Karen Chen's thesis defense: this Thursday 6/11, 1pm on zoom

[Artur Dubrawski]

Team,

Please join and become a witness of a culmination of Karen's 16 year tenure
as a researcher in the Auton Lab. She will be defending her doctoral
dissertation this Thursday at 1pm via zoom.

Hope to see a lot of Autonians there to cheer for Karen!

Artur

*Title: * Augmenting Human Perceptual and Reasoning Capabilities with
Intelligent Multimodal Analytics: From Critical Care to Coaching Math
Problem Solving

Zoom Meeting

https://cmu.zoom.us/j/94937712479?pwd=MFZubFNVdUNjYWxFQkl0UWRhZVJWUT09

Meeting ID: 949 3771 2479

Password: 562565

*Thesis Committee:  *

Artur Dubrawski (Chair)

Daniel Nagin

Amy Ogan (HCII, CMU)

Sidney D’Mello (University of Colorado Boulder)



*Link to thesis draft *

https://cmu.box.com/s/1n2hg6q4l5yrg8hd0b4brbf7o58cyrlp



*Abstract:*

Augmenting human perceptual and reasoning capability by leveraging machine
intelligence has been on the agenda of AI exploration since its inception.
The recent developments in sensing technology make it possible to generate
and accumulate massive amounts of high frequency multimodal data. This
creates new opportunities for automating the analysis of this type of data
where the dynamic and time sensitive decision making plays a major role in
quality of service. In this thesis, I conduct simultaneous investigation in
two presumably unrelated domains: monitoring of patients for instability in
critical care, and monitoring elementary school students for their
cognitive and affective states during math problem solving exercises. Those
two contexts share similar monitoring paradigms with similar challenges,
and offer comparable opportunities with high frequency multimodal
observations. The goal of my thesis is to explore and demonstrate the
practical utility of multimodal analytics of high frequency monitoring data
in support of decision making in those contexts.

I organized my research work around  the following two thrusts. The first
thrust (Chapter 2) is motivated by the tension between the limited human
capacity of ICU nurses to make best use of monitoring resources in
fast-paced critical care settings. The current practice primarily relies on
the vigilance of clinical personnel to recognize CRI early, however this is
often in-sufficient given the complexity and apparent unpredictability of
temporal patterns of risk progression for CRI. The methods developed in
this project aim to augment individual nurse ability to identify
physiologic indicators of impending CRI (perceptual capability)and
recognize how bedside monitor data patterns reflect  heterogeneous disease
progression (reasoning capability).

The second thrust concerns education: augmenting human teacher perceptual
and reasoning capability in providing personalized and adaptive support to
students in either online or offline learning environments. The first
project (Chapter 3) aims to address the reasoning capabilities required by
teachers to understand and eventually to adapt to the learning progression
in math practices ("learning curve") of a large number of students using
the fine-grained log data collected from an on-line learning environment. I
developed a data driven method for decomposing population level learning
curves into distinctive groups that reveal interpretable patterns of "skill
growth" which correlate with the students’ future learning outcomes.The
second project (Chapter 4) explores off-line learning scenarios of coaching
math problem solving with young students. I collected multi-modal data from
one-on-one coaching sessions between parent-child dyads in a naturalist
environment. Using those datasets, I developed analytical methods that may
ease the cognitive load in resolving the practical teaching challenge of
“assistance dilemma”, i.e. making real time decisions with regard the
timing and the type of support to provide (cognitive, metacognitive or
social), in order to maximize students’ exposure to “productive struggles”.
Eventually, those methods may be used to bootstrap the perceptual and
reasoning modules toward a vision of an "intelligent monitor" in the
classroom and home that can recognize and reason over dynamic cognitive and
affective student processes during math problem solving in an off-line
learning environment.
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