Wollega, E., & Ambrosio Winckler, V. (2016, June), User-Based Collaborative Filtering Recommender Systems Approach in Industrial Engineering Curriculum Design and Review Process  Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.27119

\bibitem{asee_peer_27119}
  Wollega, E., \& Ambrosio Winckler, V. (2016, June), \emph{User-Based Collaborative Filtering Recommender Systems Approach in Industrial Engineering Curriculum Design and Review Process}
  Paper presented at 2016 ASEE Annual Conference \& Exposition, New Orleans, Louisiana.
  10.18260/p.27119

Ebisa Wollega and Vitor Ambrosio Winckler.  "User-Based Collaborative Filtering Recommender Systems Approach in Industrial Engineering Curriculum Design and Review Process".  2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana, 2016, June.  ASEE Conferences, 2016.  https://peer.asee.org/27119 Internet.  11 Sep, 2024

\bibitem{asee_peer_27119}
  Ebisa Wollega and Vitor Ambrosio Winckler.
  "User-Based Collaborative Filtering Recommender Systems Approach in Industrial Engineering Curriculum Design and Review Process".
  \emph{2016 ASEE Annual Conference \& Exposition, New Orleans, Louisiana, 2016, June}.
  ASEE Conferences, 2016.
  https://peer.asee.org/27119 Internet.  11 Sep, 2024

@INPROCEEDINGS{asee_peer_27119
author = "Ebisa Wollega and Vitor Ambrosio Winckler"
title = "User-Based Collaborative Filtering Recommender Systems Approach in Industrial Engineering Curriculum Design and Review Process"
booktitle = "2016 ASEE Annual Conference \& Exposition"
year = "2016"
month = "June"
address = "New Orleans, Louisiana"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/27119}
number = {10.18260/p.27119}
}

TY  - CPAPER
AB  - Industrial engineering curriculum is relatively very sensitive to changes in industry needs compared to other engineering disciplines because of its structure. The effectiveness of the curriculum design and review process depends on the variety and the volume of input data.  Industrial engineering educators usually collect data from students, alumni, and industry stakeholders. With the availability of massive online data, mining relevant information and combining the information with the data collected from the traditional data sources would improve the efficacy of the industrial engineering curriculum design and review process. In this paper, we propose online job posting data as additional relevant information that can be integrated to the curriculum design and review process. We describe the adaptation of a user-based collaborative filtering recommender systems algorithm to analyze the online data and to convert the data into relevant information that can be used as input to the process. An undergraduate industrial engineering Operations Planning and Control course case study was used to illustrate the adaptation of the algorithm. Some of the topics taught in the course were searched on websites that advertise jobs and tallied. A professor who is familiar with the topics also provided expert judgments with regard to the relevance of the topics to industry needs. Both data sets were used as inputs to the algorithm. The experimental results show that some of the topics are highly correlated with the expert judgment than others; these topics would be given more emphasis than the less correlated topics during the curriculum design and review process. Analysis of new topics that did not receive expert judgments is also presented. The method proposed in this paper plays a great role in continuous curriculum review process as massive data sets can be extracted from online sources and processed within short time window. The industrial engineering educators can make use of more of the online data as input to curriculum design and review process to improve the efficiency of the process. This paper can also lead engineering educators to possibly explore the contribution of massive online data as an input to curriculum design and review process instead of simply relying on the traditional data sources.  
AU  - Ebisa Wollega
AU  - Vitor Ambrosio Winckler
CY  - New Orleans, Louisiana
DA  - 2016/06/26
PB  - ASEE Conferences
TI  - User-Based Collaborative Filtering Recommender Systems Approach in Industrial Engineering Curriculum Design and Review Process
UR  - https://peer.asee.org/27119
DO  - 10.18260/p.27119
ER  -