Gillian Saunders-Smits

Bio: Gillian Saunders-Smits is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Engineering education & Active learning. The author has an hindex of 9, co-authored 26 publications receiving 219 citations.

Evaluation of competency methods in engineering education: a systematic review

Abstract: The purpose of this systematic review is to evaluate the state-of-the-art of competency measurement methods with an aim to inform the creation of reliable and valid measures of student mastery of c...

Assessment of curriculum quality through alumni research

Abstract: In today's output defined society, alumni are the output of higher education. This article shows how alumni research can be used as an important indicator of curriculum quality. This relatively unexplored area of engineering education research in Europe is highlighted using a case study carried out in the Netherlands, the outcomes of which have served as a useful input for curriculum development at the institute where it was carried out.

Using the Engineering Design Cycle to Develop Integrated Project Based Learning in Aerospace Engineering

Abstract: Over the past four years the Faculty of Aerospace Engineering at Delft University of Technology in the Netherlands has redeveloped its BSc curriculum to mimic an engineering design cycle. Each semester represents a step in the design cycle: exploration; system design; sub-system design; test, analysis & simulation; verification & validation.

Research and Practice of Active Learning in Engineering Education

Abstract: Since 2001, the international network Active Learning in Engineering education (ALE) organized a series of international workshops on innovation of engineering education. The papers in this book are selected to reflect the state of the art, based on contributions to the 2005 ALE workshop in Holland. This overview of experiences in research and practice aims to be a source of inspiration for engineering educators.

The Use of Teaching Assistants in Project Based Learning at Aerospace Engineering

Abstract: Project-based learning by its nature requires a lot of staff efforts in terms of tutoring of the project groups. At the Faculty of Aerospace Engineering at Delft University of Technology in the Netherlands, traditionally in the first two years of the BSc degree use is made of senior and Master students to take over part of the tutoring role from the academic staff. This is due to the fact that in the Netherlands the number of PhD students and academic staff per 100 students is relatively low in comparison to the US. This paper describes how these assistants are used in the projects, what their responsibilities are, and the need for training. The pros and cons of using teaching-assistants in project-based learning are also discussed with a clear look at the ethical side of the use of teaching-assistants. Also a close look will be taken at the limitations of the use of teaching-assistants.

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

Abstract: Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

Management teams: Why they succeed or fail

Abstract: A Study of Teams: How It All Began The Apollo Syndrome Teams Containing Similar Personalities Identifying further Team Roles Team Leadership The Missing Team Roles Developing an inventory Unsuccessful teams Winning teams Ideal team size Features of good members of a team Teams in Public Affairs How Belbin reports developed Case Studies in Using Belbin

The Globally Competent Engineer: Working Effectively with People Who Define Problems Differently

Abstract: This paper offers and tests an approach to conceptualizing the global competency of engineers. It begins by showing that the often-stated goal of working effectively with different cultures is fundamentally about learning to work effectively with people who define problems differently. The paper offers a minimum learning criterion for global competency and three learning outcomes whose achievement can help engineering students fulfill that criterion. It uses the criterion to establish a typology of established methods to support global learning for engineering students. It introduces the course, Engineering Cultures, as an example of an integrated classroom experience designed to enable larger numbers of engineering students to take the critical first step toward global competency, and it offers a test application of the learning criterion and outcomes by using them to organize summative assessments of student learning in the course.

Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models

Abstract: Predicting student academic performance has long been an important research topic in many academic disciplines. The present study is the first study that develops and compares four types of mathematical models to predict student academic performance in engineering dynamics - a high-enrollment, high-impact, and core course that many engineering undergraduates are required to take. The four types of mathematical models include the multiple linear regression model, the multilayer perception network model, the radial basis function network model, and the support vector machine model. The inputs (i.e., predictor variables) of the models include student's cumulative GPA, grades earned in four pre-requisite courses (statics, calculus I, calculus II, and physics), and scores on three dynamics mid-term exams (i.e., the exams given to students during the semester and before the final exam). The output of the models is students' scores on the dynamics final comprehensive exam. A total of 2907 data points were collected from 323 undergraduates in four semesters. Based on the four types of mathematical models and six different combinations of predictor variables, a total of 24 predictive mathematical models were developed from the present study. The analysis reveals that the type of mathematical model has only a slight effect on the average prediction accuracy (APA, which indicates on average how well a model predicts the final exam scores of all students in the dynamics course) and on the percentage of accurate predictions (PAP, which is calculated as the number of accurate predictions divided by the total number of predictions). The combination of predictor variables has only a slight effect on the APA, but a profound effect on the PAP. In general, the support vector machine models have the highest PAP as compared to the other three types of mathematical models. The research findings from the present study imply that if the goal of the instructor is to predict the average academic performance of his/her dynamics class as a whole, the instructor should choose the simplest mathematical model, which is the multiple linear regression model, with student's cumulative GPA as the only predictor variable. Adding more predictor variables does not help improve the average prediction accuracy of any mathematical model. However, if the goal of the instructor is to predict the academic performance of individual students, the instructor should use the support vector machine model with the first six predictor variables as the inputs of the model, because this particular predictor combination increases the percentage of accurate predictions, and most importantly, allows sufficient time for the instructor to implement subsequent educational interventions to improve student learning.

Which ABET Competencies Do Engineering Graduates Find Most Important in their Work

Abstract: Background ABET-accredited engineering programs must help students develop specific outcomes (i.e., competencies). Faculty must determine the relative emphasis among the competencies. Yet, information is sparse about the relative importance of each competency for professional practice. Purpose (Hypothesis) This study synthesizes opinions of engineering graduates about which competencies are important for professional practice. Design/Method A survey asked undergraduate alumni of a large public university in the Midwest to rate the importance of the ABET competencies in their professional experience. Responses included descriptions of education, post-graduate work environment, and demographics. Protected, post-hoc, all-pairwise multiple comparisons determined patterns in the importance ratings, for the aggregate, and for descriptive subgroups. Results The lowest-rated competency's mean rating was 3.3 out of 5. Graduates of 11 engineering majors rated a top cluster of competencies (teamwork, communication, data analysis, and problem solving) significantly higher than a bottom cluster (contemporary issues, design of experiments, and understanding the impact of one's work). Importance ratings of five other competencies fell in an intermediate cluster in which importance was statistically tied to either the top or bottom cluster, depending on work environment or academic discipline-not demographics. The clusters were stable over time, that is, over seven survey administrations (1999–2005), years since graduation (0, 2, 6 & 10), and graduation year (1989–2003). Conclusions Graduates across engineering disciplines share a pattern of importance for professional practice among the ABET competencies that is statistically significant, consistent across demographic variables, and stable over time. This pattern can inform faculty decisions about curriculum emphasis within and across engineering disciplines.