Three paradigms in developing students' statistical reasoning

This article is a reflection on more-than-a-decade research in the area of statistics education in upper primary school (grades 4-6, 10-12 years old). The goal of these studies was to better understand young students’ statistical reasoning as they were involved in authentic data investigations and simulations in a technology-enhanced learning environment entitled Connections. The article describes three main paradigms that guided our educational and academic efforts: EDA, ISI, and Modeling. The first, EDA, refers to Exploratory Data Analysis – children investigate sample data they collected without making explicit inferences to a larger population. The second, ISI, refers to Informal Statistical Inference – children make inferences informally about a larger population than the sample they have at hand. The third, Modeling-children use computerized tools to model the phenomenon they study, and simulate many random samples from that model to study statistical ideas. In each of these three paradigms, we provide a short rationale, an example of students’ products, and learned lessons. To conclude, current challenges in statistics education are discussed in light of these paradigms

Statistical reasoning learning environment

This article describes a model for an interactive inquiry-based statistics learning environment that is designed to develop students’ statistical reasoning. This model is called a “Statistical Reasoning Learning Environment” (SRLE) and is built on the socio-constructivist theory of learning and teaching. This model is based on six principles of instructional design: fundamental statistical ideas, motivating real data sets, inquiry- and data-based classroom activities, innovative technological tools, classroom norms, and alternative assessment. Two examples of SRLEs are briefly discussed

Ariadne’s thread, daedalus’ wings and the learners autonomy

Il existe une tension apparente entre l’idée d’apprenant autonome, promue par les tenants des classes « participatives », et l’argument participatif selon lequel l’apprentissage est un processus intrinsèquement collectif d’induction vers des formes d’action historiquement établies. Dans cet article, nous essayons de comprendre la nature de cette tension et ses conséquences pour les pratiques d’éducation. Nous commençons par une brève présentation de la perspective commognitive sur l’apprentissage, qu’on peut considérer comme une version particulière de l’approche participative, selon laquelle la pensée est une forme individualisée de la communication interpersonnelle et l’apprentissage scolaire un processus de modification et d’extension de ce discours. Nous introduisons alors la distinction entre apprentissage niveau-objet et apprentissage niveau-meta, ce dernier nécessitant de suivre ceux-qui-savent plutôt que de se limiter à conduire ses propres explorations inventives. Nous soutenons qu’un certain taux de compréhension et d’accords mutuels est nécessaire pour que l’apprentissage méta soit efficace. Quelques exemples illustratifs, pris en classes de mathématiques et de statistiques, montrent un apprentissage qui survient alors que l’accord est respecté, et d’autres ce qui se passent lorsque certains éléments de cet accord sont violés. Nous concluons avec une mise en garde contre une interprétation rapide et unidimensionnelle du principe de l’autonomie de l’apprenant

Innovating Pedagogy 2017: Exploring new forms of teaching, learning and assessment, to guide educators and policy makers. Open University Innovation Report 6

This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This sixth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE). Themes: • Big-data inquiry: thinking with data • Learners making science • Navigating post-truth societies • Immersive learning • Learning with internal values • Student-led analytics • Intergroup empathy • Humanistic knowledge-building communities • Open Textbooks • Spaced Learnin