In studies over recent years, there has been an increasing interest in teachers' predicting middle school students' thinking processes. However, as far as we are aware, there are no studies examining students' thinking in terms of mathematical thinking components. This study primarily aimed to determine the mathematical thinking of middle school…

Morphological analysis is a form of problem solving to work out the meanings of unfamiliar words by applying knowledge of morphemes. It has emerged recently as an important predictor of reading comprehension. However, while gamification can potentially be used to teach this skill, few studies have examined its use. To address this, a…

The National Assessment of Educational Progress (NAEP) program only reports state-level subgroup results if it samples at least 62 students identifying with the subgroup. Since some subgroups constitute small proportions of many states' general student populations, these minority subgroups are seldom sufficiently sampled to meet this sample size…

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 2)…

To test the hypothesis that a higher tendency to "s"pontaneously "f"ocus "o"n "m"ultiplicative "r"elations (SFOR) leads to improvements in rational number knowledge via more exact estimation of fractional quantities, we presented sixth graders (n = 112) with fraction number line estimations and a…

The gold-standard for evaluating the effect of an educational intervention on student outcomes is running a randomized controlled trial (RCT). However, RCTs may often be small due to logistical considerations, and resulting treatment effect estimates may lack precision. Recent methods improve experimental precision by incorporating information…

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…

Introduction: Prior research consistently provides evidence supporting potential relationships between epistemological beliefs and learning. The current study examines the relationship between epistemological beliefs, academic motivation, and self-regulated learning strategies among different ethnic groups of American adolescents. Method: This…

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…

This study investigated risk factors for school absenteeism in a sample of 983 children in elementary and lower secondary schools in Denmark, using administrative data on absenteeism measured in the year following risk factor measurement. Risk factors were measured by survey (children and teachers) and register data. Two methods of determining…

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…

Self-esteem has always been a hot research object in the field of adolescent mental health. But in longitudinal research, using a single slope to describe the trajectory of adolescent self-esteem is unrealistic. The piecewise growth mixture model (PGMM) was used to fit the data in this study. Selecting from China Family Panel Studies database, a…

Pandemic-related policies are typically developed by districts and translated to all schools for implementation. Understanding the degree to which the pandemic impacted school-level performance would provide additional perspective for researchers looking to help district and school officials move forward. The main purpose of this study is to…

We examined how emotional and communication functioning at kindergarten predicted the academic trajectories of refugee children. Drawing from a population-based Canadian cohort, the study followed 629 refugee children from age 5 to 13 and (i) modeled kindergarten, Grade 4, and Grade 7 academic trajectories via group-based trajectory modeling and…

Black and Latinx youth are situated in a maladaptive discriminatory context in the United States; however, prosociality may be one way that youth can promote their own positive development in the face of these experiences. We examined the longitudinal associations between discrimination and prosociality among 380 Black and Latinx early adolescents…