As educators turn to technology to supplement classroom instruction, the integration of natural language processing (NLP) into educational technologies is vital for increasing student success. NLP involves the use of computers to analyze and respond to human language, including students' responses to a variety of assignments and tasks. While NLP…

Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent…

The emergence of immersive digital technologies, such as shared Augmented Reality (shAR), Virtual Reality (VR) and Motion Capture (MC) offers promising new opportunities to advance our understanding of human cognition and design innovative technology-enhanced learning experiences. Theoretical frameworks for embodied and extended cognition can…

Since GPT-4's release it has shown novel abilities in a variety of domains. This paper explores the use of LLM-generated explanations as on-demand assistance for problems within the ASSISTments platform. In particular, we are studying whether GPT-generated explanations are better than nothing on problems that have no supports and whether…

The learning sciences inherently involve interdisciplinary research with an overarching objective of advancing theories of learning and to inform the design and implementation of effective instructional methods and learning technologies. In these endeavors, learning sciences encompass diverse constructs, measures, processes, and outcomes…

Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…

Instructors in STEM classrooms often frame students' interactions with technologies to help them learn content. For instance, in many STEM domains, instructors commonly help students translate physical 3D models into 2D drawings by prompting them to focus on (a) orienting physical 3D models and (b) generating 2D drawings. We investigate whether…

This paper responds to a 2016 systematic literature review of the research on learning games by Ke (2016). The review paper unpacked the idea of intrinsic integration in learning games, analyzing important emergent themes. The key ideas and the value of this review are discussed in the context of the recent shift to virtual instruction. The…

In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…

Classroom experiments that evaluate the effectiveness of educational technologies do not typically examine the effects of classroom contextual variables (e.g., out-of-software help-giving and external distractions). Yet these variables may influence students' instructional outcomes. In this paper, we introduce the Spatial Classroom Log Explorer…

Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students -- explanations, feedback, and other pedagogical interactions. Considering the…