Gajin Kwon
Seoul National University · 情報科学
研究室紹介
Professor Gajin Kwon's research lab focuses on educational technology, cognitive psychology, and human-computer interaction, with a strong emphasis on understanding and enhancing learning through digital media and adaptive support systems. The lab investigates how different technologies—such as mobile applications, augmented reality, and intelligent tutoring systems—affect learning outcomes, student engagement, and behavioral regulation in children and adolescents. Key research directions include the impact of reading media (print vs. digital), the design of collaborative learning environments with feedback mechanisms, and the development of neural models for solving educational problems. The lab also explores technology-mediated interventions to address issues like smartphone and internet addiction among youth.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15We investigated the similarities and differences among four addiction groups in Korean adolescents: Non-Addiction (NONE), Smartphone Addiction (SA), Internet Addiction (IA), and Internet-Smartphone Addiction (BOTH). For the dependent variables, we examined 12 addiction-risk factors related to psychology, family, and school environment that can influence the adolescents’ normative developmental path. To collect data, we conducted an addiction-risk factor comparison survey with 768 Korean adolesce
This paper describes results from a series of experimental studies to explore issues related to structuring productive group dynamics for collaborative learning using an adaptive support mechanism. The first study provides evidence in favor of the feasibility of the endeavor by demonstrating with a tightly controlled study that even without adaptive support, problem solving in pairs is significantly more effective for learning than problem solving alone. The results from a second study offer gui
We examined the effects of the reading medium (print vs. digital) on readers’ visual patterns, reading performance, and reading attitudes. Two within-subject experiments were conducted with 74 readers, who read articles using three reading media: print, computer, and tablet. The experimental results showed that in terms of visual patterns, readers exhibited a shorter fixation duration and a higher fixation count during print reading than during screen reading; reading performance, as measured on
Solving algebraic word problems has recently emerged as an important natural language processing task. To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using 'Op (operator/operand)' tokens as a unit of input/output. However, such a neural model suffered two issues: expression fragmentation and operand-context separation. To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses
In this paper, we explore the effect of the form of feedback offered by a computer supported collaborative learning (CSCL) environment on the roles that students see themselves as taking and that their behavior reflects. We do this by experimentally contrasting collaboration in two feedback configurations, one which is identical to the state-of-the-art in intelligent tutoring technology (Immediate Feedback), and one which is based on a long line of investigation of the use of worked out examples
There has been a growing concern over the huge increase in use of smart media by young children. This study explores the possibility of using augmented-reality(AR) for regulat-ing preschoolers' media usage behavior. With MABLE (mobile application for behavioral learning and education), parents can provide AR-assisted feedback by changing facial expressions and sound effects. When overlaying a smart media, which has MABLE running, in front of a QR marker on a puppet, a facial expression is displa
In this paper, we propose a neural model EPT-X (Expression-Pointer Transformer with Explanations), which utilizes natural language explanations to solve an algebraic word problem. To enhance the explainability of the encoding process of a neural model, EPT-X adopts the concepts of plausibility and faithfulness which are drawn from math word problem solving strategies by humans. A plausible explanation is one that includes contextual information for the numbers and variables that appear in a give