Christine Kwon

Welcome to my personal website! I am a fourth year PhD student in the Human Computer Interaction Institute (HCII) at Carnegie Mellon University advised by Dr. John Stamper and Dr. Amy Ogan .

My research interests center on educational technologies (EdTech) and learning sciences . I am most passionate about improving access to meaningful education through accessible educational technologies for marginalized communities of learners globally! Through my research, I aim to work with educational technologies globally and collaborate with other educators and researchers on providing meaningful, affordable, and high-quality education by leveraging the functionalities of EdTech. My work has primarily focused on how low-infrastructure and contextually aligned technologies can support out-of-school learning for marginalized learners. Through my research, I aim to work with educational technologies globally and collaborate with other educators and researchers on providing meaningful, affordable, and high-quality education by leveraging the functionalities of EdTech!

I am highly interested in research collaborations and connecting with those who share my passion for EdTech research! Please feel free to reach out to discuss potential collaborative opportunities.

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Recent News

Publications

Low-infrastructure EdTech as a digital learning resource is especially critical to understand in remote contexts where educational opportunities and resources are limited. Our work investigated insights from 81 learners who engaged with a remote course that provided engineering education through radios and mobile phones in rural Uganda.

This study is part of a larger quasi-experiment conducted in Uganda, where learners could choose to learn in English, Leb-Lango (a local language), or in Hybrid mode (a combination of both) in a remote EdTech course. We examined how learners who chose the Hybrid option navigated English and Leb-Lango.

In this study, we conducted a smallscale randomized controlled trial to examine the causal impact of instructional language on learning in a radio- and mobile-based engineering course, providing insights relevant to scaling learning in multilingual contexts.

This paper investigates learner language usage behaviors in a quasi-experiment in Uganda with 2,931 participants enrolled in a non-formal radio- and mobile-based engineering course, where learners self-selected instruction in Leb Lango (a local language), English, or a Hybrid option combining both languages.

This quasi-experimental study integrates a large language model (LLM) with expert qualitative analysis to examine how instructional design variations in computer-supported collaborative learning (CSCL) shape collaboration in data-centric programming.

In this paper, we introduce a novel solution for studying real-time student engagement with offline learning technology. We present "Prize Codes" as a novel method for measuring student engagement with mobile-learning broadcasts.

This study examines genAI's impact on student learning and self-efficacy within a graduate course where students created eight microlessons incorporating distinct learning science principles through an A/B experimental design.

This study investigates the integration of genAI into the microlesson creation process for novice instructional designers. Conducted within a graduate-level course focusing on instructional design and learning engineering, we examined how using genAI influences the quality of student-created microlessons.

We studied a randomized controlled trial (RCT) in a rural region within Northern Uganda, comparing a local versus colonial language as a medium of instruction in an engineering course for out-of-school learners.

In this study, we develop CoComTag, an LLM-powered approach using GPT-4o that captures students' collaborative competency.

We analyzed log interaction data from an existing offline radio-and-phone based course to examine how participation was associated with changes in learners’ STEM motivations, engineering mindsets, and income mobility. We further investigated how learner outcomes related to initial motivation, demographic characteristics, and access to technology.

Our work provides a unique LLM-based multimodal pipeline to interpret and verify task-related key steps in a video within organized knowledge schemas, in which demonstrated video steps are automatically extracted, systematized, and validated in comparison to a text manual of official steps.

In this work, we introduce an instructional content repository, a "YouTube for the Navy" that makes crucial content easily accessible and allows repository maintenance to keep content accurate and up-to-date. Repository maintenance can be laborious and prone to human error.

This work describes an educational technology system, Yiya Air Science, to reach “last mile” students in Africa (specifically rural Uganda) where access to basic computers or smartphones is rare. Courses are deployed on a system of radio broadcast and basic texting phones (USSD).

To enhance the process of knowledge sharing, we propose an advanced ontological knowledge structure which we denote as a Knowledge Object (KO) defined around a particular task of action.