Main Article Content
This article presents the case of the Learning Analytics Architecture (LARC) dataset, a collaborative effort at the University of Michigan to develop a common and extensible tool using administrative data and designed primarily for learning analytics researchers to investigate enrolled students’ academic careers, demographics, and related teaching and learning outcomes. The institutional context prior to the creation of the dataset and the rationale, design, development, and maintenance involved in creating LARC are all detailed. Also discussed are the procedures for access, documentation, and ensuring the continued usability and relevance of the dataset for a growing learning analytics and data science research community. The authors conclude the case description with recommendations for institutions seeking to replicate this effort.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
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. https://dx.doi.org/10.20429/ijsotl.2010.040217
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. https://er.educause.edu/articles/2007/7/academic-analytics-a-new-tool-for-a-new-era
Gray, J., & Szalay, A. (2004). Where the Rubber Meets the Sky: Bridging the Gap Between Databases and Science. Technical Report no. MSR-TR-2004-110. Redmond, WA, USA: Microsoft Research. https://arxiv.org/pdf/cs/0502011.pdf
Lonn, S. (2017). The LARC project: Normalizing student data for IR and learning analytics. Presentation at the Association for Institutional Research Forum, 30 May–1 June 2017, Washington, D.C. http://airforum2017.azurewebsites.net/SessionDetail.aspx?id=89100
Lonn, S., & Auerbach, G. (2018). The data is flat: Enabling learning analytics research using institutional student data. Presentation at the Higher Education Data Warehousing Forum, 8–10 April 2018, Corvallis, OR, USA. https://hedw.org/hedwpresentation/2018-the-data-is-flat-enabling-learning-analytics-research-using-institutional-student-data/
Lonn, S., McKay, T. A., & Teasley, S. D. (2017) Cultivating institutional capacities for learning analytics. In J. Zilvinskis & V. Borden (Eds.), New Directions for Higher Education, no. 179 (pp. 53–63). San Francisco, CA, USA: Jossey-Bass. https://dx.doi.org/10.1002/he.20243
Long, P., and Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40. https://er.educause.edu/~/media/files/article-downloads/erm1151.pdf
Lupton, D. (2016). The Quantified Self: A Sociology of Self-Tracking. Cambridge: Polity Press. https://dx.doi.org/10.1111/1467-9566.12495
Nam, S., Lonn, S., Brown, T., Davis, C. S., & Koch, D. (2014). Customized course advising: Investigating engineering student success with incoming profiles and patterns of concurrent course enrollment. In Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 16–25). New York: ACM. https://dx.doi.org/10.1145/2567574.2567589
Norris, D., Baer, L., & Offerman, M. (2009). A national agenda for action analytics. Paper presented at the National Symposium on Action Analytics, 21–23 September, 2009, Minneapolis, MN, USA. http://lindabaer.efoliomn.com/uploads/settinganationalagendaforactionanalytics101509.pdf
Pink, S., Ruckenstein, M., Willim, R., & Duque, M. (2018). Broken data: Conceptualising data in an emerging world. Big Data & Society, 5(1), 1–13. https://dx.doi.org/10.1177/2053951717753228
Prinsloo, P., Slade, S., & Khalil, M. (2018). Stuck in the middle? Making sense of the impact of micro, meso and macro institutional, structural and organisational factors on implementing learning analytics. In Proceedings of the European Distance and E-Learning Network Annual Conference, 17–20 June 2018, Genova, Italy (pp. 326–334). Budapest, Hungary: European Distance and E-Learning Network. https://www.researchgate.net/publication/325870750
Shultz, G. V., Winschel, G. A., & Gottfried, A. (2015). Impact of general chemistry on student achievement and progression to subsequent chemistry courses: A regression discontinuity analysis. Journal of Chemical Education, 92(9), 1449–1455. https://dx.doi.org/10.1021/acs.jchemed.5b00209
Stonebraker, M., Frew, J., Gardels, K., & Meredith, J. (1993). The Sequoia 2000 storage benchmark. ACM SIGMOD Record, 22(2), 2–11. New York: ACM. https://dx.doi.org/10.1145/170036.170038
Szalay, A. S., Kunszt, P. Z., Thakar, A., Gray, J., Slutz, D., & Brunner, R. J. (2000). Designing and mining multi-terabyte astronomy archives: The Sloan Digital Sky Survey. ACM SIGMOD Record, 29(2), 451–462. https://dx.doi.org/10.1145/335191.335439
York, D. G., Adelman, J., Anderson Jr., J. E., Anderson, S. F., Annis, J., Bahcall, N. A., ... & Boroski, W. N. (2000). The Sloan Digital Sky Survey: Technical summary. The Astronomical Journal, 120(3), 1579–1587. https://dx.doi.org/10.1086/301513