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User-Based Collaborative Filtering Recommender Systems Approach in Industrial Engineering Curriculum Design and Review Process

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2016 ASEE Annual Conference & Exposition


New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016





Conference Session

Industrial Engineering Division Technical Session 3

Tagged Division

Industrial Engineering

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Paper Authors


Ebisa Wollega Colorado State University - Pueblo

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Ebisa D. Wollega, Ph.D. is an Assistant Professor of Engineering at Colorado State University-Pueblo. He received his Ph.D. degree in industrial engineering from the University of Oklahoma. His research interest areas include stochastic systems modeling and optimization, big data analytics, large scale optimization algorithms, and engineering education. His email is and his web page is

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Vitor Ambrosio Winckler Colorado State University - Pueblo

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Vitor Ambrosio Winckler is an undergraduate student of Mechatronics Engineering program at the Polytechnic School of Sao Paulo University, Brazil. He is participating in the Brazil Scientific Mobility Program with the Colorado State University – Pueblo as the host university. He worked for Siemens in Brazil where he was recognized for two awards. His research interests include artificial intelligence and big data analytics.

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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.

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

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