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ABSTRACT Engineering faculty should take advantage of data analytics to improve their curriculum, program, and student success. As such, faculty need to strategically conduct the entire data process: knowing the right questions to ask, determining the relevant data to collection, choosing the appropriate instruments to collect those data, analyzing that data, recommending appropriate actions, implementing them, and evaluating the implementation. Furthermore, students need to learn how to analyze data as part of their professional repertoire of design and development intellectual toolkits. To that end, learning analytics practices are detailed in this paper. Engineering education constitute the contextual focus. |
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KEYWORDS learning analytics, data analytics, data, curriculum, instructional design |
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REFERENCES [1] ABET, Criteria for Accrediting Engineering Programs, 2016-2017, ABET, 2016. [2] Tableau, The State of Data Education in 2016, Tableau, 2016, p. 6. [3] Safer, A., Farmer, L., & Chuk, E., Data Mining Technology across Academic Disciplines, Intelligent Information Management, Vol.3, No.2, 2011, pp. 43-48. [4] Long, P., & Siemens, G., Penetrating the Fog: Analytics in Learning and Education, Educause Review, Vol.46, No.5, 2011, pp. 31-40. [5] Romero.,C., & Ventura, S., Data Mining in Education, WIREs Data Mining and Knowledge Discovery, Vol.3, 2013, pp. 12-27. [6] Finos, R., Wikibon Big Data Analytics Survey, SiliconANGLE Media, 2015. [7] Henschen, D., 2015 analytics and IB Survey, UBM Tech, 2014. |
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Cite this paper Lesley S. J. Farmer. (2017) Learning Analytics For Engineering Education. International Journal of Education and Learning Systems, 2, 6-11 |
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