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Data Mining to Help Determine Sources of Difficulty in an Introductory Continuous-Time Signals and Systems Course

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2014 ASEE Annual Conference & Exposition


Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014



Conference Session

Improvements in ECE Signals and Systems

Tagged Division

Electrical and Computer

Page Count


Page Numbers

24.353.1 - 24.353.14



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Paper Authors


Mario Simoni Rose-Hulman Institute of Technology

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Mario Simoni is an Associate Professor of Electrical and Computer Engineering at Rose-Hulman Institute of Technology in Terre Haute, IN.

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Farrah Fayyaz Purdue University


Ruth Streveler Purdue University, West Lafayette

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Ruth A. Streveler is an Associate Professor in the School of Engineering Education at Purdue University. Dr. Streveler has been the Principal Investigator or co-Principal Investigator of ten grants funded by the US National Science Foundation. She has published articles in the Journal of Engineering Education and the International Journal of Engineering Education and has contributed to the Cambridge Handbook of Engineering Education Research. She has presented workshops to over 500 engineering faculty on four continents. Dr. Streveler’s primary research interests are investigating students’ understanding of difficult concepts in engineering science and helping engineering faculty conduct rigorous research in engineering education.

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Data Mining to Help Determine Sources of Difficulty in an Introductory Continuous-Time Signals and Systems CourseThe introductory continuous-time signals and systems (CTSS) course is one of the most difficultcourses that students encounter in an electrical and computer engineering (ECE) curriculum, asevidenced by well-above-average drop/failure rates. There are many speculations about whystudents might have difficulty in CTSS courses. Possible reasons might be mismatches betweenpresentation and learning style, a lack of understanding of the relevance of the concepts, or a lackof ability to process and visualize the complex mathematical operations and procedures. In thisstudy, we analyzed a large data set to try to identify some possible sources of difficulty bylooking for correlations between the students’ performance in the course with other measures ofacademic ability or learning style preferences. This data set consists of approximately 1700students taking an introductory CTSS course, ECE300, at a single institution over the past 10years.The primary measures of performance in ECE300 include the course grade, and pre- and post-scores on the nationally administered Signals and Systems Concept Inventory (SSCI). Thecourse grade is based primarily on exams and homework which principally measure an ability toperform complex mathematical operations and to plot and visualize data. The concept inventoryinstead measures a student’s ability to comprehend the concepts and apply them in differentsituations without any mathematical calculation. While the two forms of data are expected to bestrongly positively correlated what is more interesting are the cases that are negativelycorrelated.The ECE300 performance data is correlated with grades from math, physics, programming, andelectromagnetics courses and also overall GPA. Introductory CTSS courses are verymathematical and theoretical, and require an ability to use mathematics to model and visualizeabstract concepts. The grades from these courses will provide a measure a student’s abilities inthese areas. Correlations between these performance metrics and ECE300 will help determine ifa student who is weaker in these areas can still grasp the concepts in ECE300. Conversely, itcould also indicate that strength in these areas is not a guarantee for success. Looking at GPAallows us to correlate overall academic ability with performance in ECE300.The final type of data is the Felder-Silverman Index of Learning Styles (ILS). Data suggests thatmost engineering students have an Active, Visual, and Sensory learning style. However, thematerial in introductory CTSS courses is so abstract and theoretical, it is most often presented inways that are opposite to students’ preferred learning style. Correlating performance in ECE300to ILS data could suggest a need to alter how the material is introduced to the students.

Simoni, M., & Fayyaz, F., & Streveler, R. (2014, June), Data Mining to Help Determine Sources of Difficulty in an Introductory Continuous-Time Signals and Systems Course Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20244

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