Session: DEC-03-01 Innovative Practices in Design Education (Other Topics)

Paper Number: 70250

Start Time: August 18, 11:10 AM

70250 - Assessment of Student Learning Through Reflection on Doing in Engineering Design 

How can instructors leverage assessment instruments in design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve courses?

A Take-away is one type assessment method. It is unstructured text written by a student AME4163 Course: Principles of Engineering Design, the University of Oklahoma, Norman, US to record what they understand by reflecting on authentic immersive experiences throughout the semester. The immersive experiences include lectures, assignments, reviews, building, testing and a post-analysis for design of an electro-mechanical system to address a given customer need. In the context of a Take-away a student then writes a Learning Statement. The Learning Statement is a single sentence written as a triple, i.e., Experience|Learning|Value. Over the past three years at the University of Oklahoma (OU), we have collected about 18,000 Take-aways and 18,000 Learning Statements from almost 400 students. In our earlier papers, we primarily concentrate on analyzing students’ Learning Statements by a text mining framework.

In this paper, we focus on analyzing students’ Take-aways data using a Latent Dirichlet Allocation (LDA) algorithm, and then relate the Take-aways data to instructor’s expectations using text similarity.  By connecting and comparing what students learned (embodied in Take-aways) and what instructors expected the students to learn (embodied in the course booklet), we provide evidence-based guidance to instructors on how to improve the delivery of AME4163: Principles of Engineering Design. The proposed method can be generalized to be used for the assessment of ABET Student Outcomes 2 and 7.

Presenting Author: Yanwei Sun Beijing Institute of Technology

Authors:

Yanwei Sun Beijing Institute of Technology
Shan Peng University of Oklahoma
Zachary Ball Carnegie Mellon University
Zhenjun Ming Beijing Institute of Technology
Janet K. Allen University of Oklahoma
Farrokh Mistree University of Oklahoma