Sangho Suh

selected publications [full list]

1. UIST

   CodeToon: Story Ideation, Auto Comic Generation, and Structure Mapping for Code-Driven Storytelling

   Suh, Sangho, Zhao, Jian, and Law, Edith

   In ACM Symposium on User Interface Software and Technology (UIST) 2022

   Abs arXiv PDF DOI Talk Demo Preview

   Recent work demonstrated how we can design and use coding strips, a form of comic strips with corresponding code, to enhance teaching and learning in programming. However, creating coding strips is a creative, time-consuming process. Creators have to generate stories from code (code->story) and design comics from stories (story->comic). We contribute CodeToon, a comic authoring tool that facilitates this code-driven storytelling process with two mechanisms: (1) story ideation from code using metaphor and (2) automatic comic generation from the story. We conducted a two-part user study that evaluates the tool and the comics generated by participants to test whether CodeToon facilitates the authoring process and helps generate quality comics. Our results show that CodeToon helps users create accurate, informative, and useful coding strips in a significantly shorter time. Overall, this work contributes methods and design guidelines for code-driven storytelling and opens up opportunities for using art to support computer science education.
2. DIS Best Paper
   Honorable
   Mention Award

   PrivacyToon: Concept-driven Storytelling with Creativity Support for Privacy Concepts 🎖

   Suh, Sangho, Lamorea, Sydney, Law, Edith, and Zhang-Kennedy, Leah

   In ACM SIGCHI Conference on Designing Interactive Systems (DIS) 2022

   Abs PDF DOI Website Demo Preview

   With privacy-related concepts often abstract and difficult to define, comics can be an effective visual storytelling medium for explaining and raising awareness about privacy. However, existing privacy and security educational comics do not support content creation. To address this, we contribute PrivacyToon, a comic-based authoring tool that leverages concept-driven storytelling and ideation cards to help users create customizable privacy-related visual content. Our exploratory user study with 23 students and teachers shows PrivacyToon’s potential as a creative tool for communicating privacy concepts and stories. Our results show that a wide range of creativity preferences and contexts must be considered when designing systems that integrate ideation card-based design processes.
3. IUI

   Leveraging Generative Conversational AI to Develop a Creative Learning Environment for Computational Thinking

   Suh, Sangho, and Pengcheng, An

   In 27th Annual Conference on Intelligent User Interfaces (IUI) 2022

   Abs PDF DOI Talk

   We explore how generative conversational AI can assist students’ learning, creative, and sensemaking process in a visual programming environment where users can create comics from code. The process of visualizing code in terms of comics involves mapping programming language (code) to natural language (story) and then to visual language (of comics). While this process requires users to brainstorm code examples, metaphors, and story ideas, the recent development in generative models introduces an exciting opportunity for learners to harness their creative superpower and researchers to advance our understanding of how generative conversational AI can augment our intelligence in creative learning contexts. We provide an overview of our system and discuss interaction scenarios to demonstrate ways we can partner with generative conversational AI in the context of learning computer programming.
4. SIGCSE

   Using Comics to Introduce and Reinforce Programming Concepts in CS1

   Suh, Sangho, Latulipe, Celine, Lee, Ken Jen, Cheng, Bernadette, and Law, Edith

   In ACM Technical Symposium on Computer Science Education SIGCSE 2021

   Abs arXiv PDF DOI Slides Website Talk Blog

   Recent work investigated the potential of comics to support the teaching and learning of programming concepts and suggested several ways coding strips, a form of comic strip with its corresponding code, can be used. Building on this work, we tested the recommended use cases of coding strip in an undergraduate introductory computer science course at a large comprehensive university. At the end of the course, we surveyed students to assess their experience and found they benefited in various ways. Our work contributes a demonstration of the various ways comics can be used in introductory CS courses and an initial understanding of benefits and challenges with using comics in computing education gleaned from an analysis of students’ survey responses and code submissions.
5. VL/HCC

   Coding Strip: A Pedagogical Tool for Teaching and Learning Programming Concepts through Comics

   Suh, Sangho, Lee, Martinet, Xia, Gracie, and Law, Edith

   In IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2020

   Abs PDF DOI Supp Slides Website Blog

   The abstract nature of programming makes learning to code a daunting undertaking for many novice learners. In this work, we advocate the use of comics—a medium capable of presenting abstract ideas in a concrete, familiar way—for introducing programming concepts. Particularly, we propose a design process and related tools to help students and teachers create coding strips, a form of comic strips that are associated with a piece of code. We conducted two design workshops with students and high school computer science teachers to evaluate our design process and tools. We find that our design process and tools are effective at supporting the design of coding strips and that both students and teachers are excited about using coding strip as a tool for learning and teaching programming concepts.

6. CHI

   Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents

   Law, Edith, Baghaei Ravari, Parastoo, Chhibber, Nalin, Kulic, Dana, Lin, Stephanie, Pantasdo, Kevin D, Ceha, Jessy, Suh, Sangho, and Dillen, Nicole

   In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems 2020

   Abs PDF DOI Blog

   Learning by teaching is an established pedagogical technique; however, the exact process through which learning happens remains difficult to assess, in part due to the variability in the tutor-tutee pairing and interaction. Prior research proposed the use of teachable agents acting as students, in order to facilitate more controlled studies of the learning by teaching phenomenon. In this work, we introduce a learning by teaching platform, Curiosity Notebook, which allows students to work individually or in groups to teach a conversational agent a classification task in a variety of subject topics. We conducted a 4-week exploratory study with 12 fourth and fifth grade elementary school children, who taught a conversational robot how to classify animals, rocks/minerals and paintings. This paper outlines the architecture of our system, describes the lessons learned from the study, and contributes design considerations on how to design conversational agents and applications for learning by teaching scenarios.

7. NIPS

   Re-VACNN: Steering Convolutional Neural Network via Real-time Visual Analytics

   Chung, Sunghyo, Park, Cheonbok, Suh, Sangho, Kang, Kyeongpil, Choo, Jaegul, and Kwon, Bum Chul

   In Future of interactive learning machines workshop at the 30th annual conference on neural information processing systems (NIPS) 2016

   Abs PDF Poster Slides Talk Demo Blog

   Recently, deep learning has become exceptionally popular due to its outstanding performances in various machine learning and artificial intelligence applications. Convolutional neural network (CNN), a representative model of deep learning, has been successfully applied to solve computationally burdening tasks in various applications like computer vision. Despite its outstanding capability, training a CNN model properly is time-consuming and prone to overfitting and/or bad local minima. To address these issues, this study aims at improving the interpretation of the training process and using it for subsequent human intervention, specifically steering the training process of CNN model in the real time. In this paper, we present ReVACNN, a real-time visual analytic system for CNN, where (1) the overall training process (e.g., the amount of activations and that of changes each filter/layer has at a particular iteration/epoch) is visualized in network view and (2) the 2D embedding of trained filters within layers is visualized to show the relationships between filters as well as layers. In particular, ReVACNN allows users to perform several interactions in real time: (1) skipping the gradient descent update on the sub-part of a CNN model to reduce the subsequent training time and (2) steering filters interactively in the 2D embedding view to avoid bad local minima. At the end, we present several use cases that demonstrate the benefits users can gain from ReVACNN.
8. ICDM Best Student
   Paper Award

   L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization 🏆

   Suh, Sangho, Choo, Jaegul, Lee, Joonseok, and Reddy, Chandan K

   In 2016 IEEE 16th International Conference on Data Mining (ICDM) 2016

   Abs PDF Code Slides Talk Blog

   Nonnegative matrix factorization (NMF) has been widely applied in many domains. In document analysis, it has been increasingly used in topic modeling applications, where a set of underlying topics are revealed by a low-rank factor matrix from NMF. However, it is often the case that the resulting topics give only general topic information in the data, which tends not to convey much information. To tackle this problem, we propose a novel ensemble model of nonnegative matrix factorization for discovering high-quality local topics. Our method leverages the idea of an ensemble model, which has been successful in supervised learning, into an unsupervised topic modeling context. That is, our model successively performs NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. Our algorithm for updating the input matrix has novelty in two aspects. The first lies in utilizing the residual matrix inspired by a state-of-the-art gradient boosting model, and the second stems from applying a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users. We evaluate our proposed method by comparing it against other topic modeling methods, such as a few variants of NMF and latent Dirichlet allocation, in terms of various evaluation measures representing topic coherence, diversity, coverage, computing time, and so on. We also present qualitative evaluation on the topics discovered by our method using several real-world data sets.