Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Project Overview
The document explores the integration of generative AI (GenAI) in education, emphasizing its role in enhancing learning performance through Intelligent Tutoring Systems (ITSs) and addressing data sparsity. A systematic framework is proposed that utilizes tensor factorization for data imputation alongside Generative Adversarial Networks (GAN) and Generative Pretrained Transformers (GPT) for data augmentation. The research findings demonstrate that tensor factorization surpasses traditional knowledge tracing methods in predictive accuracy and learner modeling, with GAN providing stability and GPT offering flexibility in generating synthetic data. Additionally, the document highlights various applications of GenAI in educational settings, such as delivering feedback, personalizing learning experiences, and increasing student engagement. Use cases illustrate how large language models (LLMs) like ChatGPT can automate feedback generation, create multiple-choice questions, and support self-regulated learning, showcasing the transformative potential of these technologies in facilitating tailored educational experiences and improving overall learning outcomes.
Key Applications
Generative AI for Personalized Feedback and Tutoring
Context: Higher education and K-12 settings, targeting data science and literacy education, with a focus on personalized learning experiences and self-regulated learning.
Implementation: Utilization of large language models (LLMs) and generative models to provide personalized feedback, generate assessments, and model learner characteristics for tailored instruction.
Outcomes: ['Improved feedback mechanisms for students, enhancing learning through immediate responses.', 'Personalized feedback enabling tailored learning experiences, leading to increased student agency and motivation.', 'Improved learning outcomes through tailored instruction.']
Challenges: ['Limitations in understanding context and nuances of student responses.', 'Data privacy concerns and the need for robust data infrastructure.', 'Complexity of accurately modeling diverse learner needs.', 'Dependence on student motivation and varying levels of digital literacy.']
Implementation Barriers
Technical Barrier
High levels of data sparsity make it difficult to accurately model learner performance. Additionally, limitations exist in understanding the context and nuances of student interactions with AI.
Proposed Solutions: Utilization of tensor factorization for data imputation, generative models like GAN and GPT for data augmentation, and developing more advanced LLMs with improved contextual awareness.
Implementation Barrier
Difficulty in capturing the complex nature of learner data and performance patterns.
Proposed Solutions: Use of advanced machine learning techniques and generative models that can learn from existing data distributions.
Privacy Barrier
Concerns over student data privacy and security when using AI tools.
Proposed Solutions: Implementing robust data protection protocols and transparent data usage policies.
Quality Barrier
Ensuring the quality and relevance of AI-generated educational materials.
Proposed Solutions: Incorporating human oversight in the content generation process.
Engagement Barrier
Variability in student motivation and digital literacy affecting engagement with AI tools.
Proposed Solutions: Providing training and resources to enhance digital literacy and motivation.
Project Team
Liang Zhang
Researcher
Jionghao Lin
Researcher
John Sabatini
Researcher
Conrad Borchers
Researcher
Daniel Weitekamp
Researcher
Meng Cao
Researcher
John Hollander
Researcher
Xiangen Hu
Researcher
Arthur C. Graesser
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Liang Zhang, Jionghao Lin, John Sabatini, Conrad Borchers, Daniel Weitekamp, Meng Cao, John Hollander, Xiangen Hu, Arthur C. Graesser
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai