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New frontiers in simulation-based teacher training have been unveiled with the advancement of artificial intelligence (AI). Integrating AI into virtual student agents increases the accessibility and affordability of teacher training simulations, but ...

This study explores the dynamics of engagement in online learning communities (OLCs), focusing on online math discussion forums. It employs Social Network Analysis (SNA) and Cross-Recurrence Quantification Analysis (CRQA) to examine interaction patterns ...

Automated Writing Evaluation (AWE) tools have proved beneficial to writing development. Research on AWE methods is essential for improving tool performance and further comparative studies are needed as new methods emerge. This study examines the ...

Ensuring constructive alignment between learning outcomes (LOs) and assessment design is crucial to effective learning design (LD). While previous research has explored the alignment of LOs with assessments, there is a lack of empirical studies on how ...

AI models have shown a remarkable ability to perform representation learning using large-scale data. In particular, the emergence of Large Language Models (LLMs) attests to the capability of AI models to learn complex hidden structures in a bottom-up ...

Educational stakeholders are often particularly interested in sparse, delayed student outcomes, like end-of-year statewide exams. The rare occurrence of such assessments makes it harder to identify students likely to fail such assessments, as well as ...

This paper introduces a novel approach to create a high-resolution “map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive processes and concepts required for problem solving in a college-level ...

The increasing use of machine learning in learning analytics (LA) has raised significant concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual-purpose tool, enhancing privacy and improving fairness in LA models. However, ...

Students are increasingly relying on Generative AI (GAI) to support their writing—a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI ...

This research investigates the characteristics of student essays written with and without generative AI assistance, using stylometric analysis and deep learning techniques to explore human-AI collaboration in academic writing. To address three research ...

The widespread use of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, has come along with multiple benefits in education (e.g., 24h teacher, augmenting student monitoring). However, at the same time, these tools hinder teachers’ ...

Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating language. Advances in generative AI via large language models (LLMs) are ...

The role of multiple-choice questions (MCQs) as effective learning tools has been debated in past research. While MCQs are widely used due to their ease in grading, open response questions are increasingly used for instruction, given advances in large ...

As large language models (LLMs) show great promise in generating a wide spectrum of educational materials, robust yet cost-effective assessment of the quality and effectiveness of such materials becomes an important challenge. Traditional approaches, ...

Peer feedback (PF) is essential for improving student learning outcomes, particularly in Computer-Supported Collaborative Learning (CSCL) settings. When using digital tools for PF practices, student data (e.g., PF text entries) is generated automatically. ...

Skills in collaborative problem solving (CPS) are essential for the 21^st century, enabling students to solve complex problems effectively. As the demand for these skills rises, understanding their development and manifestation becomes increasingly ...

Uncovering algorithmic bias related to sensitive attributes is crucial. However, understanding the underlying causes of bias is even more important to ensure fairer outcomes. This study investigates bias associated with Attention Deficit Hyperactivity ...

Linguistic synchrony, or alignment, has been shown to be critical for student learning, particularly for L2 students (second language learners), whose patterns of synchrony often differ from fluent speakers due to proficiency constraints. While many ...

Question-answering systems facilitate adaptive learning and respond to student queries, making education more responsive. Despite that, challenges such as natural language understanding and context management complicate their widespread adoption, where ...

Receiving timely and personalized feedback is essential for second-language learners, especially when human instructors are unavailable. This study explores the effectiveness of Large Language Models (LLMs), including both proprietary and open-source ...