Integrating Data Science Into Undergraduate Science and Engineering Courses: Lessons Learned by Instructors in a Multiuniversity Research-Practice Partnership
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| Title: | Integrating Data Science Into Undergraduate Science and Engineering Courses: Lessons Learned by Instructors in a Multiuniversity Research-Practice Partnership |
|---|---|
| Language: | English |
| Authors: | Md. Yunus Naseri (ORCID |
| Source: | IEEE Transactions on Education. 2025 68(1):1-12. |
| Availability: | Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=13 |
| Peer Reviewed: | Y |
| Page Count: | 12 |
| Publication Date: | 2025 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 1915538 1915487 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Data Science, Courses, Research and Development, Theory Practice Relationship, Universities, Teacher Attitudes, Undergraduate Study, Science Curriculum, Engineering Education, Learning Modules, Faculty, Curriculum Enrichment |
| DOI: | 10.1109/TE.2024.3436041 |
| ISSN: | 0018-9359 1557-9638 |
| Abstract: | Contribution: This article discusses a research-practice partnership (RPP) where instructors from six undergraduate courses in three universities developed data science modules tailored to the needs of their respective disciplines, academic levels, and pedagogies. Background: STEM disciplines at universities are incorporating data science topics to meet employer demands for data science-savvy graduates. Integrating these topics into regular course materials can benefit students and instructors. However, instructors encounter challenges in integrating data science instruction into their course schedules. Research Questions: How did instructors from multiple engineering and science disciplines working in an RPP integrate data science into their undergraduate courses? Methodology: A multiple case study approach, with each course as a unit of analysis, was used to identify data science topics and integration approaches. Findings: Instructors designed their modules to meet specific course needs, utilizing them as primary or supplementary learning tools based on their course structure and pedagogy. They selected a subset of discipline-agnostic data science topics, such as generating and interpreting visualizations and conducting basic statistical analyses. Although instructors faced challenges due to varying data science skills of their students, they valued the control they had in integrating data science content into their courses. They were uncertain about whether the modules could be adopted for use by other instructors, specifically by those outside of their discipline, but they all believed the approach for developing and integrating data science could be adapted to student needs in different situations. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1460207 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1460207 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrating Data Science Into Undergraduate Science and Engineering Courses: Lessons Learned by Instructors in a Multiuniversity Research-Practice Partnership – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Md%2E+Yunus+Naseri%22">Md. Yunus Naseri</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4537-6509">0000-0002-4537-6509</externalLink>)<br /><searchLink fieldCode="AR" term="%22Caitlin+Snyder%22">Caitlin Snyder</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3341-0490">0000-0002-3341-0490</externalLink>)<br /><searchLink fieldCode="AR" term="%22Katherine+X%2E+Perez-Rivera%22">Katherine X. Perez-Rivera</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-3071-9716">0009-0001-3071-9716</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sambridhi+Bhandari%22">Sambridhi Bhandari</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0009-9171-2866">0009-0009-9171-2866</externalLink>)<br /><searchLink fieldCode="AR" term="%22Habtamu+Alemu+Workneh%22">Habtamu Alemu Workneh</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0005-5160-6111">0009-0005-5160-6111</externalLink>)<br /><searchLink fieldCode="AR" term="%22Niroj+Aryal%22">Niroj Aryal</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2285-1060">0000-0003-2285-1060</externalLink>)<br /><searchLink fieldCode="AR" term="%22Gautam+Biswas%22">Gautam Biswas</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2752-3878">0000-0002-2752-3878</externalLink>)<br /><searchLink fieldCode="AR" term="%22Erin+C%2E+Henrick%22">Erin C. 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Lohani</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4010-5862">0000-0003-4010-5862</externalLink>)<br /><searchLink fieldCode="AR" term="%22Landon+T%2E+Marston%22">Landon T. Marston</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9116-1691">0000-0001-9116-1691</externalLink>)<br /><searchLink fieldCode="AR" term="%22Christopher+P%2E+Vanags%22">Christopher P. Vanags</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2200-745X">0000-0003-2200-745X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kang+Xia%22">Kang Xia</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22IEEE+Transactions+on+Education%22"><i>IEEE Transactions on Education</i></searchLink>. 2025 68(1):1-12. – Name: Avail Label: Availability Group: Avail Data: Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. 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Background: STEM disciplines at universities are incorporating data science topics to meet employer demands for data science-savvy graduates. Integrating these topics into regular course materials can benefit students and instructors. However, instructors encounter challenges in integrating data science instruction into their course schedules. Research Questions: How did instructors from multiple engineering and science disciplines working in an RPP integrate data science into their undergraduate courses? Methodology: A multiple case study approach, with each course as a unit of analysis, was used to identify data science topics and integration approaches. Findings: Instructors designed their modules to meet specific course needs, utilizing them as primary or supplementary learning tools based on their course structure and pedagogy. They selected a subset of discipline-agnostic data science topics, such as generating and interpreting visualizations and conducting basic statistical analyses. Although instructors faced challenges due to varying data science skills of their students, they valued the control they had in integrating data science content into their courses. They were uncertain about whether the modules could be adopted for use by other instructors, specifically by those outside of their discipline, but they all believed the approach for developing and integrating data science could be adapted to student needs in different situations. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2025 – Name: AN Label: Accession Number Group: ID Data: EJ1460207 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TE.2024.3436041 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Data Science Type: general – SubjectFull: Courses Type: general – SubjectFull: Research and Development Type: general – SubjectFull: Theory Practice Relationship Type: general – SubjectFull: Universities Type: general – SubjectFull: Teacher Attitudes Type: general – SubjectFull: Undergraduate Study Type: general – SubjectFull: Science Curriculum Type: general – SubjectFull: Engineering Education Type: general – SubjectFull: Learning Modules Type: general – SubjectFull: Faculty Type: general – SubjectFull: Curriculum Enrichment Type: general Titles: – TitleFull: Integrating Data Science Into Undergraduate Science and Engineering Courses: Lessons Learned by Instructors in a Multiuniversity Research-Practice Partnership Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Md. Yunus Naseri – PersonEntity: Name: NameFull: Caitlin Snyder – PersonEntity: Name: NameFull: Katherine X. Perez-Rivera – PersonEntity: Name: NameFull: Sambridhi Bhandari – PersonEntity: Name: NameFull: Habtamu Alemu Workneh – PersonEntity: Name: NameFull: Niroj Aryal – PersonEntity: Name: NameFull: Gautam Biswas – PersonEntity: Name: NameFull: Erin C. Henrick – PersonEntity: Name: NameFull: Erin R. Hotchkiss – PersonEntity: Name: NameFull: Manoj K. Jha – PersonEntity: Name: NameFull: Steven Jiang – PersonEntity: Name: NameFull: Emily C. Kern – PersonEntity: Name: NameFull: Vinod K. Lohani – PersonEntity: Name: NameFull: Landon T. Marston – PersonEntity: Name: NameFull: Christopher P. Vanags – PersonEntity: Name: NameFull: Kang Xia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0018-9359 – Type: issn-electronic Value: 1557-9638 Numbering: – Type: volume Value: 68 – Type: issue Value: 1 Titles: – TitleFull: IEEE Transactions on Education Type: main |
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