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HCII Seminar Series - featuring HCII Postdocs

Adetunji Adeniran, Danielle Chine and Tricia Ngoon
Postdoctoral Researchers


This seminar will be presented virtually.

Video link


The HCII regularly gives our postdocs an opportunity to present during the HCII Speaker Series.  This week we will hear from three postdocs from the learning sciences: Adetunji Adeniran, Danielle Chine and Tricia Ngoon.  Each person will present for approximately 15 minutes with limited time allowed for questions. 


Adetunji Adeniran

Postdoctoral Fellow, faculty advisor Ken Koedinger

TitlePL Supported Human Tutoring - To Bridge Learning Research with Tutoring Practice

Abstract:   Mentoring is one strategy that can be used to narrow the achievement gap across socioeconomic and ethnic groups, especially for disadvantaged students who are more likely to have low socio-motivation and a negative attitude towards learning. In this talk, Tunji will discuss the Personalized Learning2 application, which was inspired with the goal of assisting tutors in becoming better mentors and increasing student access to math mentoring. He will discuss the present utility of PL2 and our current efforts to improve “PL2-Enhanced Learning” support for human tutors.

Bio:    Tunji earned a Ph.D. in Computing Science from the University of Aberdeen in Scotland. His research revolves around studying "how people learn", “how to design learning content and stimulate effective learning environments” and “how we can use computing powers to enhance cognitive experiences, particularly in an adaptive e-learning environment. He is interested in human (learners) studies to gather and analyze learner/learning-data to better understand and improve cognitive processes in specific contexts, as well as creating environments that scaffold learning. He is motivated to improve achievement opportunities for learners of all socioeconomic and ethnic backgrounds. Tunji loves nature and his family.


Danielle Chine

Postdoctoral Fellow, faculty advisor Ken Koedinger

Title:  Human-Computer Tutoring Systems, Motivational Interventions, and Efforts to Closing the Gap

Abstract:  Educational technologies show promise in closing opportunity gaps among marginalized students. In this talk, Danielle gives a brief update on her postdoctoral experience researching the human-computer tutoring system, Personalized Learning2 and the motivational math software, MEME Tutor. Her recent contributions to Edtech and learning science assist in the efforts to close the gap— progress is incremental and requires collaboration. 

Bio:  Danielle Chine is a postdoctoral researcher at Carnegie Mellon University in the Human-Computer Innovation Institute within the School of Computer Science. She also  teaches principal licensure courses as a senior lecturer at the University of Akron. Danielle earned an Ed.D. in Educational Leadership, M.S. in Educational Administration, and both a M.S and B.S. in Chemistry. Her past experiences as a chemist, educator, and principal complement her work at CMU in advancing learning science— one teacher, one student, and one study at a time.


Tricia Ngoon

Postdoctoral Fellow, faculty advisor Amy Ogan

Title:  Data-driven Tools for Solving the Puzzle of Teaching

Abstract:  Teaching is much like a puzzle. Figuring out how to put all the pieces of successful teaching and learning together can be challenging. Classroom observations are the most prevalent and effective ways to receive feedback on the nuances of teaching such as interaction with students, use of classroom space, and body posture, but this method doesn't scale. With classroom sensing, we can now collect and present meaningful classroom data to educators to help them improve their teaching practices beyond what is possible with human observation.

In this talk, Tricia will present her work with two systems that leverage classroom data for reflection and goal-setting: ClassInSight and Edulyze. In ClassInSight, we take an autobiographical design approach to developing a tool that facilitates reflective conversation for teachers and professional development researchers to reflect on classroom discussion data and set new goals for improvement. In Edulyze, we develop an analytics pipeline to process and visualize complex, multimodal classroom data to give meaningful and actionable insights to instructors. Both these systems contribute to her vision of data-driven computational coaching tools for open-ended, creative problem-solving.

Bio:  Tricia received her Ph.D in Cognitive Science at UC San Diego, advised by Scott Klemmer. Her research interests span psychology, computer science, and education to better understand creative cognition. Previously, she received her Bachelor's degree in Psychology from UC Berkeley and worked at the Stanford Cognitive and Systems Neuroscience Lab.