Faculty Projects
Modernizing Statistics Education and Experience in Industrial Engineering

Moses Chan headshot

Faculty

Moses Chan, IEMS

Amount Requested

$10,000

Summary

The recent introduction of the first-year engineering probability and statistics course (GEN_ENG 231) has opened up room for fundamentally reimagining engineering statistics (IEMS 303) to meet the evolving demand of modern methods. This project proposes a course redesign from a traditional introductory statistics course into a modern, project-based experience focused on experimentation, uncertainty quantification, and statistical decision-making.

The new course will be structured around an experiential learning framework centered on engineering experiments, a computational statistics lab, and team-based projects. Students will learn how to design experiments, oversee data collection and analysis, quantify uncertainty and risk, and make decisions under uncertainty using modern statistical approaches that represent both classical and Bayesian methodology.

This project will shape IEMS 303 into an advanced statistics course that bridges foundational concepts (in GEN_ENG 231, IEMS 302) and advanced coursework in specific topics: statistical and machine learning, and experimental design. The redesigned course will serve all IEMS students and is expected to fulfill a second mathematical requirement in departments such as applied mathematics, mechanical engineering, and others, establishing a distinctive statistical experience that emphasizes hands-on learning, computation, and engineering decision-making.

Planned Activities/Investments

The project supports the design and implementation of the new IEMS 303 with its first expected offering in Spring 2027.

Major activities include:

  • Development of a team-based engineering experiment project.
  • Curation of textbook and references for the development of course content.
  • Creation of computational labs covering resampling methods, bootstrap inference, Bayesian inference, model comparison, and uncertainty quantification.
  • Development of engineering case studies that connect statistical methods to real engineering decisions.
  • Creation of reusable datasets, software templates, and computational materials.
  • Development of assessment rubrics for homework, lab, and project.
  • Coordination with IEMS 304 (Statistical Learning) and IEMS 307 (Design of Experiments) to establish a coherent statistics pathway.

Impact

The redesigned IEMS 303 will impact all IEMS students. This course will provide an advanced statistics elective option relevant to majors that benefit from a strong foundation in uncertainty quantification, for example, applied mathematics and mechanical engineering, where digital twin technology and simulation-based analyses are at the forefront.

The impact will be measured through faculty observations of selected classes, performance on course learning objectives, student feedback during the redesigned course, and assessment of student preparedness in downstream statistical and capstone classes.

Deliverables

The major deliverables include:
  • A fully redesigned IEMS 303 curriculum
  • A set of 7 - 8 computational labs.
  • A repository of engineering case studies and real datasets.
  • The design framework of a quarter-long experimental project.
  • A set of reusable instructional materials and open source software.
  • A set of assessment instruments including rubrics, survey, homework, and exams.

Sustainability

If funded, the Murphy Award will support the initial development of course materials and the infrastructure of the experiential learning project. Once developed, the course materials will undergo incremental improvements and become the regular IEMS 303 materials.

IEMS 303 is expected to continue as a core course in IEMS, and has traditionally served many students outside of the department. With the front-loaded effort in redesign, the course will be well-positioned to serve the foreseeable cohorts of engineering students.

Budget Overview

  • Undergraduate assistant(s): $5,500 — To assist development, testing, and documentation of course content (labs, homework, datasets, and project materials).
  • Educational and experimental materials: $1,500 — To support initial curation of reference materials and development of experimental project.
  • Software and computation: $2,000 — Computational resources used for development and testing of instructional materials.
  • Project presentation showcase: $1,000 — Support for presentation and dissemination of student project outcomes.

Total Budget Amount: $10,000

Matching Funds

Discretionary funds will also be used to support the development of this project.