Invited Talk: Dr. Min Kyu Kim presented the COI model and the Issue-Hypothesis Tree. 🔗
September 18, 2023
The director, Dr. Min Kyu Kim, was invited to the NSF-ALOE Use-Inspired AI meeting to present the Community of Inquiry (COI) Model and the Issue-Hypothesis Tree.
Dr. Kim introduced the Revised-CoI framework, which includes learning presence, both as a design framework to guide the development of instructional activities integrated with AI technologies for adult online education and as an analytic framework to assess the learner experience in a course with respect to COI elements. Specifically, for ALOE research, he has detailed teaching presence with two distinct types: one led by AI and the other by human instructors who may incorporate AI information. Dr. Kim has demonstrated how the R-COI model could help construct the issue-hypothesis tree (which is an elaboration of issues, testable hypotheses, anticipated outcomes, and appropriate measures) for NSF ALOE research.
AI-ALOE Newsletter Feature 🔗
August 17, 2023
We are delighted to share the recent recognition of our exceptional lab members in the AI-ALOE Summer Newsletter!
Our lab director, Dr. Min Kyu Kim, has been prominently featured in the Team Member Splotlight for his work with SMART. SMART has helped transform concept learning through AI, enabling learners to engage with learning materials and enhance their understanding. SMART's impact, evident through successful implementations in TCSG College classes, has brought about positive engagement, motivation, and performance outcomes for adult learners. Dr. Kim has also unveiled exciting furture directions for SMART, promising continued innovation and advancement.
Furthermore, we celebrate the achivements of our graduate associate, Jinho, in the Student Highlights section. Her research journey within AI-ALOE has centered on personalization in concept learning and AI-augmented summary writing, harnessing the power of SMART. Jinho's dedication to enhaning the learning expereince is driven by a motivation to make a positive impact online education.
Be sure to check out the full newsletter here: AI-ALOE Newsletter: Summer 2023
Congratulations! The director, Dr. Min Kyu Kim, has been awarded a new NSF IUSE grant. 🔗
August 4, 2023
We have great news! The director, Dr. Min Ky Kim, has received the new NSF IUSE grant entitled 'Artificial Intelligence-Scaffolded Pre-Classroom Learning for Large, Introductory Undergraduate Physics Courses.' Dr. M. Shameer Abdeen from the Department of Physics & Astronomy at Georgia State University is the Co-PI of the grant project. See the information below.
NSF: Improving Undergraduate STEM Education (Grant/Award Number: 2315709)
- Title: IUSE-Engaged Student Learning (Level 1): AI-Scaffolded Pre-Classroom Learning for Large/Introductory Undergraduate Physics Courses
- Position: Principal Investigator
- Project dates: 08/01/2023 – 07/31/2026
- Budget: $ 298,375
This Engaged Student Learning Level 1 project aims to serve the national interest by designing and implementing an Artificial Intelligence (AI)-augmented formative assessment and feedback system. This system will help students develop source-based STEM arguments, such as STEM text summarization, or problem spaces, which are mental representations of a problem and of multiple paths to solving it. This will be implemented in large, undergraduate introductory physics courses at an urban university that serves diverse and historically underrepresented student groups. Persistent learner engagement in pre-classroom learning activities is critical to learner success in introductory STEM courses. Undergraduate students often need to develop a solid understanding of content or problem situations in self-paced online learning contexts to prepare for in-classroom active and collaborative learning. However, unsupervised pre-classroom learning can be an ongoing issue in a student-centered learning model. This problematic situation is particularly evident in large introductory-level STEM courses where traditional instructional techniques are less effective. The innovation of the project will include AI-generated adaptive scaffolding information and learning progress feedback with data visualization techniques to help students with concept learning and self-regulatory behaviors. The unique learning opportunities supported by an AI-scaffolded feedback system will significantly increase students' engagement levels in self-paced online pre-classroom learning. This, in turn, will help students acquire content knowledge and build a proper understanding of problems to prepare themselves for success in in-classroom interactive problem-solving activities.
This project will take three phases to develop and investigate the effectiveness of AI-augmented pre-classroom activities to promote engaging student experiences in undergraduate physics courses. The project's research will take a Participatory Research (PR) approach that emphasizes the direct engagement of faculty members who teach physics courses in designing and implementing new assignments. These faculty members will also co-construct research through a partnership with researchers to conduct a mixed-methods study of instructors and students in the courses. The primary research goal of the first phase is to identify topics and problem tasks that utilize AI-scaffolded pre-classroom learning and investigate learner engagement and progression in the pre-class assignments. The evaluation studies during the second phase will prove whether knowledge development during pre-classroom learning can help students solve cognitively demanding tasks in classrooms and develop positive self-efficacy in STEM. The findings will also determine whether AI in education improves students' well-being inside and outside of classrooms, with a focus on students traditionally underrepresented in STEM education. Extensive data collected in the final phase will uncover the relationships among pre-classroom activities, in-classroom performance, self-efficacy, interest in physics, and student backgrounds, including gender, race, ethnicity, first-generation status, and L2 learners. Our sequence mining and cluster analysis will reveal students' different hidden engagement states and group their engagement trajectories, explaining how cluster membership and trajectories vary across students' backgrounds. Consequently, this project will lay the groundwork for further research to develop an AI-scaffolded pre-classroom learning model that promotes most students' success in introductory physics courses. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.
Dr. Young Ju Jeong has joined our lab as a visiting scholar. 🔗
August 4, 2023
It is a great pleasure to introduce another visiting scholar, Dr. Young Ju Jeong, who has joined our lab!
Dr. Jeong received her Ph.D. in computer science and electrical engineering from the University of Southern California, Los Angeles, USA. She is currently an associate professor in the software department at Sookmyung Women's University, Seoul, Korea. Before joining Sookmyung, she worked as a Research Member at Samsung Advanced Institute of Technology, Suwon, South Korea. Her research interests include Augmented Reality and Virtual Reality content rendering and display design. During her visit, she will lead the research and development efforts to integrate AI techniques with AR/VR learning environments.
Welcome our visiting scholar: Mr. Taehee Lee! 🔗
August 3, 2023
We are very excited to introduce Mr. Taehee Lee as our new visiting scholar.
During his visit, he will work with us to advance AI-augmented teaching and learning research. Mr. Lee earned a B.S. degree in electrical engineering from Korea University, Seoul, Korea, in 2004, and an M.S. degree in electrical engineering from the University of Southern California, Los Angeles, United States, in 2006. He is currently pursuing his Ph.D. degree in computer education from Sungkyunkwan University, Seoul, Korea. Additionally, he holds a senior engineer position at Korea Telecom in Seoul, South Korea. Prior to joining KT, he worked as a junior engineer at Samsung Advanced Institute of Technology. His areas of expertise include computer vision and specialized person attribute recognition in artificial intelligence, industrial/vertical deep learning applications, unstructured data analysis, and AI-based education platforms.
AI-ALOE's Annual Review Meeting attended by AI2 Lab Members 🔗
June 22, 2023
Director Dr. Min Kyu Kim and the graduate associates from the AI2 lab, Jinho Kim and Yoojin Bae, attended the AI-ALOE Annual Review Meeting on June 22, 2023. NSF's annual review team, as well as multiple AI-ALOE scholars, research scientists, postdocs, and graduate research assistants participated in this meeting either in person or virtually.
During the meeting, Yoojin presented about Concept Learning in the Research Overview & Overall Evaluations session while Jinho presented about Personalization in Concept Learning in the Personalized Learning session.
Our two graduate associates also took part in the poster session to present their recent research work:
- An Investigation of Knowledge-Based AI vs. Human Evaluation in the Context of Academic Summary Evaluation: Similarities, Dissimilarities, and Being Toward Mutual Understandings
- Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course
For more information about the Annual Review Meeting, please visit the following links:
Presentations at the 2023 ISLS Annual Meeting 🔗
June 15, 2023
Dr. Min Kyu Kim along with our graduate associates Jinho Kim and Yoojin Bae presented three short papers at the 2023 International Society of Learning Sciences (ISLS) Annual Meeting that took place from June 10-15 in Montreal, Canada. The sessions were very successful, with a large number of attendees.
A Test of Learning Progress Models Using an AI-Enabled Knowledge Representation System
Monday, June 12th, 3:00 to 4:30 PM EDT, S-LAAI2 - AI and Human Interaction in Learning Environments
This study tested two competing learning progress models - the three-stage and two-stage models - based on an AI-enabled formative assessment tool. Additionally, human-rated scores were used to further validate the models. The data for this investigation consisted of expository essays about a complex problem scenario written by 116 students and 6 experts. The validation analyses, including descriptive statistics, C-LCDM, and group-mean difference tests, demonstrated that the two-stage model was a better framework given the technology. The findings showed the potential of the model to determine learners' conceptual change over time.
An Investigation of Knowledge-Based AI vs. Human Evaluation in the Context of Academic Summary Evaluation: Similarities, Dissimilarities, and Being Toward Mutual Understandings
Monday, June 12th, 3:00 to 4:30 PM EDT, S-LAAI2 - AI and Human Interaction in Learning Environments
This study aims to explore the similarities and dissimilarities of knowledge-based AI evaluations vs. human evaluations and discuss how they can be utilized for formative feedback in academic summary writing. Data were collected from 62 students who utilized AI-based formative feedback to make revisions to their summaries. We compared indices on three dimensions (surface, structure, semantic) that were automatically generated through this software with human-rated evaluations. MANOVA results of learners’ initial draft and final revision showed learning gains in the semantic dimension and human-evaluated scores. Some significant correlations were observed between automatic and human-rated evaluations. Given that each measure can be interpreted to provide different insights, we suggest combining knowledge-based AI and human evaluations for rich and informative feedback.
Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course
Thursday, June 15th, 10:30 to 12:00 PM EDT, CSCL-S10 / Important considerations for STEM collaboration in Higher Education
Given the pervasive use of online learning, especially asynchronous discussion forums, ensuring students’ ongoing engagement is critical. Examining students’ cognitive presence throughout a series of discussions can disclose different engagement patterns. However, little research has leveraged longitudinal discussion data. The current study aims to identify learner engagement states and their trajectories in discussion forums using two-cohort data from a graduate-level online course. We used HMM (Hidden Markov model) to discover the hidden longitudinal engagement clusters. Results showed two clusters in the first cohort and three clusters in the second cohort. Students tended to remain in a similar engagement state, and their participation patterns depended on the discussion dynamics likely influenced by class size.
AI2 lab members attended the AI ALOE's Year 2 Executive Advisory Board Meeting 🔗
May 19, 2023
Director Dr. Min Kyu Kim and the graduate associates from the AI2 lab, Jinho Kim, Lia Haddadian, and Yoojin Bae, attended the AI ALOE's Year 2 Executive Advisory Board Meeting on May 19, 2023. Approximately 40 members, including 7 EAB members, participated in the meeting either in person or online.
During the meeting, Dr. Kim presented his recent AI in education research titled "Personalized Learning Strategies for Concept Learning."
Additionally, Jinho and Yoojin participated in the poster session, where they presented their recent research work:
- An Investigation of Knowledge-Based AI vs. Human Evaluation in the Context of Academic Summary Evaluation: Similarities, Dissimilarities, and Being Toward Mutual Understandings
- Clustering Cognitive Engagement Changes in Longitudinally Traced Discussion Data from a Graduate-Level Online Course
For more information about the EAB meeting, please visit the following link: https://aialoe.org/eab2023/
NSF AI ALOE Institute Research Videos Now Available 🔗
May 4, 2023
We have created two video presentations for public access. The videos introduce our AI research and development, centering on two topics: Concept Learning and Personalization. We define concept learning as the cognitive process of building a solid understanding of STEM content necessary for developing critical thinking and problem-solving skills. Personalization is one of the goals pursued in AI in education that enables learners to be better engaged in and regulate their learning and performance. Additionally, personalized support for instructors can empower them to support learners in more adaptive and appropriate ways. Our three graduate associates, Jinho, Lia, and Yoojin, have served as narrators. Enjoy!
Attending and Presenting at the 2023 AERA Annual Meeting 🔗
April 13, 2023
Members of our AI2 Lab attended and presented at the 2023 American Educational Research Association (AERA) Annual Meeting, which took place from April 13-16 at Chicago, IL.
Dr. Min Kyu Kim and our graduate associate, Jinho Kim, presented the paper titled "Using Machine Learning for Cognitive Presence Detection in Asynchronous Online Learning" at the Instructional Technology SIG Roundtables on the 13th.
Title:
Using Machine Learning for Cognitive Presence Detection in Asynchronous Online Learning
Abstract:
This study aims to explore and develop a machine learning model to automatically detect individual students’ levels of cognitive presence they exert while participating in online discussions. To this end, we used the Practical Inquiry Model to analyze 1,360 discussion posts gathered from the multiple sections of a graduate-level online course. We have used the training data to develop a machine learning model. Especially, we tested a deep learning algorithm, Bidirectional Encoder Representations from Transformers (BERT) model, to find the most suitable hyperparameters. Our final model showed a 94% accuracy in detecting cognitive presence levels reflected on given discussion posts. Findings demonstrate the potential of AI to detect students’ cognitive presence and provide real-time feedback.