GAISE II: Bringing Data into Classrooms

In the context of an afterschool program in which students explore relatively large authentic datasets, we investigated how 11-to 14-year old students worked with categorical variables. During the program, students learned to use the Common Online Data Analysis Platform (CODAP), a statistical analysis platform specifically designed for middle and high school students, to create and interpret graphs. Following the program, we conducted individual clinical interviews, during which students used CODAP to answer questions about relationships between variables.Here, we describe how students engaged in exploratory data analysis that involved looking at relationships between two categorical variables. Students worked from data in table form and created "contingency graphs," a variant of contingency tables, which they used to analyze and draw insights from the data. Our research identified four strategies that students used to examine the data in order to explore patterns, make comparisons, and answer questions with the data.

Section: Literature Reviewmentioning

confidence: 99%

Students' approaches to exploring relationships between categorical variables

Higgins

Mokros²,

Rubin

et al. 2023

“…The link between statistics, mathematics, and computer science has been established in the field of data science, with CT supporting data processes [20]. STEM professionals rely on statistical and CT when exploring natural phenomena through mathematical modelling; and in an attempt to prepare students for future STEM careers, educational policy initiatives and curriculum involving CT, in different subject areas, are increasing around the globe [eg, 4,7,8,12–14,16]. Statistics teaching and learning in classrooms, structured around investigations, may also support student development of CT processes when students work to identify and define problems (decomposition), plan and collect data in order to generate information about an issue (data collection, pattern recognition, and abstraction), that is then communicated clearly and succinctly (generalization and evaluation) [15,30].…”

Section: Introductionmentioning

confidence: 99%

“…In your car, while finding the quickest route to work, digital maps can be manipulated to focus on routes that ignore the specific details of nearby houses and shops, while highlighting traffic lights and tolls in order to minimize traffic congestion. In situations involving statistical thinking, a problem solver must understand the context of variables, in relation to statistical investigative questions, and may select certain variables to focus on while ignoring others, to explore associations between them [4].…”

Section: Introductionmentioning

confidence: 99%

M in CoMputational thinking: How long does it take to read a book?

Fry

Makar

Hillman

2023

Even at the primary level, computational thinking (CT) can support young students to prepare for participating in futures that are immersed in data.In mathematics classrooms, there are few explanations of the ways CT can support students in formulating and solving complex problems. This paper presents an example of a primary classroom investigation (8-9 year olds) over seven lessons of the problem "How long does it take to read a book?" The aim is to illustrate ways a statistical investigation can provide context for CT and demonstrate how the two complement each other to solve problems involving mathematics. The findings highlight opportunities and challenges that students face across the elements of CT-decomposition, abstraction, pattern recognition and modelling, and generalization and algorithmic thinking, including recommendations for teaching.

“…The participant had received no formal instruction in data science, as verified by reviewing the school's mathematics curriculum for his grade. Although recent developments in statistics education [2] have strongly advocated for data science and exploratory data analysis in the early years, this school gives a major role to the arithmetic component of the mathematics curriculum and a minor role to conventional visual representations of data.…”

Section: Introductionmentioning

confidence: 99%

“…Data science has been understood as "the science of learning from data" [7], as an advanced form of exploratory data analysis [6] and as the process of extracting knowledge from data [17]. Today, data science is not only a requirement in a data-driven society but also an explicit recommendation for the curriculum [2]. However, teaching continues to privilege the study of traditional and prefabricated datasets that turn their backs on the current needs of a citizen living in a society in which data abounds [10,15].…”

Section: Introductionmentioning

confidence: 99%

The possibilities of exploring nontraditional datasets with young children

Zapata‐Cardona

2023

Today's world is characterized by the extensive production of data in different scenarios that everyday citizens need to understand for their informed participation in society. With the increase in the availability of data in a society defined by the industrious production of data, the educational system needs to think of possibilities to bring young children closer to the world of data science. This paper presents a nontraditional data exploration experience with an 8‐year‐old participant helped by a data visualization tool. A task‐based interview was conducted while the participant explored a carbon dioxide emission dataset. This paper studied how the participant interrogates the data, draws inferences and exhibits dispositions. At the end, some reflections are presented when introducing the exploration of nontraditional data in teaching.