Oosthuizen, RudolphGrobbelaar, S2022-02-252022-02-252021-10Oosthuizen, R. & Grobbelaar, S. 2021. Implementing bibliometric analysis and topic modelling to inform curriculum development for engineering management. http://hdl.handle.net/10204/12293 .978-8-9853334-0-4978-1-7138415-6-2https://www.proceedings.com/content/062/062095webtoc.pdfhttp://hdl.handle.net/10204/12293This paper aims to inform the course curriculum development for a master's degree in Engineering Management. Implementing a bibliometric analysis with topic modelling performed on relevant publications in the field of Engineering Management provides valuable inputs to the selection of content for a curriculum. Topic modelling is a form of unsupervised machine-learning-based natural language processing. The algorithm extracts main topics from the titles and abstracts of a wide range of papers published about Engineering Management. These topics are identified and compared, for validation, to current thinking about Engineering Management curriculums. This approach is implemented to ensure that the course content is relevant to the field of Engineering Management.AbstractenEngineering curriculum developmentEngineering managementNatural Language ProcessingTopic modellingConference PresentationOosthuizen, R., & Grobbelaar, S. (2021). Implementing bibliometric analysis and topic modelling to inform curriculum development for engineering management. http://hdl.handle.net/10204/12293Oosthuizen, Rudolph, and S Grobbelaar. "Implementing bibliometric analysis and topic modelling to inform curriculum development for engineering management." <i>American Society for Engineering Management (ASEM) Virtual International Conference, Virtual, 27-30 October 2021</i> (2021): http://hdl.handle.net/10204/12293Oosthuizen R, Grobbelaar S, Implementing bibliometric analysis and topic modelling to inform curriculum development for engineering management; 2021. http://hdl.handle.net/10204/12293 .TY - Conference Presentation AU - Oosthuizen, Rudolph AU - Grobbelaar, S AB - This paper aims to inform the course curriculum development for a master's degree in Engineering Management. Implementing a bibliometric analysis with topic modelling performed on relevant publications in the field of Engineering Management provides valuable inputs to the selection of content for a curriculum. Topic modelling is a form of unsupervised machine-learning-based natural language processing. The algorithm extracts main topics from the titles and abstracts of a wide range of papers published about Engineering Management. These topics are identified and compared, for validation, to current thinking about Engineering Management curriculums. This approach is implemented to ensure that the course content is relevant to the field of Engineering Management. DA - 2021-10 DB - ResearchSpace DP - CSIR J1 - American Society for Engineering Management (ASEM) Virtual International Conference, Virtual, 27-30 October 2021 KW - Engineering curriculum development KW - Engineering management KW - Natural Language Processing KW - Topic modelling LK - https://researchspace.csir.co.za PY - 2021 SM - 978-8-9853334-0-4 SM - 978-1-7138415-6-2 T1 - Implementing bibliometric analysis and topic modelling to inform curriculum development for engineering management TI - Implementing bibliometric analysis and topic modelling to inform curriculum development for engineering management UR - http://hdl.handle.net/10204/12293 ER -25126