No abstract

In the course of a project 1 to create physics education materials for secondary schools in the USA (Erickson and Cooley, 2004) we have, not surprisingly, had insights into how students develop certain mathematical understandings. Some of these translate directly into the mathematics classroom. With our materials, students get data from a variety of sources, data that arise in realworld phenomena. Students find functions to model the data; these appear as curves on scatter plots. Through this, students learn about the phenomena, and also about the mathematics behind the functions. This is neither complicated nor particularly new (see, e.g., Hestenes 1987; Wells et al. 1995). Thus this paper focuses on some details. How do students model the data? In what ways do students find these tasks challenging? And why don't we already do this more commonly? The observations we make in this paper are conjectures based on observations of students in classrooms in the San Francisco, USA area; documenting them more thoroughly is the object of ongoing research. In this paper, we will discuss how students do modeling, describe the search for meaning in the parameters (for example, the meaning of slope), and describe some of the problems students seem to have coordinating the data and the models.

This paper describes a short module [https://concord.org/awash-in-data] for introducing data science to senior school students or other data‐science beginners. The design focuses on “data moves.” Students use CODAP [https://codap.concord.org] to do their work.

SummaryUnderstanding a Bayesian perspective demands comfort with conditional probability and with probabilities that appear to change as we acquire additional information. This paper suggests a simple context in conditional probability that helps develop the understanding students would need for a successful introduction to Bayesian reasoning.

Figure 1: Story Builder allows students to organize scatter plots, data maps, web pages, images, text, and other media into narratives that describe how their analysis and findings unfold over "moments" in time.