This report focuses on the ways in which 36 Grade 4 students recognized, explained, described, and employed variation during interviews conducted 1 month after participating in STEM-based activities in which they tested, adjusted, and re-tested catapults. An inductive thematic methodology was used for analysis of the interview transcripts to…

Background: Wearable technologies offer new opportunities to address in-school physical inactivity. However, children are often excluded from discussing issues which directly impact them, including the use of wearable technologies in a school setting. Thus, the aim of this study is to understand primary school pupils' experiences and perceptions…

The purpose of this study was to analyze the activities in the Problem-Based Learning (PBL) methodology among high school students when solving problems related to the field of plane figures, both in extra-mathematical and intra-mathematical environments. The research was qualitative and constituted a case study. The technique used to select the…

The teaching and learning of statistical thinking begins at a young age in Australia, with a focus on data representation and interpretation from Foundation Year (age 5), and the collection, sorting and categorising of items from the natural environment starting even earlier. The intangible concept of "data," as part of statistical…

National testing and reform agendas, with their focus on school improvement, has led to increased collection and scrutiny of student data. The analysis of these data usually occurs at a school level, often by school leaders. What is less common is the opportunity for students to scrutinise their individual data and take ownership over the results…

The need for data literacy is an increasingly pressing priority in society, but most of the work in data-centred education has focused on developing skills at the middle school, secondary, and post-secondary levels, with little attention on the potential for engaging elementary-aged students in reasoning with and about data. This paper reports…

Adaptive Learning Technologies (ALT) and Learning Analytics (LA) are expected to contribute to the customisation and personalisation of pupil learning by continually calibrating and adjusting pupils' learning activities towards their skill and competence levels. The overall aim of the study presented in this paper was to obtain a comprehensive…

We explore 11-12-year-old students' emerging ideas of models and modelling as they engage in a data-modelling task involving inquiry based on data obtained from an experiment. We report on a design-based study in which students identified what and how to measure, decided how to structure and represent data, and made inferences and predictions…

Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…

The purpose of the study is to analyze the affective traits that affect mathematics achievement through Structural Equation Modeling (SEM) as a traditional regression model and Multivariate Adaptive Regression Splines (MARS), as one of the data mining methods. Structural Equation Modeling, one of the regression-based methods, is quite popular for…

The purpose of this study was to investigate the effect of a heart rate measurement system on student motivation and parent satisfaction of the information obtained. Participants (n=27) either took a survey as a parent/guardian (n=11) or were interviewed as a student (n=16). The survey was based on a qualitative measure and used to determine the…

While aggregate reasoning is a core aspect of statistical reasoning, its development is a key challenge in statistics education. In this study we examine how students' aggregate reasoning with samples and sampling (ARWSS) can emerge in the context of statistical modeling activities of real phenomena. We present a case study on the emergent ARWSS…

Given growing interest in K-12 data and data science education, new approaches are needed to help students develop robust understandings of and familiarity with data. The model of the "quantified self"--in which data about one's own activities are collected and made into objects of study--provides inspiration for one such approach. By…

The purpose of this paper is to assess the validity of multiple-choice questions in measuring fourth grade students' ability to interpret graphs related to physical science topics such as motion and temperature. We administered a test including 6 multiple-choice questions to 28 fourth grade students. Students were asked to explain their thinking…

This study uses information about in-game strategy use, identified through cluster analysis of actions in an educational video game, to make data-driven modifications to the game in order to reduce construct-irrelevant behavior. The examination of student strategies identified through cluster analysis indicated that (a) it was common for students…