An Investigation of First-Year Engineering Student and Instructor Perspectives of Learning Analytics Approaches

David B Knight
Cory Brozina
Brian Novoselich


This paper investigates how first-year engineering undergraduates and their instructors describe the potential for learning analytics approaches to contribute to students’ success.  Results of qualitative data collection in a first-year engineering course indicated that both students and instructors emphasized a preference for learning analytics systems to focus on aggregate as opposed to individual data.  Another consistent theme across students and instructors was an interest in bringing data related to time (e.g., how time is spent outside of class) into learning analytics products.  Students’ and instructors’ viewpoints diverged in the “level” at which they would find a learning analytics dashboard useful—instructors remained focused on a specific class, but students drove the conversation to a much broader scope at the major or university level but in a discipline-specific manner.  Such practices that select relevant data and develop models with learners and teachers instead of for learners and teachers should better inform development of and, ultimately, sustainable use of learning analytics-based models and dashboards.


Student and instructor perspectives; student data; engineering disciplinary context

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Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers & Education, 62, 130-148.

Arnold, K. (2010). Signals: Applying academic analytics. EDUCAUSE Review, 1-9.

Baepler, P., & Murdoch, C.J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), 1-9.

Baer, L, & Campbell, J. (2012). From metrics to analytics, reporting to action: Analytics’ role in changing the learning environment. In Game changers: Education and information technologies. D.G. Oblinger (Ed.). EDUCAUSE.

Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gašević, D., Mulder, R., Williams, D., Dawson, S., Lockyer, L. (2016). A conceptual framework linking learning design with learning analytics. In Proceedings of the 6th Annual Conference on Learning Analytics and Knowledge, Edinburgh, United Kingdom.

Bientkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Office of Educational Technology, U.S. Department of Education.

Brown, M. (2015). Six trajectories for digital technology in higher education. EDUCAUSE Review, Jul/Aug 2015.

Buckingham Shum, S. J., Gašević, D., & Ferguson, R. (2012). In Proceedings of the 2nd International Conference on Learning Analytics and Lnowledge: April 29 - May 2, 2012, Vancouver, British Columbia, Canada.

Campbell, J.P., DeBlois, P.B., & Oblinger, D.G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40-57.

Chatti, M.A., Dyckhof, A.L., Schroeder,U., & Thues, H. (2013) A reference model for learning analytics, International Journal of Technology Enhanced Learning, 4, (5-6), 318-331.

Corrin, L., & de Barba, P. (2014). Exploring students’ interpretation of feedback delivered through learning analytics dashboards. In Proceedings of the 31st Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ascilite 2014), Dunedin.

Corrin, L., & de Barba, P. (2015). How do students interpret feedback delivered via dashboards? In Proceedings of the 5th International Conference on Learning Analytics and Knowledge, Poughkeepsie.

Creswell, J. W. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, CA: SAGE.

Duval, E., Klerkx, J., Verbert, K., Nagel, T., Govaerts, S., Parra Chico, G.A., Santos, J.L., & Vandeputte, B. (2012). Learning dashboards and learnscapes. Educational Interfaces, Software, and Technology, May, 1-5.

Feng, M., Krumm, A. E., Bowers, A. J., & Podkul, T. (2016, April). Elaborating data intensive research methods through researcher-practitioner partnerships. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 540-541). ACM.

Fives, H., Barnes, N., Bratkovich, M.O., Dacey, C.M. (2016, April), Classroom Level Data-Use: Mapping the Sub and Microprocesses of One Teacher’s Practice. In annual meeting of the American Educational Research Association, Washington, D.C.

Goldstein, P.J. (2005). Academic analytics: The uses of management information and technology in higher education. EDUCAUSE Center for Applied Research.

Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42-57.

Hora, M T. & Ferrare, J. J. (2013). Instructional Systems of Practice: A Multidimensional Analysis of Math and Science Undergraduate Course Planning and Classroom Teaching. Journal of the Learning Sciences, 22. pp. 212–257.

Jones, B. D. (2009). Motivating Students to Engage in Learning: The MUSIC Model of Academic Motivation. International Journal of Teaching and Learning in Higher Education, 21(2), 272-285.

Kelly, J.T., Kendrick, M.M., Newgent, R.A., & Lucas, C.J. (2007) Strategies for student transition to college: A proactive approach, College Student Journal, 41 (4).

Krumm, A.E., Waddington, R.J., Lonn, S., & Teasley, S.D. (2012). Increasing academic success in undergraduate engineering education using learning analytics: A design-based research project. Paper presented at the annual meeting of the American Educational Research Association, Vancouver.

Leydens, J. A., Moskal, B. M., & Pavelich, M. J. (2004). Qualitative methods used in the assessment of engineering education. Journal of Engineering Education, 93(1), 65-72.

Madhavan, K., Richey, M.C., (2016) Problems in Big Data Analytics in Learning, Journal of Engineering Education, 105 (1), 6-14.

Mandinach, E. B., Honey, M., & Light, D. (2006, April). A theoretical framework for data-driven decision making. In annual meeting of the American Educational Research Association, San Francisco, CA.

Marsh, J. A. (2012). Interventions promoting educators’ use of data: Research insights and gaps. Teachers College Record, 114(11), 1-48.

McKay, R., Miller, K., & Tritz, J. (2012). What to do with actionable intelligence: E2 coach as an intervention engine. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. pp. 88-91.

McPherson, J., Tong, H.L., Fatt, S.J., & Liu, D.Y.T. (2016). Student perspectives on data provision and use: Starting to unpack disciplinary differences. In Proceedings of the 6th Annual International Conference on Learning Analytics and Knowledge, Edinburgh, United Kingdom.

Nelson-Laird, T. F., Shoup, R., Kuh, G. D., and Schwarz, M.J. (2007). The Effects of Discipline on Deep Approaches to Student Learning and College Outcomes. Research in Higher Education, 49, pp. 469–494.

Neumann, R., Parry, S., & Becher, T. (2002). Teaching and learning in their disciplinary contexts: A conceptual analysis. Studies in Higher Education, 27, pp. 405–417.

Newland, B., Martin, L., & Ringan, N. (2015). Learning analytics in UK HE 2015: A HeLF survey report: HeLF Heads of e-learning forum. Available:

Patton, M. Q. (2002). Qualitative Research & Evaluation Methods: Thousand Oaks: Sage Publications.

Robson, C. (2011). Real world research. West Sussex, UK: John Wiley & Sons.

Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state-of-the-art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601-618.

Schunk, D. H., & Zimmerman, B. J. (1998). Self-regulated learning: From teaching to self-reflective practice. New York: Guilford Press.

Santos, J.L., Govaerts, S., Verbert, K., & Duval, E. (2012). Goal-oriented visualizations of activity tracking: a case study with engineering students. 2nd International Conference on Learning Analytics & Knowledge, Vancouver.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. doi:10.1177/0002764213498851

Smart, J. C., & Ethington, C. A. (1995). Disciplinary and institutional differences in undergraduate education goals. New Directions for Teaching and Learning, 64, pp. 49-57.

Stark, J. S., Lowther, M. A., Bentley, R. J., Ryan, M. P., Martens , G. G., Genthon, M. L., & others (1990). Planning introductory college courses: Influences on faculty. Ann Arbor, MI: University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning. (ERIC Document Reproduction Service No.ED330277)

U.S. Department of Education (2015). The President’s fiscal year 2015 budget request for education.



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