Online educational technologies facilitate pedagogical experimentation, but typical experimental designs assign a fixed proportion of students to each condition, even if early results suggest some are ineffective. Experimental designs using multi-armed bandit (MAB) algorithms vary the probability of condition assignment for a new student based on…

Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that…

This study addresses the opportunities presented by AI applications in education and the ethical issues brought about by this technology. AI in education holds excellent potential in personalized learning, automated assessment and feedback, and monitoring and analyzing student performance. However, using these technologies also raises ethical…

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…

Recent research suggests that neural networks, algorithms designed to reflect the human brain's behavior to recognize patterns, can be used to develop data dashboards that provide teachers with more specific and frequent feedback to improve their instruction (Jacobs et al., 2022). This qualitative case study examines six teachers' perceptions of…

This study offers an analysis and comparison of search results from Google concerning the topic of Artificial Intelligence (AI) in two geographically and politically different contexts: the United States and Italy. As new AI systems, tools, and solutions are developed and implemented in each sector of human life on a global scale, certain…

Research into "gaming the system" behavior in intelligent tutoring systems (ITS) has been around for almost two decades, and detection has been developed for many ITSs. Machine learning models can detect this behavior in both real-time and in historical data. However, intelligent tutoring system designs often change over time, in terms…

Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do…

Contextual multi-armed bandits have previously been used to personalize student support messages given to learners by supplying a model with relevant context about the user, problem, and available student supports. In this work, we propose using careful feature selection with relevant domain knowledge to improve the quality of student support…

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term…

Educational Robotics is increasingly used in elementary-school classrooms to develop students' algorithmic thinking and programming skills. However, most research appears descriptive and lacks experimental evidence on the effects of teaching interventions using robotics to develop algorithmic thinking. Using the robots Dash and Dot, this study…

The Jeong (2020) study found that greater use of backward and depth-first processing was associated with higher scores on students' argument maps and that analysis of only the first five nodes students placed in their maps predicted map scores. This study utilized the jMAP tool and algorithms developed in the Jeong (2020) study to determine if the…

The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…

In this paper, we report the results of a 3-year, quasi-experimental study comparing students' engagement and deep learning of course materials between students who took an undergraduate engineering course that used a video game approach to a control group. The video game, EduTorcs, provided challenges in which students devised control algorithms…

Computational thinking (CT) is a fundamental ability for learners in today's society. Although CT assessments and interventions have been studied widely, little is known about CT predictions. This study predicted students' CT achievement in the ICILS 2018 using five machine learning models. These models were trained on the data from five European…