Board 123: What Can We Learn from a Research Experiences for Teachers (RET) Site? Three Perspectives on Big Data and Data Science

Stephanie Boggess Philipp; Jason Immekus

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Conference: 2019 ASEE Annual Conference & Exposition
Location: Tampa, Florida
Publication Date: June 15, 2019
Start Date: June 15, 2019
End Date: June 19, 2019
Conference Session: NSF Grantees Poster Session
Tagged Topic: NSF Grantees Poster Session
Page Count: 10
DOI: 10.18260/1-2--32217
Permanent URL: https://peer.asee.org/32217
Download Count: 372

Paper Authors

biography

Stephanie Boggess Philipp University of Louisville orcid.org/0000-0002-3215-3680

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Dr. Philipp is an assistant clinical professor in the Department of Middle and Secondary Education at the University of Louisville. She spent eight years as a project manager for various environmental and geophysical exploration firms and then as many years as a middle and secondary science teacher in chemistry and physics. She is a liaison between the Center for Research in Mathematics and Science Teacher Development and the Center for Teaching and Learning in Engineering at the University of Louisville. Her research includes studying changes in science and engineering teacher practice, best practices in teacher professional learning experiences, teacher and student learning in mathematical and computational thinking, and the use of undergraduate learning assistants in introductory STEM coursework.
Address: Department of Middle and Secondary Education, Porter Building, University of Louisville, Louisville, Kentucky 40292 Phone: 502.852.3948 Email: sbphil02@louisville.edu

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Jason Immekus University of Louisville

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Abstract

This paper will share initial findings from the first year of a Research Experience for Teachers site, supporting nine secondary STEM teachers from diverse schools in six-week university research projects in Big Data and Data Science. We have examined the perspectives on learning of three key site groups: the computer scientist principal investigator, the secondary STEM teachers participating in the RET, and the graduate research assistants who mentored the teachers in original research projects. The research projects chosen by the teachers focused on socially impactful data science. Teachers translated their research experience into curriculum incorporating the engineering practice of mathematical and computational thinking and described the lessons they learned from the research process through focus group interviews, seminar presentations, and lesson plans. The computer scientist leading the RET kept a journal of her learning experiences and reflections during the preparation for and implementation of the first summer cohort of the RET site. The graduate research assistants shared their learning experiences in working with middle and high school teachers during semi-structured interviews with an education researcher. We analyzed these qualitative data sets using a case study method, with the phenomenological case being the RET site and the subunits of the case defined as the three groups participating in the RET: the computer science researcher, the secondary STEM teachers, and the graduate student mentors. The case study analyzed the interactions and relationships between the three groups in the RET and changes in perspectives about teaching computational thinking by the computer scientist, the teachers, and the graduate research assistants.

Preliminary finding indicate that the computer scientist learned about the different ways that secondary teachers think about data science content and teaching. She learned that teachers are also expert learners and have developed particular methods of learning that are effective for them. The teachers learned that the research process is not linear, but is recursive and nonlinear. They came to understand that computational thinking does not necessarily involve computers, but is a way of thinking that looks to first solve a problem, then code it. Based on their research experience, they also learned that lessons using computational thinking are best developed as experiences that solicit student input and interest. The graduate research assistants learned that by working with the secondary teachers, who were novice computer science learners, they had to break down the content for teacher understanding. That work forced them to reflect on their own knowledge at a deeper level, and they feel much more comfortable explaining their work to non-engineers, a skill they anticipated needing as they enter the engineering workforce after graduation.

Citation
Format

Philipp, S. B., & Immekus, J. (2019, June), Board 123: What Can We Learn from a Research Experiences for Teachers (RET) Site? Three Perspectives on Big Data and Data Science Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32217

TY  - CPAPER
AB  - This paper will share initial findings from the first year of a Research Experience for Teachers site, supporting nine secondary STEM teachers from diverse schools in six-week university research projects in Big Data and Data Science. We have examined the perspectives on learning of three key site groups: the computer scientist principal investigator, the secondary STEM teachers participating in the RET, and the graduate research assistants who mentored the teachers in original research projects. The research projects chosen by the teachers focused on socially impactful data science. Teachers translated their research experience into curriculum incorporating the engineering practice of mathematical and computational thinking and described the lessons they learned from the research process through focus group interviews, seminar presentations, and lesson plans. The computer scientist leading the RET kept a journal of her learning experiences and reflections during the preparation for and implementation of the first summer cohort of the RET site. The graduate research assistants shared their learning experiences in working with middle and high school teachers during semi-structured interviews with an education researcher. We analyzed these qualitative data sets using a case study method, with the phenomenological case being the RET site and the subunits of the case defined as the three groups participating in the RET: the computer science researcher, the secondary STEM teachers, and the graduate student mentors. The case study analyzed the interactions and relationships between the three groups in the RET and changes in perspectives about teaching computational thinking by the computer scientist, the teachers, and the graduate research assistants.

Preliminary finding indicate that the computer scientist learned about the different ways that secondary teachers think about data science content and teaching. She learned that teachers are also expert learners and have developed particular methods of learning that are effective for them. The teachers learned that the research process is not linear, but is recursive and nonlinear. They came to understand that computational thinking does not necessarily involve computers, but is a way of thinking that looks to first solve a problem, then code it. Based on their research experience, they also learned that lessons using computational thinking are best developed as experiences that solicit student input and interest. The graduate research assistants learned that by working with the secondary teachers, who were novice computer science learners, they had to break down the content for teacher understanding. That work forced them to reflect on their own knowledge at a deeper level, and they feel much more comfortable explaining their work to non-engineers, a skill they anticipated needing as they enter the engineering workforce after graduation. 
AU  - Stephanie Boggess Philipp
AU  - Jason Immekus
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Board 123: What Can We Learn from a Research Experiences for Teachers (RET) Site? Three Perspectives on Big Data and Data Science
UR  - https://peer.asee.org/32217
DO  - 10.18260/1-2--32217
ER  -