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

\bibitem{asee_peer_20244}
  Simoni, M., \& Fayyaz, F., \& Streveler, R. (2014, June), \emph{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

Mario Simoni, Farrah Fayyaz, and Ruth Streveler.  "Data Mining to Help Determine Sources of Difficulty in an Introductory Continuous-Time Signals and Systems Course".  2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana, 2014, June.  ASEE Conferences, 2014.  https://peer.asee.org/20244 Internet.  24 Apr, 2024

\bibitem{asee_peer_20244}
  Mario Simoni, Farrah Fayyaz, and Ruth Streveler.
  "Data Mining to Help Determine Sources of Difficulty in an Introductory Continuous-Time Signals and Systems Course".
  \emph{2014 ASEE Annual Conference \& Exposition, Indianapolis, Indiana, 2014, June}.
  ASEE Conferences, 2014.
  https://peer.asee.org/20244 Internet.  24 Apr, 2024

@INPROCEEDINGS{asee_peer_20244
author = "Mario Simoni, Farrah Fayyaz, and Ruth Streveler"
title = "Data Mining to Help Determine Sources of Difficulty in an Introductory Continuous-Time Signals and Systems Course"
booktitle = "2014 ASEE Annual Conference \& Exposition"
year = "2014"
month = "June"
address = "Indianapolis, Indiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/20244}
number = {10.18260/1-2--20244}
}

TY  - CPAPER
AB  -             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.
AU  - Mario Simoni
AU  - Farrah Fayyaz
AU  - Ruth Streveler
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Data Mining to Help Determine Sources of Difficulty in an Introductory Continuous-Time Signals and Systems Course
UR  - https://peer.asee.org/20244
DO  - 10.18260/1-2--20244
ER  -