Research

Research Experience

During the first few years of my PhD, my research experience specifically focused on the educational sector. On the student side, I worked with the Virtual Learning Lab project. In this project, I developed machine learning models to predict student affect while they interact with an online mathematics learning platform. Besides aiming to improve accuracy, I have investigated how these models generalize between different groups of students [see EDM 2019]. My other work with student affect is through the EMERGE project led by Dr. June Gruber. In this study, I helped develop and implement an experience sampling method to measure first-year students’ affect and its context over several weeks. This gave me experience with the practical side of designing experiments, managing data collection, and later analysis.

In addition to affect, I studied the context through which students are using the platform in order to understand their motivations; that is, are students logging in because they want to improve their math ability, or does their grade depend on them completing a certain assignment? Finally, I mentored a team working to prospectively predict student performance on quizzes using their activity patterns on the platform. This work draws on theoretical modeling of Item Response Theory as well as time-series analysis and modeling through machine learning [see LAK 2021a]. Through this work, I gained experience managing large and messy datasets. This primarily involved working with interaction log and student self-report data modalities.

My work in education also focused on improving teacher outcomes. I previously worked with the Cyber-Enabled Teacher Discourse project to develop an automated approach to teacher feedback [see LAK 2021b] and professional development. In this project, I developed machine learning models to identify key elements of teacher speech associated with greater student engagement and learning. I integrated these models into an automated framework [see CHI 2020] where teachers can record audio of their teaching and receive feedback in the form of a smartphone application. This project gave me experience working with audio processing as well as natural language processing techniques.

More recently, I have pivoted outside of the education (that is, traditional school) sector and am starting to think about learning and cognitive modeling in a broader context. For example, my recent work with the Cyber-Physical Systems: Cognitive Autonomy project involves interpretable modeling of human drivers and their decisions in risky environments [see ITSC 2022]. In the future, these models can be used to estimate human situational awareness [see CPHS 2022] and trigger interventions if needed. This type of work not only draws on my experience in the cognitive sciences but has also allowed me to collaborate with researchers specializing in formal control systems analysis.

Finally, I’m beginning to investigate how we can use technology to teach people complex skills, such as physical tasks or those specific to job training. I am currently working on developing a project that will adaptively create training levels based on their estimated skills while they learn UAV controls for an inspection task.

Research Interests

My research interests are broadly focused on the process of developing novices into experts for complex tasks. This draws on my previous experience in the educational sector, especially questions on how to measure mastery, how to develop a curriculum, and how to autonomously guide someone through the training course.

Beyond the educational questions, I know that earning users’ trust and acceptance is vital for the success of whatever technology we develop. In particular, I’m interested in how users interpret automated formative feedback, especially if their memory of their performance in the exercise differs than the feedback they receive. These research questions will draw on theory and practice of Human-Robot Interaction, especially focused user studies and analysis of explainability.