Jessica Flack, Center for Complexity and Collective Computation in the Wisconsin Institute for Discovery, UW Madison, and The Santa Fe Institute
Talk Abstract. We have proposed that biological systems are information hierarchies organized into multiple functional space and time scales. This multi-scale structure results from the collective effects of components estimating regularities in their environments by coarse-graining or compressing time series data and using these perceived regularities to tune strategies.  As coarse-grained (slow) variables become for components better predictors than microscopic behavior (which fluctuates), and component estimates of these variables converge, new levels of organization consolidate, giving the appearance of downward causation. This intrinsic subjectivity suggests that the fundamental macroscopic properties in biology will be informational in character. If this view is correct, a natural approach is to treat the micro to macro mapping as a collective computation performed by system components in their search for configurations that reduce environmental uncertainty.  I discuss how we can move towards a thermodynamics of biology by studying this process inductively. This includes strategy extraction from data, construction of stochastic circuits that map micro to macro, dimension reduction techniques to simplify the circuits and move towards an algorithmic theory for the macroscopic output, and macroscopic tuning and control.

Advancing University Teaching and Learning Analytics: Linking Pedagogical Intent and Student Activity through Data-Based Reflection (Annual Theme Series)

Monday Nov 17, 2014

Alyssa Wise, Faculty of Education, Simon Fraser University
Talk Abstract. Learning analytics are data traces of student activity that can be used to better understand and support learning processes and outcomes. Over the last few years there have been remarkable advances in our ability to calculate and display useful information about what students are doing. Now, we face the challenge of how to mobilize this intelligence to have a meaningful impact on university teaching and learning. We need to consider and design for the ways in which learning analytics can become a part of (and change) the activity patterns of instructors and students. Working within the scope of the university course, I describe ways to integrate learning analytics into teaching and learning processes by using data-informed reflection to probe the connections (and disconnects) between instructors’ and designers’ pedagogical intents and students’ actual activity patterns. Particular attention will be paid to roles for students in the process, and the use of different reference frames for data interpretation. To ground the discussion, work from the E-Listening Project at Simon Fraser University will be presented as an initial example of a learning analytics application developed and implemented in a university course using such an integrated approach.

Workshop: Spatial Analysis in Architecture and Archaeology

Friday Nov 14, 2014

The workshop introduced the study of space in the built environment from a social viewpoint. The continuous built space is represented in discrete components according to basic aspects of human behavior and activities including movement, co-awareness and co-presence. Case studies of various scales, including houses, complex buildings, settlements, and archaeological records, were examined according to three main representational techniques of convex partitions, axial maps and visibility fields. Relational patterns of connections and separations among spatial components were analyzed as networks according to graph-theoretic methods to reveal the underlying social function (interfaces and program). The workshop covered the drawing of maps and isovists; justified graphs; UCL Depthmap software; data export and import; space syntax configuration, topological measures; and interpretation of graphical and numerical results. Led by Dr. Ermal Shpuza

Quantifying the Social Logic of Buildings and Cities (Visiting Fellows Speaker Series)

Wednesday Nov 12, 2014
Ermal Shpuza, Department of Architecture,  Southern Polytechnic State University
Talk Abstract. The built environment is the largest and most complex physical human artifact. Relational patterns of connections and separations in the built space underlie important aspects of human behavior and illuminate the spatial logic of society. Buildings and cities are described according to topological properties in contrast to geometrical representations that have traditionally informed architectural and urban theories. This research is aimed at unfolding important aspects of the social logic of built space by means of quantitative analysis of buildings and cities based on graph-theoretic methods. Spatial complexes are studied as networks of connections among elementary units of rooms, circulation spaces and streets, which support structured conditions of encounter, co-presence, co-awareness, and movement. Several themes of inquiry are discussed supported by the morphological analysis of various scales of built space including: the evolution of Adriatic and Ionian coastal cities, complex dynamics of arterial roads during city growth, shape description based on human perception of space, interaction between boundary shape and circulation structure in buildings and cities, and Balkan vernacular houses. The research spans cross-disciplinary links from architecture and urbanism to complexity science, graph theory, morphology, morphometry, physiography, historical cartography, urban history, organizational management, and environmental studies.

Teaching and Learning in an Evolving Educational Environment (Annual Theme Series)

Wednesday Oct 15, 2014

Charles Dziuban, The Center for Distributed Learning, University of Central Florida
Talk Abstract. Charles Dziuban, educational researcher and internationally recognized expert on blended learning environments, will present outcomes from twenty years of research on the concept of learning analytics through an effective teaching and learning perspective. He compares student success rates in varying course modalities in addition to preference for instructional formats. He showed the characteristics of excellent instructors from the student point of view using concepts such as the Anna Karenina Phenomenon. Finally, he presented examples of how individual faculty members at the University of Central Florida are undertaking an analytic approach to improving their courses with the scholarship of teaching and learning.

Quantitative Models for Ancient Historians (Quantitative Humanities Series)

Tuesday Oct 7, 2014

Walter Scheidel, Department of Classics, Stanford University 
Talk Abstract. Realistic simulation of historical processes is a final frontier for the study of the past. The ultimate purpose of simulation is to test causal hypotheses regarding the nature of the determinants of observed outcomes. This approach rests on the ability to assess the impact of different variables in an interactive model, an ability that requires concurrent consideration of factors such as geography, ecology (climate, land cover), natural endowments (such as mineral resources), the distribution of population, and the real cost of connectivity in terms of time and price, which is itself a function of geographical, infrastructural and technological conditions as well as institutional constraints. Recent years have witnessed considerable progress in discrete areas of simulations. The most notable examples include increasingly sophisticated raster-based simulation of state formation (PNAS 110, 2013, 16384-9) and geospatial modeling of the patterning of connectivity created by real transfer costs (Orbis 2.0, June 2014). We now also have access to spatial models for population and land use (e.g. HYDE), as well as to a growing number of geo-referenced datasets for various features such as settlements and certain types of deposits. What is still missing is proper integration of all these diverse elements, which is a vital precondition for meaningful multivariate simulation and hypothesis testing. Cooperation among different project teams was established in 2013/14 in order to pursue this goal, both specifically for Roman history and more globally. This paper, which draws on an international collective effort, seeks to illustrate the potential and challenge inherent in this ongoing endeavor by means of case studies (currently in progress) that focus on the properties of economic and political connectivity in the Roman world.

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