The IEEE Education Society started the Frontiers in Education Conference (FIE) in 1971, a time of concern for its survival for financial and membership reasons, a time of unrest in the engineering education community around the world, and as is so common, a time of great challenge and opportunities. FIE began with about 100 attendees in Atlanta, led by Demetrius Paris. It expanded by adding the Educational Research and Methods (ERM) Division of ASEE in 1973, and the IEEE Computer Society (CS) in 1995. FIE ventured outside the USA in 1974, and continued taking on new challenges, and by giving young scholars in engineering education research a venue for interaction and evaluation of their work. These scholars are asking good questions-important questions, questions that challenge us all, and are getting answers that are improving our education. In turn, these answers lead to new and more challenging questions. This is a journey, not a destination. While not the only contributor, this conference has made a difference-it is small enough for much interaction, big enough for bringing together new scholars with “old-timers.” Both authors have complete files of all FIE Proceedings. Some of this history will be traced, but the emphasis will be on results and a look at the future.

This Work-in-Progress paper describes an ongoing project for modernizing the probability and statistics program component for electrical and computer engineering majors at the University of Kansas. Traditionally in engineering curricula around the nation, this three-hour senior-level course has at most focused on the probability of overlapping events, properties and examples of discrete and continuous random variables, and only rudimentary statistical principles involving confidence intervals, hypothesis testing, and linear least-squares curve fitting. Some courses nationwide emphasize only probability and random variables, some focus only on the statistical aspects of data handling, and a few also include small projects that feature computer-generated pseudo-random numbers. ABET has program criteria that require graduates to have some knowledge of probability and statistics. Usually, programs seek to satisfy this requirement by one of these single self-contained three-hour courses. What is needed to modernize the curricula is a more comprehensive treatment that not only improves the course and extends these concepts beyond into the senior capstone and other courses but also prepares graduates for more extensive probability and statistics applications after graduation.

Each year since the founding in 1971, Frontiers in Education Conference participants have sought the newest innovations and concepts in engineering education, i.e., the true frontiers at the cutting-edge of the profession. While those frontiers have obviously changed over the past 42 years, the primary objective of identifying and addressing them has remained at the forefront. This WIP paper describes what was perceived as the main frontiers in each of four periods corresponding to the four decades since the first conference by examining papers from two sample conferences in each decade. In addition to a careful analysis of selected FIE Proceedings, results of a brief survey of long-time FIE leaders and more recent participants are presented as further evidence on identifying the recognized frontiers for each period. Changes during the intervening years of the conference included more formal presentations, required reviews before accepting papers, and references to clearly show the relevance of each paper within the body of knowledge. Currently, papers are categorized as innovative practice, research-to-practice, or research; an emphasis on STEM research nationally has created engineering education departments within universities. Faculty and staff from these departments regularly present their research results at FIE Conferences. The overall goal for conference organizers and program chairs is to make certain not only that accepted papers point to existing frontiers as the major focus of the FIE Conference each year but also include invited speakers, sessions, panels, and keynote addresses that focus on other emerging frontiers.

Anonymized Call Detail Records (CDRs) contain positional information of large populations and therefore have been extensively analyzed to understand human mobility. Due to the temporally sparse and spatially coarse nature of the data, most of these studies have focused on primitive aspects of movements such as travel distance and speed. Incorporating underlying geographic information in these analyses would allow analysts to put these movements into context and to gain deeper insight into how metropolitan areas function. In this paper, we present a set of procedures for inferring mobile users' mobility patterns while retaining the context of underlying geography. We apply these methods to our case study on New York City anonymized CDRs. We find that our methods verify current areal semantics and commuting rush-hour patterns, and we also derive further implications regarding geographic, demographic, and other effects on human mobility.

As the next generation of engineers, engineering students at the local level have specific views on what key issues should be addressed in the coming decades. Results of a University of Kansas survey completed by over 220 engineering students from sophomores to graduate students in seven engineering majors from five departments are reported, analyzed, and compared with the 14 Grand Challenges for Engineering presented by the National Academy of Engineering in 2008. While some areas follow from the NAE list, other suggestions are innovative beyond that list and yet support the goals and spirit of the subsequent NAE Grand Challenge Scholars Program. The contributions of this study are: (1) a report of survey findings from six classes of students, (2) an analysis of student responses according to levels and majors, and (3) a comparison of these viewpoints with the NAE Grand Challenges for Engineering.

A model validation procedure based on Theil's inequality coefficient (TIC) is extended for handling nonstationary signal responses from actual systems and simulated models. Initially, the parent coefficient for TIC and its estimate, as defined by Theil, are presented. Thereafter, trend removal concepts are applied to the nonstationary data to produce weakly stationary difference data from which desired statistics may be computed by time series analysis. A least-squares curve fit is proposed for removing time-varying means. For certain applications more elaborate procedures involving nonlinear operations are required to handle time-varying variances. Some cases require segmenting the data and using trend removal procedures on a piece-wise basis. After time series analysis has been utilized on the resulting weakly stationary data, the removed trends are reinserted to yield a time-varying estimate of TIC. Examples are provided to illustrate two types of nonstationary cases.

The principle of dynamic programming is used to develop a digital computer software package for determining for each of several subsystems the optimal numerical integration algorithm from among given candidate techniques. As by-products, the corresponding optimal step sizes and error contributions of the subsystems are provided to the user. The developed program minimizes subsystem sum-squared errors subject to the constraint that the total time available for executing all subsystem integrations is specified in advance. After the basic concept of dynamic programming has been reviewed briefly, the details of the computer software package are presented. Numerical results are given for two examples, including a limited hybrid partitioning problem for a six-degree-of-freedom air-defense missile simulation. Finally, general considerations for hybrid partitioning are discussed, and extensions to the developed program are suggested.

Variational equations are derived as a preliminary step in determining efficient digital integration techniques for nonlinear dynamical systems. The variational approach is applied initially to linear time-invariant systems to introduce the basic concept and then to nonlinear time-varying systems. For systems containing both linear and nonlinear parts, a combination technique which uses the exact difference equation for the linear part is developed. Higher order variational equations are also derived and compared on a simple system. Numerical approximations for solving these variational equations are discussed and illustrated for a second-order mildly nonlinear example. A significant improvement in both accuracy and execution time is realized over results obtained by the conventional fourth-order Runge–Kutta method. Finally, the new approach is discussed from the viewpoint of computational experience and special limitations for practical applications.