Thursday Sessions

Thursday Sessions Thursday Matrix Placeholder Proceedings IEEE Catalog Number: 03CH37487/$17.00 November 5 - 8, 2003, Boulder, Colorado 33rd ASEE/IEEE Frontiers in Education Conference 57 Thursday Sessions Thursday Matrix Backpage Proceedings IEEE Catalog Number: 03CH37487/$17.00 November 5 - 8, 2003, Boulder, Colorado 33rd ASEE/IEEE Frontiers in Education Conference 58 Thursday Sessions Session T1A: Keynote - "Engineering as a Human Endeavor", William Wulf Chair: Melinda Piket-May and James Avery, University of Colorado at Boulder Time and place: Thursday, 8:30 am - 9:30 am Westminster 3 & 4 KEYNOTE - ENGINEERING AS A HUMAN ENDEAVOR William Wulf, National Academy of Engineering William Wulf is president of the National Academy of Engineering and vice chair of the National Research Council, the principal operating arm of the National Academies of Sciences and Engineering. He is on leave from the University of Virginia-Charlottesville, where he is the AT&T professor of engineering and applied sciences. At the university he is involved in a complete revision of the undergraduate computer science curriculum, research on computer architecture and computer security, and helping humanities scholars exploit information technology.Dr. Wulf has had a distinguished pro- fessional career that includes serving as assistant director of the National Science Foundation; chair and chief executive officer of Tartan Laboratories, Inc., in Pittsburgh, Pennsylvania; and professor of computer science at Carnegie Mellon University, also in Pittsburgh. He is the author of more than 80 papers and technical reports, has written three books, and holds one U.S. patent. Session T2A: Successful Students Chair: Elizabeth A. Eschenbach, Humboldt State University Time and place: Thursday, 10:00 am - 11:45 am Cotton Creek I THE STUDY STRATEGIES OF ACADEMICALLY SUCCESSFUL STUDENTS AT THE COLORADO SCHOOL OF MINES Ruth A. Streveler, Colorado School of Mines, Tawni Hoeglund, Colorado School of Mines and Carla Stein, Western Nebraska Community College A 42-item questionnaire was administered to 285 Colorado School of Mines students in sophomore design. Factor analysis was performed, resulting in a five-factor solution. Factors were then correlated with cumulative grade point average. Four of the five factors were significantly correlated to cumulative grade point average. (Three factors were negatively correlated, one positively correlated to grade point.) Step-wise regression was also performed to see the effect of each factor on cumulative grade point average. A RETROSPECTIVE PROFILE OF ELECTRICAL ENGINEERING GRADUATES FROM THE FAMU-FSU COL- LEGE OF ENGINEERING Leslie Inniss, Florida A&M University and Reginald Perry, FAMU-FSU College of Engineering With the continuing interest in the successful retention of engineering students, particularly underrepresented minority students, many studies have been conducted on the students experiences while in college. The results of these studies have been used to identify factors that will enhance student retention. In this paper we take a slightly different perspective. We will present a retrospective look at 278 students who have successfully completed a bachelor of science in electrical engineering degree program between 1999 and 2002 from the joint Florida A&M University-Florida State University (FAMU- FSU) College of Engineering. Florida A&M University is a historically black university while Florida State University has a majority white student enrollment. Students complete their basic mathematics and science credits at their home university and then enter the joint FAMU-FSU engineering program to complete their remaining degree requirements. This exploratory study is not meant to explain why the students graduated, but rather to offer a comprehensive profile of a successful electrical engineering graduate. WORK IN PROGRESS - CHARACTERISTICS OF STUDENTS WHO FAILED (OR SUCCEEDED) THE INTRO- DUCTORY CS COURSE Judith Gal-Ezer, The Open University of Israel, Tamar Vilner, The Open University of Israel and Ela Zur, The Open University of Israel The Open University of Israel, with its policy of open admissions, offers an undergraduate program of study in Com- puter Science. At the beginning of their studies, students take mathematics courses and the CS1 course Introduction to Computer Science which many students fail. This study attempts to identify characteristics which can predict success or failure in the course in order to identify students who are likely to fail. Assuming that students background knowledge is a predictor of success, we chose to examine the relationship between students prior knowledge and their success in the course. On the basis of our findings we will suggest ways to increase the pass rate in the course. Proceedings IEEE Catalog Number: 03CH37487/$17.00 November 5 - 8, 2003, Boulder, Colorado 33rd ASEE/IEEE Frontiers in Education Conference 59 Thursday Sessions PREDICTING STUDENT PERFORMANCE: AN APPLICATION OF DATA MINING METHODS WITH AN EDU- CATIONAL WEB-BASED SYSTEM Behrouz Minaei-Bidgoli, Michigan State University, Deborah A. Kashy, Michigan State University, Gerd Kortem- eyer, Michigan State University and William F. Punch, Michigan State University Newly developed web-based educational technologies offer researchers unique opportunities to study how students learn and what approaches to learning lead to success. Web-based systems routinely collect vast quantities of data on user patterns, and data mining methods can be applied to these databases. This paper presents an approach to classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. We design, implement, and evaluate a series of pattern classifiers and compare their performance on an online course dataset. A combination of multiple classifiers leads to a significant improvement in classification performance. Further- more, by learning an appropriate weighting of the features used via a genetic algorithm (GA), we further improve predic- tion accuracy. The GA is demonstrated to successfully improve the accuracy of combined classifier performance, about 10 to 12% when comparing to non-GA classifier. This method may be of considerable usefulness in identifying students at risk early, especially in very large classes, and allow the instructor to provide appropriate advising in a timely manner. A STRUCTURAL MODEL OF ENGINEERING STUDENTS SUCCESS AND PERSISTENCE Brian F. French, Purdue University, Jason C. Immekus, Purdue University and William Oakes, Purdue University This study examined a model of student success and persistence at two levels: university and engineering major. The model, based on theoretical and empirical evidence, included both cognitive and noncognitive factors. Cognitive factors included High School Rank, Scholastic Aptitude Scores, and University Grade Point Average. Noncognitive factors included motivation, as well as faculty and student integration. Outcome variables in the model were grade point average, enrollment at the university, as well as within engineering. Through the use of path analysis, several significant relation- ships among the factors were found. For instance, grade point average was significantly related to enrollment in both the university and engineering major. Increased levels of student interactions were significantly related to continued enrollment in engineering. Interestingly, student with higher faculty integration were more likely to change majors. Implications and directions for future research are discussed. ENGINEERING SCHOLARS PROGRAM AT FAU Sam Hsu, Florida Atlantic University, Sharon Schlossberg, Florida Atlantic University and Karl Stevens, Florida Atlantic University A dual-enrollment credit summer program offered by the College of Engineering at Florida Atlantic University for high-achieving high school students is described. Known as the Engineering Scholars Program, and funded partially by the State of Florida Governor s Summer Program, this activity provides gifted students challenging educational opportunities not available in regular high school settings. This program is designed around nine learning objectives that focus upon teamwork, communication, and placement of math and science concepts into the context of real-world problems. Typical course offerings are Automation and Robotics, Electronic Design with Operational Amplifies, Introduction to Inventive Problem Solving, and Introduction to Web Authoring/Programming. Select members of the College of Engineering faculty, assisted by a class mentor, provide instruction for these courses. Program assessment is based primarily on student evaluations. Currently, over 80% of the student participants report a high level of satisfaction with the program. When it began in Summer 1997, the program involved four courses and about 70 students. It has now expanded to five courses and over 100 students, with a wait-list for participation. Session T2B: Assessment and Information Technology Chair: Susan Haag, Arizona State University Time and place: Thursday, 10:00 am - 11:45 am Cotton Creek II WORK IN PROGRESS - NUMBERS ARE NO SUBSTITUTE FOR JUDGEMENT Frank S. Barnes, University of Colorado at Boulder The substitution of numbers for the evaluation of universities, teaching and research, have proven to be a convenient way to avoid the hard work of finding out what the significance of the various activities really are. Common examples where

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