A prior study developed the Computational Thinking Scale (CTS) for assessing individuals' computational thinking dispositions in five dimensions: decomposition, abstraction, algorithmic thinking, evaluation, and generalization. This study proposed the Developmental Model of Computational Thinking through validating the structural relationships…

The Big Data for Education Spoke of the NSF Northeast Big Data Innovation Hub and ETS co-sponsored an educational data mining competition in which contestants were asked to predict efficient time use on the NAEP 8th grade mathematics computer-based assessment, based on the log file of a student's actions on a prior portion of the assessment. In…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

Background: The importance of high school completion for jobs and postsecondary opportunities is well- documented. Combined with federal laws where high school graduation rate is a core performance indicator, school systems and states face pressure to actively monitor and assess high school completion. This proposal employs machine learning…

The disruptions to schooling created by the COVID-19 pandemic prompted nationwide concern about negative impacts on academic proficiency, particularly for vulnerable student populations. Pennsylvania's 2021 statewide assessments offered the first opportunity since the pandemic began to assess how students across the Commonwealth were faring,…

Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and effective exploration. Guidance in the form of targeted interventions or feedback therefore has…

Identifying struggling students in real-time provides a virtual learning environment with an opportunity to intervene meaningfully with supports aimed at improving student learning and engagement. In this paper, we present a detailed analysis of quit prediction modeling in students playing a learning game called Physics Playground. From the…

Educational games have become hugely popular, and educational data mining has been used to predict student performance in the context of these games. However, models built on student behavior in educational games rarely differentiate between the types of problem solving that students employ and fail to address how efficacious student problem…

New analytical tools have facilitated the exploration of the trajectories of alcohol use; however, there are a limited number of studies that explore early adolescence. A sample of 5,903 youths followed from sixth through eighth grade was used to (1) examine the trajectories of alcohol use and (2) determine the degree to which common correlates…

When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if…

The present study examined empirical models of variables posited to predict students' homework environment management at the secondary school level. The participants were 866 8th graders from 61 classes and 745 11th graders from 46 classes. Most of the variance in homework environment management occurred at the student level, with classmates'…

This causal comparative study used a set of predictable variables, including school configurations, to predict student proficiency on the Arizona Instrument to Measure Standards (AIMS) test in reading and math. The school configurations were K-8 and middle school. The researcher analyzed the 2011-2012 AIMS reading and math achievement scores of…

Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…

In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a "Discovery with Models" approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its…

Using self-determination theory as a framework, the purpose of this study was to test a structural model of hypothesized relationships among perceived need support from physical education teachers (autonomy support, competence support, and relatedness support), psychological need satisfaction (autonomy, competence, and relatedness), intrinsic…