Michael Eagle, Swarup Kumar Sahoo, Ran Liu
Postdoctoral Researchers, Human-Computer Interaction Institute, Carnegie Mellon University
Friday, September 16, 2016 - 1:30pm to 2:30pm
- Newell-Simon Hall 1305 (Michael Mauldin Auditorium)
Speaker: Michael Eagle
Title: Predicting Individual Differences for Learner Modeling in Intelligent Tutors from Previous Learner Activities
Abstract: This study examines how accurately individual student differences in learning can be predicted from prior student learning activities. Bayesian Knowledge Tracing (BKT) predicts learner performance well and has often been employed to implement cognitive mastery. Standard BKT individualizes parameter estimates for knowledge components, but not for learners. Studies have shown that individualizing parameters for learners improves the quality of BKT fits and can lead to very different (and potentially better) practice recommendations. These studies typically derive best-fitting individualized learner parameters from learner performance in existing data logs, making the methods difficult to deploy in actual tutor use. In this work, we examine how well BKT parameters in a tutor lesson can be individualized based on learners’ prior performance in reading instructional text, taking a pretest, and completing an earlier tutor lesson. We find that best-fitting individual difference estimates do not directly transfer well from one tutor lesson to another, but that predictive models incorporating variables extracted from prior reading, pretest and tutor activities perform well, when compared to a standard BKT model and a model with best-fitting individualized parameter estimates.
Speaker: Swarup Kumar Sahoo
Title: Managing Privacy of Mobile Applications
Abstract: Mobile applications are very privacy-invasive today. Apps request lot of private and sensitive data without properly informing the users about how it will be used. We are building various tools and techniques as part of privacy-enhanced android to give users full control over their private data. One main focus of our work is about making purposes of private data use an essential part of apps and use them to detect/prevent potential privacy issues. Our current approach is to have the apps explicitly declare purpose of why sensitive data is being used and then use static/dynamic program analysis and machine learning techniques to check/enforce purpose of private data. We are also building new kind of user interfaces leveraging purposes and using crowdsourcing techniques to help users make informed decisions about configuring their privacy preferences for various apps.
Speaker: Ran Liu
Title: Bridging the Gap Between Educational Data Mining and Improved Classroom Instruction
Abstract: The increasing use of educational technologies in classrooms is producing vast amounts of process data that capture rich information about learning as it unfolds. The field of Educational Data Mining (EDM) has made great progress in using the information present in log data to build models that improve instruction and advance the science of learning. However, there have been some limitations. The data used to produce such models has been frequently limited to the actions that education technologies themselves can log. A major challenge in incorporating more contextually-rich data streams into models of learning is collecting and integrating data from different sources and at different grain sizes. In my first talk, I will present methodological advances we have made in automating the integration of log data with additional multi-modal (e.g., audio, screen video, webcam video) data streams. I will also show a case study of how including the multi-modal streams in data analysis can improve the predictive fit of student models and yield important pedagogical implications. More broadly, this work represents an advancement in integrating rich qualitative details of students’ learning contexts into the quantitative approaches of EDM research. Another limitation of EDM research thus far is that findings remain largely theoretical with respect to their impact on learning outcomes and efficiency. The most important, rigorous, and firmly grounded evaluation of a data-driven discovery is whether it leads to modifications to education that produce better student learning. Such an evaluation has been referred to as "closing the loop" (e.g., Koedinger et al., 2013), as it completes cycle of system design, deployment, data analysis, and discovery leading back to design. In my second talk, I present new results that “close the loop” (via a classroom-implemented randomized controlled trial) on a data-driven, machine-automated method of improving knowledge component models.
- Speaker's Bio
Michael Eagle: Michael Eagle is a postdoctoral fellow at Carnegie Mellon University’s Human-Computer Interaction Institute working with Dr. John Stamper. Michael’s research focuses on deriving understanding from complex interaction data from intelligent tutors and video games. He has worked in data science at Blizzard Entertainment and Warner Bros. Interactive Entertainment (Turbine Inc.) Michael received a NSF GRFP Honorable Mention award, a GAANN fellow, and Freeman-ASIA recipient. Michael was also the PI on a NSF EAPSI grant, in which he traveled to Japan and collaborated with Japanese researchers also working in the educational data mining field. Michael graduated from North Carolina State University in December 2015 under the direction of Dr. Tiffany Barnes.
Swarup Kumar Sahoo: Swarup Kumar Sahoo is a Post-Doctoral Research Associate in ISR/HCII. He is currently working in the Brandeis mobile privacy project with Prof. Jason Hong and Prof. Yuvraj Agarwal. He completed his PhD in computer science at the University of Illinois at Urbana-Champaign under Prof. Vikram Adve. His main research interests lie in using static and dynamic program analysis techniques for improving software reliability, security and privacy. The focus of his current research focus is on developing techniques to improve privacy of mobile applications.
Ran Liu: Ran Liu is a post-doctoral fellow working with Ken Koedinger and John Stamper in the Human-Computer Interaction Institute at CMU. Her research uses data from education technology to improve instruction and learning outcomes. She takes a human-centered approach to this research and focuses on developing interpretable and actionable models.