Jiho Noh

AI-based Automation for Classroom Discourse Analysis (ADAS): A Foundation for Supporting Science Teacher Professional Development

This project aims to develop an Automated Discourse Analysis (ADAS) to enhance science classroom discourse by accurately distinguishing between knowledge construction and reproduction, crucial for fostering scientific reasoning. The project seeks to overcome current limitations in AI's ability to recognize epistemic work in educational settings. Within this project scope, we expand training data, optimize various AI models for discourse analysis, measure the models' confidence in predictions, and pilot the system with diverse secondary science teachers. The goal is to provide teachers with reliable, evidence-based feedback to improve discourse patterns and reduce achievement gaps, ultimately supporting complex pedagogical reasoning in science education.

Team members: Mukhesh Raghava Katragadda, Raymond Carl, Dr. Soon Lee, Dr. Jiho Noh
Supported by Interdisciplinary Seed Grant, Kennesaw State University

Creativity Assessment

This research aims to develop an automated creativity assessment using various AI/ML methodologies including Poly-Encoder, LLM-based regression methods, and pair-wise comparison-based ranking techniques. The project focuses on evaluating the creativity of student-generated ideas in scientific research settings, providing insights into the effectiveness of different assessment approaches and their potential applications in fostering creativity in learning environments.

Team members: Phillip Gregory, Sam Grouchnikov, Stanley Nurnberger, Dr. Jiho Noh
Supported by Interdisciplinary Seed Grant, Kennesaw State University

Exploring the Opioid Epidemic in Rural Areas: A Comprehensive Analysis of Opioid Use and Comorbidities in MedSter EHR Ddata

This project use various medical datasets to examine the prevalence and patterns of opioid use and associated comorbidities. The findings aim to inform interventions and policies to address the opioid epidemic and disparities in health and health care.

Automated Domain Question Mapping with Educational Learning Materials

This project develops an innovative approach to constructing Domain Question Maps (DQMs) to address challenges in automatically creating concept maps from unstructured educational materials. By leveraging publicly available question-answering datasets, the study fine-tunes pre-trained language models for question generation and uses a textbook's hierarchical outline to train a specificity classification model. This method formulates specific questions aligned with learning objectives, enhancing knowledge representation and learner engagement. The findings demonstrate that DQMs effectively generate educational questions and identify hierarchical relationships, facilitating personalized and adaptive learning, marking a significant advancement over traditional concept maps.

Educational Question Generation and Evaluation

This study introduces the YouTube Learners’ Questions on Bloom’s Taxonomy Dataset (YouLeQD), which compiles questions posed by learners in YouTube lecture video comments. The research highlights the development of two RoBERTa-based classification models that utilize Large Language Models to identify questions and assess their cognitive complexity according to Bloom’s Taxonomy. By analyzing the cognitive complexity of these learner-posed questions and their interaction metrics, the study provides valuable insights that can enhance the development of AI models for education, ultimately improving the learning experience for students. This innovative approach underscores the significance of understanding questioning in educational contexts, particularly with the integration of artificial intelligence.

Team members: Nong Ming, Sachin Sharma, Dr. Jiho Noh
Supported by Interdisciplinary Seed Grant, Kennesaw State University