This paper argues that engaging students in informal statistical reasoning from early school years is essential for the development of statistical understanding. We investigated if and how children aged six-seven years old identified variation in a table of data and made predictions through the design of a teaching experiment. The classroom…

Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on…

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…

Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an…

Adolescents with attention-deficit/hyperactivity disorder (ADHD) are at heightened risk of experiencing academic difficulties due to organizational deficits. Impairment can be exacerbated through poor sleep hygiene and excessive daytime sleepiness, prevalent sleep challenges for adolescents with ADHD. Given established relationships among sleep,…

This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text-based self-evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information…

The present investigation examined the role of teachers' instructional support (student reports of relevance, organization and clarity, feedback-feedforward) in predicting students' growth goal setting and, in turn, the roles of instructional support and growth goal setting in predicting students' academic engagement (perseverance, aspirations,…

Prediction of student performance is one of the most important subjects of educational data mining. Artificial neural networks are seen to be an effective tool in predicting student performance in e-learning environments. In the studies carried out with artificial neural networks, performance predictions based on student scores are generally made,…

Prior research shows that when parents monitor, check, and assist in completing homework without an invitation, their children's motivation and academic achievement often decline. We propose that intrusive support from parents might also send the message that children are incompetent, especially if they believe their intelligence is fixed. We…

The use of machine learning and data mining techniques across many disciplines has exploded in recent years with the field of educational data mining growing significantly in the past 15 years. In this study, random forest and logistic regression models were used to construct early warning models of student success in introductory calculus-based…

In higher education, many studies have tried to establish which student activities predict achievement in blended courses, with the aim of optimizing course design. In this paper, we examine whether taking into account temporal patterns of student activity and instructional conditions of a course help to explain course performance. A course with a…

The present meta-analysis assessed concurrent and longitudinal associations between parental educational expectations and child achievement, and factors that mediate the effect of expectations on achievement. A systematic search in electronic databases identified 169 studies that were included in a random-effects meta-analysis. We found small to…

Low-cost private schools are expanding across sub-Saharan Africa and are often perceived by parents to be of better quality than public schools. This article assesses the interplay between kindergarten (or preschool) choice, household resources, and children's school readiness in Ghana. We examine how child, household, and school characteristics…

The purpose is to improve insights and educational results by applying analytic methods. The focus is on the mathematics applied to learn from the kind of data available to most classes such as final examination marks or homework grades. The sample is 249 students learning introductory college statistics. The result is a predictive model for…

The Predict-Observe-Explain (POE) learning cycle improves understanding of the connection between empirical results and theoretical concepts when students engage in hands-on experimentation. This study explored whether training students to use a POE strategy when learning from social science texts that describe theories and experimental results…