Brendan McLoughlin, Sambridhi Bhandari, Erin Henrick, Erin Hotchkiss, Manoj Jha, Steven Jiang, Emily Kern, Landon Marston, Christopher Vanags, Caitlin Snyder, others
2022 ASEE Annual Conference & Exposition 2022
With increasing data-driven workplaces, educators face challenges in integrating data science into already-full curricula across diverse disciplines. This work is significant because it provides a structured, modular approach that can be adapted across institutions and disciplines while maintaining consistency in learning objectives and assessment practices. The modularity enables instructors to integrate data science without completely restructuring their existing courses.
This paper presents a systematic approach for integrating data science concepts into multiple undergraduate STEM+C courses across different disciplines. The authors developed twelve reusable instructional modules covering topics such as data collection, data quality, visualization, machine learning, and analysis methods. These modules were designed through a collaborative partnership and tested in courses at different universities and academic levels.
The study identified five core data science themes and analyzed how they were integrated differently across environmental science and engineering courses at different academic levels. Key findings showed that while core data science topics appear consistently, the depth and context of integration varies significantly by discipline, level, and institution. This analysis informs the development of widely-applicable guidelines for data science integration in STEM education.
@inproceedings{mcloughlin2022modular,
author = {McLoughlin, Brendan and Bhandari, Sambridhi and Henrick, Erin and Hotchkiss, Erin and Jha, Manoj and Jiang, Steven and Kern, Emily and Marston, Landon and Vanags, Christopher and Snyder, Caitlin and others},
booktitle = {2022 ASEE Annual Conference \& Exposition},
title = {A modular approach for integrating data science concepts into multiple undergraduate STEM+ C courses},
year = {2022},
month = {aug},
abstract = {With increasingly technology-driven workplaces and high data volumes, instructors across STEM+C disciplines are integrating more data science topics into their course learning objectives. However, instructors face significant challenges in integrating additional data science concepts into their already full course schedules. Streamlined instructional modules that are integrated with course content, and cover relevant data science topics, such as data collection, uncertainty in data, visualization, and analysis using statistical and machine learning methods can benefit instructors across multiple disciplines. As part of a cross-university research program, we designed a systematic structural approach–based on shared instructional and assessment principles–to construct modules that are tailored to meet the needs of multiple instructional disciplines, academic levels, and pedagogies. Adopting a research-practice partnership approach, we have collectively developed twelve modules working closely with instructors and their teaching assistants for six undergraduate courses. We identified and coded primary data science concepts in the modules into five common themes: 1) data acquisition, 2) data quality issues, 3) data use and visualization, 4) advanced machine learning techniques, and 5) miscellaneous topics that may be unique to a particular discipline (e.g., how to analyze data streams collected by a special sensor). These themes were further subdivided to make it easier for instructors to contextualize the data science concepts in discipline-specific work. In this paper, we present as a case study the design and analysis of four of the modules, primarily so we can compare and contrast pairs of similar courses that were taught at different levels or at different universities. Preliminary analyses show the wide distribution of data science topics that are common among a number of environmental science and engineering courses. We identified commonalities and differences in the integration of data science instruction (through modules) into these courses. This analysis informs the development of a set of key considerations for integrating data science concepts into a variety of STEM + C courses.},
contribution = {minor},
url = {https://peer.asee.org/a-modular-approach-for-integrating-data-science-concepts-into-multiple-undergraduate-stem-c-courses},
keywords = {data science education, STEM curriculum integration, instructional modules, undergraduate education},
month_numeric = {8}
}