Knowledge tracing (KT) models have been a commonly used tool for tracking students' knowledge status. Recent advances in deep knowledge tracing (DKT) have demonstrated increased performance for knowledge tracing tasks in many datasets. However, interpreting students' states on single knowledge components (KCs) from DKT models could be challenging…

Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…

Knowledge Tracing (KT) models predicting student performance in intelligent tutoring systems have been successfully deployed in several educational domains. However, their usage in open-ended programming problems poses multiple challenges due to the complexity of the programming code and a complex interplay between syntax and logic requirements…

Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…

The project-based learning model via generative artificial intelligence, or PjBL model via Gen-AI, is a research tool that was initiated based on the concept of project-based learning management focusing mainly on self-directed learning, in which learners are able to learn and practice through the projects they are interested in as to their…

Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this…

Based on hundreds of thousands of hours of data about how students learn in massive open online courses, educational machine learning promises to help students who are learning to code. However, in most classrooms, students and assignments do not have enough historical data for feeding these data hungry algorithms. Previous work on predicting…

Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure. In this article, we make the journey from defining a formal measure of creativity, that is, efficiently computable to applying the measure in a practical domain. The measure is general and relies on core theoretical concepts in…

The motivation, philosophy, and organization of a course on artificial intelligence in chemical engineering is presented. The purpose is to teach undergraduate and graduate students how to build AI-based models that incorporate a first principles-based understanding of our products, processes, and systems. This is achieved by combining…

Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without…

Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study…

This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models…

The University of Palermo is proud to host the 18th International Conference on Educational Data Mining (EDM) in Palermo, Italy, from July 20 to July 23, 2025. EDM is the annual flagship conference of the International Educational Data Mining Society. This year's theme is "New Goals, New Measurements, New Incentives to Learn." The theme…

Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…

Mobile learning or mLearning has become an essential tool in many fields in this digital era, among the ones educational training deserves special attention, that is, applied to both basic and higher education towards active, flexible, effective high-quality and continuous learning. However, despite the advances in Natural Language Processing…