Branch, K. J., & Butterfield, A. (2018, June), Modeling Student Performance in an Introductory Chemical  Engineering Course  Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30817

\bibitem{asee_peer_30817}
  Branch, K. J., \& Butterfield, A. (2018, June), \emph{Modeling Student Performance in an Introductory Chemical  Engineering Course}
  Paper presented at 2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah.
  10.18260/1-2--30817

Kyle Joe Branch and Anthony Butterfield.  "Modeling Student Performance in an Introductory Chemical  Engineering Course".  2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah, 2018, June.  ASEE Conferences, 2018.  https://peer.asee.org/30817 Internet.  21 Jan, 2025

\bibitem{asee_peer_30817}
  Kyle Joe Branch and Anthony Butterfield.
  "Modeling Student Performance in an Introductory Chemical  Engineering Course".
  \emph{2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah, 2018, June}.
  ASEE Conferences, 2018.
  https://peer.asee.org/30817 Internet.  21 Jan, 2025

@INPROCEEDINGS{asee_peer_30817
author = "Kyle Joe Branch and Anthony Butterfield"
title = "Modeling Student Performance in an Introductory Chemical  Engineering Course"
booktitle = "2018 ASEE Annual Conference \& Exposition "
year = "2018"
month = "June"
address = "Salt Lake City, Utah"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/30817}
number = {10.18260/1-2--30817}
}

TY  - CPAPER
AB  - We have developed an open-ended, laboratory-based introduction to chemical engineering, a course coupling traditional, hands-on, and virtual learning components. This course is supported by an online homework system which we developed to administer and track student usage on a variety of course materials, including homework assignments, lab safety training quizzes, course surveys, and simulated laboratory experiments. This system is also used to track student peer evaluations and which equipment students are certified to use in the lab.

Using the system’s tracking capabilities, we have gathered a sizable collection of data on various student characteristics and behaviors in the course, and we have performed regression analyses to search for insights into how these characteristics and behaviors may relate to student performance, as measured by their overall score in the course. From these analyses, two variables emerged as highly predictive of student performance: scores on peer evaluations and homework submission timeliness. This relationship remains strong even when the measure of student performance is adjusted so that student peer evaluations and late penalties on homework assignments do not directly factor into their adjusted overall score. We discuss potential explanations for and practical implications of this result.
AU  - Kyle Joe Branch
AU  - Anthony Butterfield
CY  - Salt Lake City, Utah
DA  - 2018/06/23
PB  - ASEE Conferences
TI  - Modeling Student Performance in an Introductory Chemical  Engineering Course
UR  - https://peer.asee.org/30817
DO  - 10.18260/1-2--30817
ER  -