A Look at the New Electrical Engineering Laboratory Program at the Hopkins

The undergraduate electrical engineering program at The Johns Hopkins University has undergone extensive revision. The most striking revision has been in the laboratory program. Laboratory courses which are distinct from the lecture courses have been developed. These laboratory courses embrace the fields of basic and advanced electrical measurements, transducers, passive circuits, active networks, communications, microwaves, materials, computers, servomechanisms, and energy conversion. The experiments in each one of these fields are designed to give the student insight into both the basic and advanced concepts involved. The sequence of presentation of the experiments is chosen to allow the most complete coverage of a subject as possible, based on the order in which the electrical engineering lecture courses are taken. The use of laboratory manuals, notebooks, reports, and examinations has been given careful thought and some significant ideas have been evolved with regard to their use in establishing a successful laboratory course.     

The problem of cultural fit—what can we learn from borrowing the German Dual System?

Culture impedes educational transactions between developed countries, and is often an area of tension when developing countries try to borrow models, or have models imposed upon them. This article uses the German Dual System as a case for examining issues surrounding the adaptation of borrowed models. Since attempts at borrowing models can fail for cultural as well as non‐cultural reasons, a contention here is that culture needs to be unpacked and laid bare, to avoid it becoming the too easy answer for failure. Borrowed models have a greater chance of taking hold, if the conditions that forged them are understood, and if conditions of the receiving country are likewise internalized. Limits imposed by culture have to be recognized. Four approaches to cultural borrowing that conceivably can reduce the tensions are proposed namely, partial borrowing, pilot borrowing, customization and conceptual borrowing.     

Faculty and administrator perceptions of their environments: Different views or different models of organization?

Individuals' perceptions of the culture and climate of the organizations in which they work influence their motivation and individual performance. Using a theoretical model of institutional culture, organizational climate, and faculty motivation, this study examines how faculty and academic administrators differ in their perceptions; whether these differences in perceptions are affected by institutional type; and to what extent faculty and administrators have different implicit models of their institutions (i.e., see different organizational variables as predictors of faculty motivation and involvement). The goals of this investigation are to shed additional light on the relationship between institutional variables and faculty performance, to examine the existence of differing implicit models, and to provide new insights for administrators in managing their postsecondary institutions.The research reported in this paper is part of the Research Program on The Organizational Context for Teaching and Learning in the National Center for Research to Improve Postsecondary Teaching and Learning (NCRIPTAL). The research is supported by a grant from the Office of Educational Research and Improvement (OERI), U.S. Department of Education (ED), to the University of Michigan (grant number G008690010). The opinions expressed herein are those of the authors and do not necessarily reflect the position or policy of the OERI/ED or the Regents of The University of Michigan, and no official endorsement should be inferred.     

Learning Environments and Learning Styles: Non-traditional Student Enrollment and Success in an Internet-based Versus a Lecture-based Computer Science Course

Colleges and universities are increasingly using information technologies to enhance the learning environment. Many educational institutions offer Internet-based on-line courses in an effort to meet the educational needs of students. The primary goal of this research was to determine if there is a relationship between students' preferred learning environment (i.e. face-to-face or on-line) and their learning style. The secondary goal was to determine if there were any differences in the academic success of students in the face-to-face versus on-line sections. Participants were adult (ages 22+ years), non-traditional computer science students given the option to take a face-to-face lecture-based or an on-line Internet-based computer science course. Results revealed that computer science students in the face-to-face learning environment were more likely to have the Assimilator learning-style, whereas computer science students in the on-line Internet-based learning environment were more likely to have the Converger learning-style. Student academic success did not reliably differ as a function of learning environment selection. Implications of these results are discussed in terms of learning style characteristics of computer science students, learning styles and gender differences and implications of student academic success in on-line vs face-to-face environments. This revised version was published online in July 2006 with corrections to the Cover Date.     

Multiple Time Scale Models in Sport and Exercise Science

In sport and exercise research, examining both within- and between-individual variation is crucial. The ability to investigate change both within competitive events and across a competitive season is a priority for many sport researchers. The aim of this article is to demonstrate an approach to analyzing intensive longitudinal data collected through time-scale-dependent longitudinal designs. Following didactic presentation of the approach, two illustrations from secondary data analysis are used to describe the modeling process in detail. Illustration 1 includes affective response data from an exercise intervention randomized pilot study. Illustration 2 uses referee and player distance data collected during professional soccer games in the English Premier League. Each illustration describes a process for testing and comparing multiple time scale models based on the three-level multilevel model, and results are interpreted. In the discussion, advantages and limitations of the approach are highlighted.