Libby Hemphill (09/16)

Title: Title Politicians and the Policy Agenda: Does U.S. Congress Twitter Use Direct New York Times Content?

Abstract: Traditionally we've understood that mainstream media hold a near monopoly on information about the policy agenda and that politicians influence the policy agenda by propagating policy problems through the media. What happens when politicians take their appeals to social media? In this talk, I'll discuss findings from a study in which we compared a year of content in the U.S Congress Twitter stream with the New York Times to understand whether and how Congress influence mainstream media through social media use. We found that for many policy areas, social media is an effective tool for gaining mainstream media attention. These findings confirm that Twitter is a legitimate political communication tool, at least for Congress, and provide updates to indexing theory to account for the diversified media landscape.

Biography: Libby Hemphill, PhD is an associate professor of communication and information studies at the Illinois Institute of Technology. She earned her M.S. in Human-Computer Interaction and Ph.D. in Information from the University of Michigan. Her recent work has focused on how users leverage Twitter to influence public and social policy. She is especially interested how people marshal information and communication technologies in service of social change and in the ethics and pragmatics of big social data.

Robin Burk (09/23)

Title: Recommendation for Multiple Stakeholders

Abstract: Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many real world applications, users are not the only stakeholders involved. There may be a variety of individuals or organizations that benefit in different ways from the delivery of recommendations. Broadening our perspective to include these stakeholders raises a number of new research questions in personalization. This talk is intended as a preliminary foray into this problem, discussing the implications of re-defining the recommender system in a multi-stakeholder environment, including representation of stakeholder preferences, evaluation of outcomes, and algorithm design.

Biography: Robin Burke is a Professor at CDM. His research interests are in artificial intelligence as applied to social computing. His current work concentrates on the area of recommender systems, including representing the interests of multiple stakeholders in recommendation, performing recommendation using data from complex heterogeneous networks, and tracking the evolution of users' tastes over time.

Professor Burke earned his PhD in 1993 from Northwestern University, working with Professor Roger Schank, one of the founders of the field of cognitive science. He worked in post-doctoral positions at the University of Chicago, and then in 1998, left academic employment to help found a 'dot-com' startup. He returned to academic work in 2000 first at the University of California, Irvine and then at California State University, Fullerton. In the Fall of 2002, he began his current position at DePaul University.

Lian Wang (09/30)

Title: The Marketing Power of First Party Data In the Big Data Era

Abstract: Although people are talking about digital data more than anything else in the Big Data Era, the first party data is the key of success for marketing strategies. The first party data is the major source of marketing power for the brands. The 2nd and 3rd party data enhances the marketing power of the 1st party data. Only if the data is integrated around the first part data, the marketing power of the data would be greatly released. Platform marketing is a very critical infrastructure and applications in the Big Data Era. DMP that is used to manage non-PII data and CRM that is used to manage PII-based customer data are two major platforms. However, DMP must be integrated with CRM to optimize the marketing power of a data platform. Several of BlueFocus practices will be presented to demonstrate the marketing power of the first party data, and a brief introduction to BlueFocus and its Big Data function will be introduced.

Biography: Dr. Lian Wang is the head of Big Data department in charge of data-based platform, products, analysis, and data-driven marketing consulting. He is the member of TLT (Top Leader Team), the executive committee of the BlueFocus group. Prior to BlueFocus, Dr. Wang has more than ten years of data mining experience in the US consumer finance industry, including statistical analysis, modeling, risk management, and database marketing. He has served Capital One, HSBC North America and Bank of America for their risk and marketing analytics. Dr. Wang received his Ph. D. in Economics from the State University of New York at Binghamton.

Craig Miller (10/07)

Title: Figurative Speech and the Errors that Novice Programmers Make

Abstract: A reference-point error occurs when a programmer writes code that mistakenly refers to one element when the intention is to refer to an element structurally related to it. I review these errors and their relation to the use of metonymy in human communication. As an example of metonymy, consider this human-to-human instruction: "Open the ice cream and serve two scoops." Human listeners effortlessly infer that it's the container, not the ice cream, that should be opened. Drawing upon the use of metonymy and cognitive theories of human communication and problem-solving, I explore three accounts of why reference-point errors occur in novice programming. The first account involves a deficient mental model, the second assumes a misconception of the notional machine, and the third considers implicit, proceduralized habits of communication. I conclude with learning objectives for students that address these sources of difficulty.

Biography: Craig Miller received his Ph.D. (1993) in Computer Science and Engineering at the University of Michigan. He has a B.S. and B.A. (1987) from Bowling Green State University in computer science and French. Prior to DePaul, he held positions as a post-doctoral research fellow at Carnegie Mellon University and as an assistant professor at Dickinson College. His research and teaching interests include cognitive science, Human-Computer Interaction and intelligent systems.


Cyril Nigg (10/14)

Title: What They Don’t Tell You About Being a Data Scientist - 10 Day-to-Day Challenges

Abstract: There is no shortage of articles, courses and white papers describing the process of building predictive models and developing innovative algorithms. However, many of the day-to-day challenges facing Data Scientists working in the business world are not widely discussed. This talk will shed some light on these challenges, which include working with dirty data, acquiring domain knowledge and aligning on business objectives, and provide insight on how professional Data Scientists overcome these obstacles.

Biography: Cyril Nigg is a Senior Director of Data Science at Catalina Marketing, where he leads personalization and consumer targeting analytical capabilities, working with top drug and grocery retailers. Cyril was previously part of Local Offer Network, a Chicago start-up specializing in machine learning and personalization technology, which was acquired by Catalina in 2013. He began his career in market research, working at Synovate and Ipsos.


Jame Wagner (10/21)

Title: Database Forensic Analysis with DBCarver

Abstract: The increasing use of databases in the storage of critical and sensitive information in many organizations has lead to an increase in the rate at which databases are exploited in computer crimes. While there are several techniques and tools available for database forensics, they mostly assume apriori database preparation, such as relying on tamper-detection software to be in place or use of detailed logging. Investigators, alternatively, need forensic tools and techniques that work on poorly-con figured databases and make no assumptions about the extent of damage in a database.

In this talk, we present DBCarver, a tool for reconstructing database content from a database image without using any log or system metadata. The tool uses page carving to reconstruct both query-able data and non-queryable data (deleted data). We describe how the two kinds of data can be combined to enable a variety of forensic analysis questions hitherto unavailable to forensic investigators. We show the generality and efficiency of our tool across several databases through a set of robust experiments.

Biography: James is a third year Ph.D. student at DePaul University. He received his Master's in Computer Science from DePaul University and his Bachelor's in Chemistry from Marquette University. His research focuses on database forensics, security, and optimization.

Amanda Lazar (10/28)

Title: The Role of Technology in Understanding Perspectives on Aging and Health

Abstract: As the population ages, research has increasingly focused on conditions associated with growing older, such as dementia. Technology is often presented as a solution for managing or treating these various health conditions. This framing can position health conditions as problems to address through design and can neglect the complexity and positive aspects of older adulthood. In this talk, I draw on critical perspectives from Human Computer Interaction and Gerontology. I describe three ways in which technology can help us understand and challenge stereotypes around aging and dementia. I will argue for a view of aging that takes into account the ways that technologies position older individuals and, in turn, the way that this view can inform the design of new technologies to enrich the experience of growing older..

Biography: Amanda Lazar is a postdoctoral fellow affiliated with the Technology and Social Behavior program at Northwestern University. Her research focuses on the ways that technologies designed for health and wellbeing position and support individuals as they age. She completed her PhD in Biomedical and Health Informatics at the University of Washington in 2015 and her undergraduate degree in Electrical Engineering at the University of California, San Diego. Her research has received Best Paper and Honorable Mention awards at the ACM DIS and UbiComp conferences and was supported by the NSF Graduate Research Fellowship and the NLM Training Grant. She has published her work in CHI, CSCW, DIS, AMIA, and Health Education & Behavior.

Sorin Matei (11/4)

Title: The 1% Effect: Social Differentiation in Social Media Groups

Abstract: If Wikipedia were a country and its content was its income, the wildly uneven distribution of wealth among its contributor-citizens would be unheard of. The wealth distribution of the U.S., for instance, which some see as quite unfair, pales by comparison, as the top 1% American earners account for “only” 15% of the national income compared to the 77% of content “owned” by the top 1% of Wikipedians.  Our book explains how this uneven distribution matters. We believe that far from being an abnormality, it is a sign that Wikipedia has developed organically and naturally. The unbalance in fact shows that the site is structurally differentiated and that functional leaders have emerged, which are essential for the good functioning of this project in particular and of other voluntary project in general.

Biography: Sorin Adam Matei - Professor of Communication, Brian Lamb School of Communication, Purdue University - studies the relationship between information technology, social media, and social behavior. He published papers and articles in Journal of Communication, Communication Research, Information Society, and Foreign Policy. He is the editor of Ethical Reasoning in Big Data,Transparency in social mediaRoles, Trust, and Reputation in Social Media Knowledge Markets: Theory and Methods (Computational Social Sciences) . His work was funded by the National Science Foundation, Mellon Foundation, Kettering Foundation, Motorola and other organizations. Dr. Matei is also known for his media work. He is a former BBC World Service, has published in Esquire and Foreign Policy. His publishes frequent research updates on his blog,  In Romania he is known for his books Boierii Mintii (The Mind Boyars)Idolii forului (Idols of the forum), and Idei de schimb (Spare ideas).

Shan Lu (04/01)

Title: Fighting Software Inefficiency Through Automated Bug Detection

Abstract: Developers frequently use inefficient code sequences that could be fixed by simple patches. These inefficient code sequences can cause significant performance degradation and resource waste, referred to as performance bugs. Meager increases in single threaded performance in the multi-core era and increasing emphasis on energy efficiency call for more effort in tackling performance bugs.
This talk will present our group’s work in understanding, detecting, and fixing performance bugs. I will first present an empirical study we did for more than 100 real-world performance bugs. I will then describe several dynamic and static program analysis tools that we have designed to automatically detect and fix performance bugs. Our tools have detect hundreds of performance bugs in the latest versions of widely used open-source applications. I will conclude the talk by discussing challenges and opportunities for future rsearc fighting performance bugs.

Biography: Shan Lu is an Associate Professor in the Department of Computer Science at the University of Chicago. She received her Ph.D. at University of Illinois, Urbana-Champaign, in 2008. She was the Clare Boothe Luce Assistant Professor of Computer Sciences at University of Wisconsin, Madison, from 2009 to 2014. Her research focuses on software reliability, particularly detecting, diagnosing, and fixing concurrency bugs and performance bugs in large software systems.

Shan has won Alfred P. Sloan Research Fellow in 2014, Distinguished Alumni Educator Award from Department of Computer Science at University of Illinois in 2013, and NSF Career Award in 2010. Her co-authored papers won two ACM-SIGSOFT Distinguished Paper Awards at ICSE 2015 and FSE 2014, one Best Paper Award at USENIX FAST in 2013, an ACM-SIGPLAN Research Highlight Award in 2011, and an IEEE Micro Top Picks in 2006. Shan currently serves as the Vice Chair of ACM-SIGOPS.

Anton Oleinik (04/08)

Title: Approaches towards mixing quantitative and qualitative content analysis

Abstract: An original path model for triangulating the results of three types of content analysis will be presented: (i) the analysis of the co-occurrence of words; (ii) the substitution model of quantitative content analysis and (iii) qualitative coding. The model refers to the “within method” as well as the “between methods” of triangulation. This model helps assess the reliability and validity of content analysis. The model has been tested in several studies ranging from a study of public servants to a study of communication is academia.

Biography:  Dr. Anton Oleinik is professor of sociology at Memorial University of Newfoundland (St. John’s, Canada). He received a PhD in sociology from Ecole des Hautes Etudes en Sciences Sociales (Paris, France) and Habilitation in economic theory from the Central Economics and Mathematics Institute of the Russian Academy of Sciences (Moscow, Russia). Some of his work includes The Invisible Hand of Power: An Economic Theory of Gatekeeping (Routledge, 2015), Knowledge and Networking: On communication in the social sciences (Transaction, 2014), Market as a Weapon: The Socio-Economic Machinery of Dominance in Russia (Transaction, 2011), Organized Crime, Prison and Post-Soviet Societies (Ashgate, 2003). His articles on methodological issues appeared in Quality & Quantity.

Öykü Işık (04/07, at 1:30pm)

Title:  Big Data Capabilities: An Organizational Information Processing Perspective

Abstract: Big data is at the pinnacle of its hype cycle, offering big promise. Everyone wants a piece of the pie, yet not many know how to start and get the most out of their big data initiatives. We suggest that realizing benefits with big data depends on having the right capabilities for the right problems. When there is a discrepancy between these, organizations struggle to make sense of their data. Based on information processing theory, in this research-in-progress we suggest that there needs to be a fit between big data processing requirements and big data processing capabilities, so that organizations can realize value from their big data initiative.

Biography: Öykü Işık is an Assistant Professor in Information Systems Management at Vlerick Business School in Belgium. She holds a PhD degree in Business Computer Information Systems (2010) from the University of North Texas. Her research interests include business intelligence and analytics, business transformation with IT, and privacy in the age of big data. Her recent work appeared in Information & Management and Journal of Information Technology Teaching Cases.

Samuel G. Armato III (04/15)

Title: Public Databases and Grand Challenges in Thoracic Imaging

Abstract:   Essential to the conduct of research in the field of computer-aided diagnosis (CAD) is a collection of clinical images that capture the disease under investigation.  Such an image collection, known as a “database,” preferably requires metadata, supplied by a domain expert radiologist, on the nature, extent, and/or location of the abnormality.  Herein lies a major impediment to investigators wishing to contribute to CAD research: the collection of image databases is a laborious and expensive task.  Issues of patient confidentiality mandate strict anonymization schemes.  The time required for radiologists to review, assess, and annotate images as required by the research may be quite onerous.  Furthermore, some institutions not affiliated with a medical center may not even have access to clinical data of any kind.  A complication with image databases, once collected, is their limited utility.  Research groups that expend the time and money to create a database will likely use it for a single project and typically are reluctant to share their database with other groups.  A further consequence of this isolation of databases is the inability to compare directly the performance of CAD methods reported in the literature, and since database composition is a known source of variability in CAD performance, such disparate databases could be expected to slow down commercialization of successful CAD technologies.  Two solutions that are emerging in an attempt to overcome these difficulties, especially for lung-image-based CAD research, are the creation of public databases and the conduct of “grand challenges” within the medical imaging research community.

Biography:  Samuel G. Armato III, Ph.D. is an Associate Professor in the Department of Radiology and the Committee on Medical Physics at The University of Chicago.  Dr. Armato received his undergraduate degree in physics from The University of Chicago in 1987.  He returned to The University of Chicago in 1991 to pursue graduate studies in medical physics and earned a Ph.D. in 1997.  Since that time, Dr. Armato has gained recognition for his work in computer-aided diagnosis (CAD), which combines physics, mathematics, computer science, and statistics to analyze medical images for the early detection, diagnosis, and quantitative evaluation of disease.  Dr. Armato has been developing and investigating CAD methods in chest radiology for the automated detection of lung nodules in thoracic computed tomography (CT) scans, the automated volumetric assessment of pleural mesothelioma in CT scans, the quantitative assessment of mesothelioma response to therapy, the analysis of temporal subtraction image quality in chest radiography, the quantitative assessment of normal tissue complications in lung cancer patients undergoing radiation therapy, and the response of chronic sinusitis patients to therapy.  He also is interested in the issue of interobserver variability in diagnostic image interpretation, especially in the context of establishing “truth” for CAD studies.

Hamed Qahri Saremi (04/15, at 2:10pm)

Title: Ambivalence and Its Responses in Information System Use: An Integrative Framework and Empirical Examination

Abstract: Information Systems (IS) research has largely conceptualized users’ orientation toward using an IS as a unidimensional, linear concept, where users are implicitly assumed to hold a neutral, positive, or negative orientation toward using an IS. However, recent studies show that individuals typically hold simultaneous negative and positive orientations toward behaviors, including possibly IS use, and this results in a sense of ambivalence. Hence, users’ mindsets are possibly more complex than previously assumed, and a bi-dimensional view regarding their orientations toward using an IS, as well as their responses to different combinations of opposing orientations is warranted. In order to address this complexity, this study first elaborates on the concepts of ambivalence and responses to ambivalence in the context of IS use. It then relies on ambivalence theories to suggest an integrative framework of IS users’ five typical states of ambivalence and possible responses to them. The framework is validated with Latent Profile Analysis applied to data collected from 442 Facebook users. The results show that all users hold some degree of simultaneous positive and negative orientations toward the use of an IS, and that each user consequently experiences one of the five hypothesized states of ambivalence. Furthermore, analyses of variance tests demonstrate that each state of ambivalence is more strongly associated with certain plausible responses than other states. Ultimately, this study extends our understanding of users’ mindsets, makes important theoretical and practical contributions to the IS use literature, and paves the way for important future research in this area.

Biography: Hamed Qahri Saremi is an assistant professor of management information systems at University of Illinois at Springfield. He received his PhD in business administrations with concentration in information systems from DeGroote School of Business at McMaster University in November 2014. Dr. Saremi's research has been mainly focused on two areas: patterns of use of information systems and electronic word of mouth. His papers have appeared in high quality research outlets in information systems such as Journal of Strategic Information Systems, Information & Management, and Expert Systems with Applications. Dr. Saremi has served as guest editor, reviewer, and session chair for different research journals and conferences within information systems and management fields. As a certified SAP Associate, Dr. Saremi has taught several different online and face to face courses at the undergraduate and graduate (MBA and Master of MIS) levels in information systems curriculum, including data mining and business process management areas.

Dorothy Kozlowski

Title: Understanding Traumatic Brain Injury

Abstract: This talk will provide an overview of traumatic brain injury including concussion and repeat concussions. In addition, research in my lab that focuses on understanding neuroplasticity and neurorehabilitation following brain injury will be presented in addition to pilot studies demonstrating our new model of repeat concussion.

Biography: Dorothy Kozlowski received her PhD in Psychology from the University of Texas-Austin. She completed postdoctoral fellowships in the Department of Neurosurgery at UCLA and in the Neurobiology program at Children’s Memorial Hospital/ Northwestern University prior to joining the faculty at DePaul in 2000. She is currently a Vincent de Paul Professor in the Department of Biological Sciences and the Director of the new Neuroscience major. She is the recipient of a DePaul Excellence in Teaching Award as well as an “Educator of the Year” award from the national Faculty for Undergraduate Neuroscience. Her research focuses on understanding how the brain changes after a traumatic brain injury and how neural plasticity and rehabilitation can be used as treatment strategies. In addition to research on brain injury, she and her students work to educate youth on concussions and playing sports safely.

Noah Rosenblatt

Title: Predicting community behaviors from biomechanical measures: applications to prosthetics and falls

Abstract: In the upcoming decade, the growth rate of the older adult population (65+ years of age) is expected to supersede that of all other age groups. Within this population, diabetes-related amputations and falls present two major challenges that can significantly compromise function and mobility. Advanced prosthetic components can improve functionality of prosthetic devices. However, lack of comfort and poor fit of the prosthetic socket often prevent people with amputations from using their prosthesis. In this presentation I will first discuss the rationale and anticipated methodology behind a recently begun endeavor to develop a new prosthetic socket to optimize comfort based on physiological signals. I will then briefly address the data analysis methods to be employed in initial testing and the data-driven questions that must be addressed before results can be applied to community-based studies. Finally, I will discuss the development of a novel fall prevention intervention addressing biomechanics of the fall recovery response, and the need to develop predictive models to target individuals most likely to benefit from this intervention.

Dr. Rosenblatt received my Bachelors in Biomedical Engineering, with a concentration in biomechanics, from Northwestern University. During this time, he had the opportunity to work as a Research Engineer for Scheck and Siress Prosthetics, where he initially gained first-hand insight into clinical aspects of prosthetic gait and function. Following this work,he received his PhD in Biomedical Engineering at Boston University where his dissertation was focused on cellular mechanics. Thereafter, he completed four years of post-doctoral training at the Clinical Biomechanics and Rehabilitation Laboratory in the Department of Kinesiology at the University of Illinois at Chicago (UIC), during which time his work focused on quantifying locomotor stability and fall prevention in older adults. He continued this work at UIC for several additional years as an Assistant Research Professor, teaching introductory and advanced classes in biomechanics of the neuromusculoskeletal system. In May of 2015 he began a position as an Assistant Professor in the Dr. William M. Scholl College of Podiatric Medicine’s Center for Lower Extremity Ambulatory Research (CLEAR) at Rosalind Franklin University of Medicine and Science.

Duru Turkoglu (05/06)

Title: Degree Four Plane Spanners: Simpler and Better

Abstract: Let P be a set of n points embedded in the plane, and let C be the complete Euclidean graph whose point-set is P. Each edge in C between two points p, q is realized as the line segment [pq], and is assigned a weight equal to the Euclidean distance |pq|. In this paper, we show how to construct in O(n lg n) time a plane spanner of C of maximum degree at most 4 and of stretch factor at most 20. This improves a long sequence of results on the construction of bounded degree plane spanners of C. Our result matches the smallest known upper bound of 4 by Bonichon et al. on the maximum degree while significantly improving their stretch factor upper bound from 156.82 to 20. The construction of our spanner is based on Delaunay triangulations defined with respect to the equilateral-triangle distance, and uses a different approach than that used by Bonichon et al. Our approach leads to a simple and intuitive construction of a well-structured spanner, and reveals useful structural properties of the Delaunay triangulations defined with respect to the equilateral-triangle distance.

Biography:  Duru Turkoglu is an Instructor at the CDM School of Computing. He received his M.S. and Ph.D. degrees in Computer Science from the University of Chicago in 2012. Previously, he received B.S. degrees in Computer Engineering and Mathematics from the Middle East Technical University in Ankara, Turkey. His main research area is design and theory of algorithms mainly in computational geometry, and more specifically in graph algorithms, spanners, mesh refinement, and dynamization and kinetization techniques. He has published in top computational geometry and algorithms conferences including Symposium on Computational Geometry, Canadian Conference on Computational Geometry, and European Symposium on Algorithms.

Massimo Di Pierro (05/13)

Title: A quick tour of numerical algorithms in python

Abstract:  This talk is quick and fast excursion over the Python language and some of its libraries. Specifically we will discuss sympy, numpy, nlib, nlib, and pyopencl. A particular focus will be given to linear regression, solvers and optimizers, and Python to C compilation.

Biography:  Massimo Di Pierro is an Associate Professor of Computer Science at the School of Computing of DePaul University. Massimo holds a Ph.D. in High Energy Theoretical Physics from the University of Southampton in UK. Before that he worked as Associate Researcher at the Fermi National Accelerator Laboratory. His current main research areas are Computational Finance and Visualization for Lattice Quantum Chromodynamics ( His work in the latter field is supported the Department of Energy under a Scientific Discovery though Advanced Computing (SciDAC) Grant. The software he writes as part of his research runs on the biggest supercomputers in the US.

Massimo Di Pierro is author of more than 60 publications. A partial list can be found at the Stanford SPIRES Database ( He has given multiple talks and presentations at Physics and Finance conference worldwide. Since 2003 Massimo has been very active in the Python community and a member of the Chicago Python Users Group (chipy). He also uses Python in many programming classes and created Pyhton libraries in python ( He is an editor of "Computing in Science and Engineering", a publication of the IEEE Computer Society.

Massimo is the lead developer of web2py (, an Open Source Web Framework based on Python. It counts more then 100 contributors, 3200 registered users (from google group), and ~180 downloads/day (in average from distinct IPs). In 2008 has published a book on web2py and a fourth edition will be released on Nov 2011. Web2py was voted by Inforword best full stack Python framework in 2011 and was the Bossie award in 2011 for Best Source Development Platform.

William N. Frost (05/20)

Title: Imaging brain networks during behavior and learning

Abstract:  Dr. Frost uses fast voltage sensitive dyes to record the firing of up to 200 individual neurons during behavioral motor programs and learning, using invertebrate model systems in which the functions of individual neurons can be determined. Topics to be covered include the degree to which network neurons participate reliably in behavior both moment-to-moment and trial-to-trial, how networks change as memories form, and the development of useful computational tools to assist such studies.

Biography:  Dr. William N. Frost received his B.A. in Biology at Reed College an M.A. and Ph.D. in Physiology at Columbia University (Eric Kandel Lab). He had academic appointments at the University of Texas Medical School at Houston and is currently Professor and Chair at the Chicago Medical School, Rosalind Franklin University of Medicine and Science.

His largest scientific contributions have addressed the following four areas: A) Self-modulation by neural networks (Intrinsic neuromodulation) (5 papers), B) How neural networks generate behavior (12 papers), C) Tool development: Use of large-scale imaging to study neural networks (10 papers) and D) Experience-dependent modification of behavior and learning (15 papers)

Peter M. Hastings (05/27)

Title: Identifying causal structure in student essays

Abstract: Educational standards put a renewed focus on strengthening students' abilities to construct scientific explanations and engage in scientific arguments. Because evaluating student writing is so time-intensive, people often turn to automatic essay evaluation programs, but these generally rely on rough measures of word choice and coverage of a topic. In contrast, our focus is on determining the causal structure in students' explanations of scientific phenomena, so that we can (eventually) provide effective feedback to help them improve their explanations. Starting with hand-annotated essays from hundreds of students in the Chicago area, we developed machine learning techniques to identify both the conceptual elements in student essays and the causal connections between them.

Biography:  Dr. Hastings came to DePaul in 2001 from the University of Edinburgh where he was a Lecturer. Prior to his move to Scotland, he worked as a post-doctoral research assistant at the University of Memphis. Before that, he was awarded a postdoctoral fellowship at the University of Michigan in Ann Arbor, where he earned his Ph.D. in computer science. He completed his masters degree at the Johns Hopkins University and his bachelors degree at Michigan State University, both in computer science. Dr. Hastings's research interests include natural language processing, cognitive science, intelligent tutoring systems, educational games, and artificial intelligence.

Denise C. Nacu (06/03)

Title: Beyond the Like Button: Encouraging Productive Contributions in an Online Social Learning Network

Abstract: Research has revealed that youth participation in online communities can help build 21st century skills such as contributing ideas, creating and refining work, communicating and collaborating with others, and taking initiative to direct one's own learning. However, recent studies have also shown that contributors of online content are a small subset of the population using technical systems, and that this subset is not representative of the larger population. This trend highlights an inequity both in terms of who takes advantage of opportunities to develop technological competencies necessary for participation in the 21st century, and in terms of who is authoring content that informs public opinion and knowledge. In this presentation, I consider how Latino youth interact around online digital artifacts and how we can design features to better support their contributions of communication and critique. I focus on a collaboration with a seventh grade teacher using an online platform in a predominantly Latino middle school, and present the design and results of two design iterations of a software feature that intended to encourage online communication and contribution, using qualitative ethnographic case studies, co-design activities, and quantitative log data. Findings suggest important cultural and pedagogical design considerations for online social learning network interfaces aiming to build learning community and engage diverse youth populations to contribute.

Biography: Denise C. Nacu, Ph.D. is a designer and researcher working at the intersection of technology, learning, and design in the College of Computing and Digital Media School of Design at DePaul University. She co-directs the Technology for Social Good Research and Design Lab and is a researcher with the Digital Youth Network. Before joining DePaul, she was Director of Design at the University of Chicago Urban Education Institute where she designed educational technology tools. She received her Ph.D. in Education (Learning Technologies) from the University of Michigan in 2004, and a B.A. in Art History, with a minor in Psychology from Columbia University in 1995.