Becker, J. P., & Sior, E., & Hoy, J., & Kahanda, I. (2019, June), Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning   Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32185

\bibitem{asee_peer_32185}
  Becker, J. P., \& Sior, E., \& Hoy, J., \& Kahanda, I. (2019, June), \emph{Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning }
  Paper presented at 2019 ASEE Annual Conference \& Exposition , Tampa, Florida.
  10.18260/1-2--32185

James P. Becker, Emily Sior, Jerad Hoy, and Indika Kahanda.  "Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning ".  2019 ASEE Annual Conference & Exposition , Tampa, Florida, 2019, June.  ASEE Conferences, 2019.  https://peer.asee.org/32185 Internet.  15 Dec, 2024

\bibitem{asee_peer_32185}
  James P. Becker, Emily Sior, Jerad Hoy, and Indika Kahanda.
  "Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning ".
  \emph{2019 ASEE Annual Conference \& Exposition , Tampa, Florida, 2019, June}.
  ASEE Conferences, 2019.
  https://peer.asee.org/32185 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_32185
author = "James P. Becker, Emily Sior, Jerad Hoy, and Indika Kahanda"
title = "Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning "
booktitle = "2019 ASEE Annual Conference \& Exposition "
year = "2019"
month = "June"
address = "Tampa, Florida"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/32185}
number = {10.18260/1-2--32185}
}

TY  - CPAPER
AB  - Writing exercises may be used in problem-centric STEM-based courses to identify common misconceptions held by the writer as well as to probe their metacognitive processes.  As grading of writing samples and providing personalized feedback regarding a student’s writing can be time-intensive, opportunities to automate the process while retaining the integrity of the grading and quality of the feedback are attractive.  

This paper describes the motivation and use of a writing-based exercise in a sophomore-level course on electric circuit analysis.  The conversion of a paper-based writing exercise to a web-based application is detailed as is its initial use in this new format.  The ultimate goal of implementing a web-based approach to administering the writing exercise is to build a fully automated application capable of evaluating student responses and providing feedback to the user in an attempt to enhance their conceptual understanding of challenging material in a manner that acknowledges instructor workload in high-enrollment, resource-constrained courses.  

The first element in the planned automated evaluation aspect of the writing application is the identification of students scoring at the lowest end of a holistic scale.  This is of significant value as there is evidence that such students are at-risk to fail the electric circuits course as it is currently constructed.  Use of a basic natural language processing (NLP) pipeline on a dataset of more than one hundred student responses is described as are the initial results of the at-risk / not at-risk binary classification task. 



AU  - James P. Becker
AU  - Emily Sior
AU  - Jerad Hoy
AU  - Indika Kahanda
CY  - Tampa, Florida
DA  - 2019/06/15
PB  - ASEE Conferences
TI  - Board 11: Predicting At-Risk Students in a Circuit Analysis Course Using Supervised Machine Learning 
UR  - https://peer.asee.org/32185
DO  - 10.18260/1-2--32185
ER  -