Data science : a gateway to belonging in STEM and other quantitative fields
Abstract/Contents
- Abstract
- The content of secondary mathematics leaves much to be desired in terms of student interest, but also in terms of its representativeness of real world mathematics, leaving students with only a limited glimpse of how math is applied in the world and perhaps prematurely deciding that the majors and careers that mathematics leads into are not of interest to them. Contrarily, data science is inherently real world, because the data itself comes from real contexts with authentic problems to solve. For example, students can explore how machine learning functions in music recommendation systems, or how data can be used to investigate skin tone representation in social media advertisements. In addition, data science can be learned and explored in contexts that are personally interesting and valuable to students. For these reasons, data science has the potential to capture the interest and engagement of mathematics students who are motivated to learn content that they find interesting and useful in ways that other mathematics coursework cannot. This study looks at the ways in which students' experience in a high school data science course served to increase their interest in pursuing majors and careers in STEM.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | LaMar, Tanya Mae |
|---|---|
| Degree supervisor | Boaler, Jo, 1964- |
| Thesis advisor | Boaler, Jo, 1964- |
| Thesis advisor | Langer-Osuna, Jennifer |
| Thesis advisor | Williamson, Peter, 1968- |
| Degree committee member | Langer-Osuna, Jennifer |
| Degree committee member | Williamson, Peter, 1968- |
| Associated with | Stanford University, Graduate School of Education |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Tanya LaMar. |
|---|---|
| Note | Submitted to the Graduate School of Education. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/wp035xy4875 |
Access conditions
- Copyright
- © 2023 by Tanya Mae LaMar
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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