21 citations · 22 across the 5 of their papers we have counts for
5 papers
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
Mohammad Khalil, Ronas Shakya, Qinyi Liu
The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. Whil…
A Showdown of ChatGPT vs DeepSeek in Solving Programming Tasks
Ronas Shakya, Sam Urmian, Mohammad Khalil
The advancement of large language models (LLMs) has created a competitive landscape for AI-assisted programming tools. This study evaluates two leading models: ChatGPT 03-mini and…
Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
Mohammad Khalil, Sam Urmian, Ronas Shakya +1
In this study, we explore the growing potential of AI and deep learning technologies, particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs), for gene…
Advancing privacy in learning analytics using differential privacy
Qinyi Liu, Ronas Shakya, Mohammad Khalil +1
This paper addresses the challenge of balancing learner data privacy with the use of data in learning analytics (LA) by proposing a novel framework by applying Differential Privacy…
Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics
Qinyi Liu, Mohammad Khalil, Ronas Shakya +1
Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse that current solutions struggle…