As online learning tools become more widespread, understanding student behaviors through learning analytics is increasingly important. Traditional methods relying on system log data fall short of capturing the full range of cognitive strategies students use. To address this, we developed an in-depth post-assignment reflection dashboard that…

Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined…

Many AIED systems support self-regulated learning, yet, support for setting and achieving practice goals has received little attention. We examine how middle school students respond to system-recommended practice goals, building on the success of similar data-driven recommendations in other domains. We introduce an adaptive dashboard in an…

Reading assessments are essential for enhancing students' comprehension, yet many EdTech applications focus mainly on outcome-based metrics, providing limited insights into student behavior and cognition. This study investigates the use of multimodal data sources -- including eye-tracking data, learning outcomes, assessment content, and teaching…

Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning…

In this study, we explore the application of process mining techniques on assessment log data to explore problem-solving strategies in Algebra. By analyzing sequences of student activities, we demonstrate the significant potential of process mining in identifying problem-solving strategies that lead to successful and unsuccessful outcomes. Our…

Many U.S. districts are investing in education technologies to improve student learning. Yet, when technologies with established promise of evidence are deployed at scale, they frequently encounter challenges that compromise their efficacy. MathSpring is a technology-based math learning platform that offers personalized content, remedial tutoring,…

This paper explores how learning analytics data provided by a math problem-solving educational technology platform informed 5th and 6th grade teachers' instructional decisions around socioemotional learning (SEL). MathSpring is an educational technology tool that provides teachers with data on students' effort, progress, and emotions while…

Educational technologies, such as teacher dashboards, are being developed to support teachers' instruction and students learning. Specifically, dashboards support teachers in providing the just-in-time instruction needed by students in complex contexts such as science inquiry. In this study, we used the Inq-Blotterteacher-alerting dashboard to…

Online educational games have been widely used to support students' mathematics learning. However, their effects largely depend on student-related factors, the most prominent being their behavioral characteristics as they play the games. In this study, we applied a set of learning analytics methods ("k"-means clustering, data…

Providing high-level support to students on NGSS inquiry practices can be challenging; however, teacher dashboards can help teachers provide just-in-time instruction to students, both in-person and online. Prior work has shown some success with a dashboard that alerts teachers in real time on students' science inquiry difficulties, but teachers…

This paper demonstrated how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. Using a data-driven approach, we examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance (i.e. posttest math knowledge scores) prediction; and…

Involving stakeholders throughout the creation of new educational technologies can help ensure their usefulness and usability in real-world contexts. However, given the complexity of learning analytics (LA) systems, it can be challenging to meaningfully involve non-technical stakeholders throughout their design and development. This article…

This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student…

We investigated the effects of proximal grouping of numbers, problem-solving goals to make 100, and prior knowledge on students' solution strategies in an online mathematics game. Logistic regression on 857 problem-level data points from 227 middle-school students showed that students were more likely to use productive solution strategies on…