Sangwon Bae, Joel Chan, Irene-Angelica Chounta
Postdoctoral Researchers, Human-Computer Interaction Institute, Carnegie Mellon University
Friday, September 2, 2016 - 1:30pm to 2:30pm
- Newell-Simon Hall 1305 (Michael Mauldin Auditorium)
Speaker: Joel Chan
Title: Accelerating innovation with computational analogy: Challenges and new solutions
Abstract: Ideas from research papers in a different domain can trigger creative breakthroughs. But most papers outside of one’s domain are not useful: the ones that trigger breakthroughs are analogically related to the target domain (e.g., share problems/solutions). To help people find useful papers outside of their domain, we need to build computational systems that can reason by analogy. In this talk, I will argue that the central challenges are twofold: 1) analogical reasoning requires structured representations, and 2) automatically transforming the unstructured text of papers into analogy-ready structured representations is hard. I will then describe our ongoing efforts to create a system that extracts structured representations from scientific papers, leveraging the complementary strengths of machine learning and crowdsourcing.
Speaker: Sangwon Bae
Talk Title: Using Passively Collected Sedentary Behavior to Predict Hospital Readmission
Abstract: Hospital readmissions are a major problem facing health care systems today, costing Medicare alone US$26 billion each year. Being readmitted is associated with significantly shorter survival, and is often preventable. Predictors of readmission are still not well understood, particularly those under the patient’s control: behavioral risk factors. Our work evaluates the ability of behavioral risk factors, specifically Fitbit-assessed behavior, to predict readmission for 25 postsurgical cancer inpatients. Our results show that sum of steps, maximum sedentary bouts, frequency and low breaks in sedentary times during waking hours are strong predictors of readmission. We built two models for predicting readmissions: Steps-only and Behavioral model that adds information about sedentary behaviors. The Behavioral model (88.3%) outperforms the Steps-only model (67.1%), illustrating the value of passively collected information about sedentary behaviors. Indeed, passive monitoring of behavior data, i.e., mobility, after major surgery creates an opportunity for early risk assessment and timely interventions.
Speaker: Irene-Angelica Chounta
Talk Title: Linking Dialogue with Student Modeling to Create an Enhanced Micro-adaptive Tutoring System
Abstract: The learning process and its outcomes depend greatly on the social interaction between teachers and students and, in particular, on the proficient and focused use of language through written text or discussions. Our overarching goal in this project is to better understand how to make automated tutorial dialogues effective and adaptive to student characteristics, such as prior knowledge. The specific goal of our current project is to develop an adaptive, natural-language tutoring system, driven by a student model, which can effectively carry out reflective conversations with students after they solve physics problems. Towards this end, we continue our work in identifying linguistic features of tutoring that predict learning gains, and extend it by characterizing the “level of support” to provide to students based on their current level of understanding particular physics concepts and principles, as dynamically captured by the student model. In this talk, I will describe the features of dialogic discourse underlying “level of support” that we have identified through the analysis of human-to-human tutorial dialogues as well as the construction and application of a coding scheme for the characterization of the “level of support”. I will present initial teacher feedback on dialogues that apply these features to coach students at different levels. I will also discuss how this line of research affects the authoring of tutorial dialogues used by an intelligent tutoring system for students who exhibit different levels of understanding.
- Speaker's Bio
Sangwon Bae: Dr. Bae is a postdoctoral researcher in the Human–Computer Interaction Institute at Carnegie Mellon University. She earned her Ph.D. in Cognitive Science and Engineering from the Yonsei University and worked at SK and Samsung before returning to academia. Her research has focused on using smartphones and wearable trackers to understand human perception and to develop models of behavior based on mobile systems. The goal of her work is to examine the feasibility and acceptability of collecting continuously-sensed contextual information and active patient-reported symptom reports and to use these types of information to develop algorithms to accurately predict behavior for use in health monitoring and treatment delivery. In this talk, she will present a paper recently accepted for publication on “Using passively collecting sedentary behavior to predict hospital readmission” (UbiComp, 2016), which present for the first time that a machine-learning model using only passively-sensed behavioral data collected from a wearable off-the-shelf fitness tracker accurately predicts 30-day hospital readmissions for postsurgical cancer patients.
Joel Chan: Joel Chan is a Postdoctoral Research Fellow in the Human-Computer Interaction Institute at Carnegie Mellon University. He received his PhD in Cognitive Psychology from the University of Pittsburgh in 2014. Joel's research integrates cognitive science and human-computer interaction to understand and improve technological support for creative and collective intelligence. His work has been recognized with a Best Paper Award at the ASME Design Theory and Methodology conference, a Best Paper of the year at the Design Studies Journal, and supported by an NSF Doctoral Dissertation Improvement Grant.
Irene-Angelica Chounta: Since April 2016 Irene-Angelica Chounta has held a post-doctoral researcher position in the Human-Computer Interaction Institute, Carnegie Mellon University. She works with Patricia Albacete, Pamela Jordan, Sandra Katz (Learning Research and Development Center, University of Pittsburgh) and Bruce McLaren (HCII, CMU) on a joint project between CMU and the University of Pittsburgh that aims to develop a student model to support a physics tutorial dialogue system.