New Orleans, Louisiana
June 26, 2016
June 26, 2016
August 28, 2016
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
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2016 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015