Larry J. Shuman

The future of engineering education

Thirteen engineering educators and researchers were each asked to choose a particular aspect of engineerings future to address. Each of the authors has contributed a short piece that has been edited into a discussion of the future as we collectively see it. Topics include the stimulating change, the changing university, teaching, learning, research, outcome assessment and technology as well as a look back at predictions for 2000.

Freshman engineers' performance when solving design problems

Among the major changes that undergraduate engineering curricula are undergoing is the incorporation of design into freshman curricula. While the objective is to teach design so that students learn both engineering processes and content knowledge, the open question remains: to what extent can freshman engineering students learn to do design? To date, few studies have actually assessed acquisition of design skills. This paper presents the results of one such study in which freshman engineering students were asked to solve three short design problems. Half of the subjects solved the problems at the beginning of the first semester, freshman year and the other half solved the problems at the beginning of the second semester. Results indicate that after only one semester of engineering, students show more sophistication in their design processes. However, while the quality of their problem solving approach improved, comparable improvements were not found in the quality of their designs.

Computing confidence intervals for stochastic simulation using neural network metamodels

This paper discusses the use of supervised neural networks as a metamodeling technique for discrete-event, stochastic simulation. An (s, S) inventory simulation from the literature is translated into a metamodel through development of parallel neural networks, one estimating expected total cost and one estimating variance of expected total cost. These neural network estimates are used to form confidence intervals, which are compared for coverage to those formed directly by simulation. It is shown that the neural network metamodel is quite competitive in accuracy when compared to the simulation itself and, once trained, can operate in nearly real-time. A comparison of metamodel performance under interpolative versus extrapolative predictions is made.

Estimating Need and Demand for Prehospital Care

Models estimating demand and need for emergency transportation services are developed. These models can provide reliable estimates which can be used for planning purposes, by complementing and/or substituting for historical data. The model estimating demand requires only four independent variables: population in the area, employment in the area, and two indicators of socioeconomic status which can be obtained from census data. The model can be used to estimate demand according to 4 operational categories and 11 clinical categories. The parameters of the model are calibrated with 1979 data from 82 ambulance services covering over 200 minor civil divisions in Southwestern Pennsylvania. This model was tested with data from another 55 minor civil divisions, also in Southwestern Pennsylvania, and it provided good estimates to total demand. The model to estimate need evolves from the demand model. It enables planners to estimate unmet need occurring in the region. The effect of emergency transportation service (ETS) provider characteristics on demand was also investigated. Statistical tests show that, for purposes of forecasting demand, when the sociodemographic factors are taken into account, provider characteristics are not significant.

First term probation: models for identifying high risk students

EC 2000 has heightened awareness among engineering faculty about the importance of student retention, especially the retention of first-year students. Previous research found that students placed on academic probation after their first term have a high probability of leaving engineering prior to graduation. Using five years of data, we examine the influence of the students initial preparedness, attitude toward his/her chosen career, and self-assessed confidence in areas such as study habits and communication skills, on first term probation and retention. Logistic regression approaches were used to develop models that have enabled us to determine the factors that most influence first term probation and to better identify students who require early interventions if they are going to successfully complete the engineering curriculum.

Changes in freshman engineers' attitudes-a cross institutional comparison: what makes a difference?

The Freshman Engineering Attitude Instrument, developed at the University of Pittsburgh, was administered to the 1995-96 freshman engineering classes at two campuses at the beginning of the year (the pre-survey). The survey was then repeated later in the first year (the post-survey). This paper discusses the results and demonstrates the potential effectiveness of the survey instrument for evaluating freshman engineering programs. This study serves as a pilot for a larger, more comprehensive national survey.

The Role of Operations Research in Regional Health Planning

Operations-research workers have not met with much success in being accepted as integral members of regional-health-planning teams, owing in part to a lack of understanding by health planners of the skills the operations researcher has to offer and in part the analysts inability to demonstrate that he can close the gap between theoretical modeling and the implementation of his results. This paper explores the growth of regional health planning in the United States and highlights its important problem areas. The literature of operations-research applications to health planning is reviewed critically with respect to the feasibility of models and the appropriateness of assumptions. Specific problems with the types of studies currently in the literature are identified and recommendations are made for improved coordination between operations-research workers and health planners.

Improving student learning through the use of multisource assessment and feedback

The paper examines the use of multisource assessment and feedback processes in the classroom and the potential impact on student learning in engineering. Grounded in control and goal setting theories, this assessment process provides a means for students to take a proactive role in their learning. Research and practice issues are addressed.

Using technology to enhance outcome assessment in engineering education

This paper describes on-going research at several major universities on the design, development, and application of outcome assessment methodologies enhanced by information technologies. Several applications are described as well as advantages and disadvantages. Future research objectives are discussed.

The Model Eliciting Activity (MEA) Construct: Moving Engineering Education Research Into the Classroom

A growing set of “professional skills” including problem solving, teamwork, and communications are becoming increasingly important in differentiating U.S. engineering graduates from their international counterparts. A consensus of engineering educators and professionals now believes that mastery of these professional skills is needed for our graduates to excel in a highly competitive global environment. A decade ago ABET realized this and included these skills among the eleven outcomes needed to best prepare professionals for the 21st century engineering world. This has left engineering educators with a challenge: how can students learn to master these skills? We address this challenge by focusing on models and modeling as an integrating approach for learning particular professional skills, including problem solving, within the undergraduate curriculum. To do this, we are extending a proven methodology — model-eliciting activities (MEAs) — creating in essence model integrating activities (MIAs). MEAs originated in the mathematics education community as a research tool. In an MEA teams of students address an open-ended, real-world problem. A typical MEA elicits a mathematical or conceptual system as part of its procedural requirements. To resolve an MEA, students may need to make new connections, combinations, manipulations or predictions. We are extending this construct to a format in which the student team must also integrate prior knowledge and concepts in order to solve the problem at hand. In doing this, we are also forcing students to confront and repair certain misconceptions acquired at earlier stages of their education. A distinctive MEA feature is an emphasis on testing, revising, refining and formally documenting solutions, all skills that future practitioners should master. Student performance on MEAs is typically assessed using a rubric to measure the quality of solution. In addition, a reflection tool completed by students following an MEA exercise assists them in better assessing and critiquing their progress as modelers and problem solvers. As part of the first phase a large, MEA research study funded by the National Science Foundation and involving six institutions, we are investigating the strategies students use to solve unstructured problems by better understanding the extent that our MEA/MIA construct can be used as a learning intervention. To do this, we are developing learning material suitable for upper-level engineering students, requiring them to integrate concepts they’ve learned in foundation courses while teasing out misconceptions. We provide an overview of the project and our results to date.© 2008 ASME