A Collaborative Approach to Sharing Learner Event Data
DOI:
https://doi.org/10.18608/jla.2021.7375Keywords:
data interoperability, data products, decision-making, instructional supportAbstract
This paper describes a collaboration organized around exchanging data between two technological systems to support teachers’ instructional decision-making. The goals of the collaboration among researchers, technology developers, and practitioners were not only to support teachers’ instructional decision-making but also to document the challenges and opportunities associated with bringing together data from instruction- and assessment-focused technologies. The approach described in this paper illustrates the potential importance of anchoring data products that combine data between two systems in the needs of teachers as well as aligning the content that students learn and are assessed on between systems. The increasing presence of data standards has made sharing complex data increasingly more feasible. The example collaboration described in this paper demonstrates the role that non-technical activities can play in supporting the exchange and use of learner event data.
References
Allaire, J. J., Horner, J., Xie, Y., Marti, V., & Porte, N. (2019). Markdown: Render Markdown with the C Library “Sundown.” R package version 1.1. https://CRAN.R-project.org/package=markdown
Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences, 2nd ed., pp. 253–272. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.016
Bakharia, A., Kitto, K., Pardo, A., Gašević, D., & Dawson, S. (2016). Recipe for success: Lessons learnt from using xAPI within the connected learning analytics toolkit. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 378–382). New York: ACM. https://doi.org/10.1145/2883851.2883882
Connor, C. M. (2019). Using technology and assessment to personalize instruction: Preventing reading problems. Prevention Science, 20(1), 89–99. https://doi.org/10.1007/s11121-017-0842-9
Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571. https://doi.org/10.1287/mnsc.32.5.554
del Blanco, A., Serrano, A., Freire, M., Martinez-Ortiz, I., & Fernandez-Manjon, B. (2013). E-Learning standards and learning analytics: Can data collection be improved by using standard data models? Proceedings of the 2013 IEEE Global Engineering Education Conference (EDUCON 2013), 13–15 March 2013, Berlin, Germany (pp. 1255–1261). Washington, DC: IEEE Computer Society. https://doi.org/10.1109/EduCon.2013.6530268
DiCerbo, K. E., & Kobrin, J. (2016). Communicating assessment results based on learning progressions. Paper presented at the American Educational Research Association Annual Conference (AERA 2016), 8–12 April 2016, Washington, DC, USA.
Dietze, S., Yu, H. Q., Giordano, D., Kaldoudi, E., Dovrolis, N., & Taibi, D. (2012). Linked education: Interlinking educational resources and the Web of data. Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC ’12), 26–30 March 2012, Riva (Trento), Italy (pp. 366–371). New York: ACM. https://doi.org/10.1145/2245276.2245347
Halverson, R. (2010). School formative feedback systems. Peabody Journal of Education, 85(2), 130–146. https://doi.org/10.1080/01619561003685270
Hao, J., Smith, L., Mislevy, R., von Davier, A., & Bauer, M. (2016). Taming log files from game/simulation-based assessments: Data models and data analysis tools. Princeton, NJ: ETS Research Report (No. RR-16-10).
Koedinger, K. R., D’Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). Data mining and education. WIREs Cognitive Science, 6(4), 333–353. https://doi.org/10.1002/wcs.1350
Krumm, A. E., Roschelle, J., & Schank, P. (2019). Analytics as a team sport: Using cloud-based tools to support data-intensive research–practice partnerships. Workshop at the 9th International Conference on Learning Analytics and Knowledge (LAK ’19), 4–8 March 2019, Tempe, AZ, USA. https://circlcenter.org/events/teamspace-lak19/
Krumm A. E., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school: A collaborative approach to improving education. New York: Routledge.
Manso-Vazquez, M., Caeiro-Rodriguez, M., & Llamas-Nistal, M. (2018). An xAPI application profile to monitor self-regulated learning strategies. IEEE Access, 6, 42467–42481. https://doi.org/10.1109/ACCESS.2018.2860519
Martin, B., Mitrovic, A., Koedinger, K. R., & Mathan, S. (2011). Evaluating and improving adaptive educational systems with learning curves. User Modeling and User-Adapted Interaction, 21, 249–283. https://doi.org/10.1007/s11257-010-9084-2
Murphy, R., Snow, E., Mislevy, J., Gallagher, L., Krumm, A. E., & Wei, X. (2014). Blended learning report. Menlo Park, CA: SRI Education.
Owen, V. E., Ramirez, D., Salmon, A., & Halverson, R. (2014). Capturing learner trajectories in educational games through ADAGE (Assessment Data Aggregator for Game Environments): A click-stream data framework for assessment of learning in play. Paper presented at the American Educational Research Association Annual Conference (AERA 2014), 3–7 April 2014, Philadelphia, PA, USA.
Pagano, P., Candela, L., & Castelli, D. (2013). Data interoperability. Data Science Journal, 12. https://doi.org/10.2481/dsj.GRDI-004
Penuel, W. R., Confrey, J., Maloney, A., & Rupp, A. A. (2014). Design decisions in developing learning trajectories–based assessments in mathematics: A case study. Journal of the Learning Sciences, 23(1), 47–95. https://doi.org/10.1080/10508406.2013.866118
Penuel, W. R., Fishman, B. J., Haugan Cheng, B., & Sabelli, N. (2011). Organizing research and development at the intersection of learning, implementation, and design. Educational Researcher, 40(7), 331–337. https://doi.org/10.3102/0013189X11421826
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Schifter, C., Natarajan, U., Ketelhut, D. J., & Kirchgessner, A. (2014). Data-driven decision-making: Facilitating teacher use of student data to inform classroom instruction. Contemporary Issues in Technology and Teacher Education, 14(4), 419–432. https://citejournal.org/volume-14/issue-4-14/science/data-driven-decision-making-facilitating-teacher-use-of-student-data-to-inform-classroom-instruction
Sottilare, R. A., Long, R. A., & Goldberg, B. S. (2017). Enhancing the Experience Application Program Interface (xAPI) to improve domain competency modeling for adaptive instruction. Proceedings of the 4th ACM Conference on Learning @ Scale (L@S 2017), 20–21 April 2017, Cambridge, MA, USA (pp. 265–268). New York: ACM. https://doi.org/10.1145/3051457.3054001
State Educational Technology Directors Association. (2018). State education leadership interoperability: Leveraging data for academic excellence. Retrieved from http://www.setda.org/master/wp-content/uploads/2018/05/State-Leadership-Interoperability.pdf
Winne, P. H., & Hadwin, A. F. (1998) Studying as self-regulated learning. In D. J. Hacker & J. Dunlosky (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ: Erlbaum.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
TEST
