Reasoning about data in elementary school
Student strategies and strengths when reasoning with multiple variables
Bibliographic Data
| ID | 21589589 |
|---|---|
| Authors | Jessica Sickler (0000-0002-0572-3377, J. Sickler Consulting, Pittsburgh, PA, USA, corresponding author), Michelle Lentzner (0000-0002-1866-9306, J. Sickler Consulting, Pittsburgh, PA, USA), Lynn T Goldsmith (0000-0002-9463-7701, Education Development Center, Waltham, MA, USA), Lauren Brase (0000-0002-8961-3449, American Geosciences Institute, Alexandria, VA, USA), Randall Kochevar (0000-0002-4252-9522, Education Development Center, Waltham, MA, USA) |
| Year | 2024 |
| Volume | 46 |
| Issue | 16 |
| Pages | 1736-1756 |
| Publication date | 2024-11-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | International Journal of Science Education, Part B (JOURNAL) |
| Journal identifiers | ISSN: 2154-8455 • E-ISSN: 2154-8463 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09500693.2023.2298214 |
| OpenAlex | W4391018040 |
| Language | EN |
| References cited | 31 |
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 findings from a foundational study to explore the natural strengths, skills, and strategies that upper elementary students bring to reasoning about data-centred problems. It was the first phase of a project that aimed to design and test activities to promote data literacy among upper elementary students. Clinical interviews with students in grades 3, 4, and 5 centred on a series of non-mathematical data ‘scenarios’ designed to elicit students’ reasoning about data without requiring them to manipulate or interpret tabular or graphical representations. The findings indicate that young students were able to reason about multivariate problems and were particularly adept at thinking critically about the data sources and evidence in the data. The findings indicate that students bring foundational strengths that can inform the development of curricular interventions, as well as stimulate further research into the early stages of students’ development of data literacy
Logical reasoning · Mathematics education · Pedagogy · Primary education · Qualitative reasoning · Science education · Scientific reasoning · Computer Science · Educational Assessment and Pedagogy · Mathematics Education and Teaching Techniques · Psychology · Statistics Education and Methodologies · Artificial Intelligence
A theory of cognitive development
Representing Student Argumentation as Functionally Emergent From Scientific Activity
Introducing Children to Modeling Variability
Ethics & Big Data
Learning what to change
Modeling Natural Variation Through Distribution
A General Inductive Approach for Analyzing Qualitative Evaluation Data
| Highly cited | No |
|---|