OnTask: Delivering Data-Informed, Personalized Learning Support Actions

Kathryn Bartimote
Simon Buckingham Shum
Shane Dawson
Jing Gao
Dragan Gašević
Steve Leichtweis
Danny Liu
Roberto Martínez-Maldonado
Negin Mirriahi
Adon Christian Michael Moskal
Jurgen Schulte
George Siemens
Lorenzo Vigentini


The learning analytics community has matured significantly over the past few years as a middle space where technology and pedagogy combine to support learning experiences. To continue to grow and connect these perspectives, research needs to move beyond the level of basic support actions. This means exploring the use of data to prove richer forms of actions, such as personalized feedback, or hybrid approaches where instructors interpret the outputs of algorithms and select an appropriate course of action. This paper proposes the following three contributions to connect data extracted from the learning experience with such personalized student support actions: 1) a student–instructor centred conceptual model connecting a representation of the student information with a basic set of rules created by instructors to deploy Personalized Learning Support Actions (PLSAs); 2) a software architecture based on this model with six categories of functional blocks to deploy the PLSAs; and 3) a description of the implementation of this architecture as an open-source platform to promote the adoption and exploration of this area.

Full Text:



Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 26(2), 600-614. doi:10.1007/s40593-016-0105-0

Brooks, C., Greer, J., & Gutwin, C. (2014). The Data-Assisted Approach to Building Intelligent Technology-Enhanced Learning Environments. In J. A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 123-156). New York: Springer. doi:10.1007/978-1-4614-3305-7_7

Bull, S., & Kay, J. (2016). SMILI☺: a Framework for Interfaces to Learning Data in Open Learner Models, Learning Analytics and Related Fields. International Journal of Artificial Intelligence in Education, 26(1), 293-331.

Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., . . . Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, 10(1).

Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In B. K. Daniel (Ed.), Big Data and Learning Analytics in Higher Education (pp. 195-219). Switzerland: Springer. doi:10.1007/978-3-319-06520-5_12

Clow, D. (2012). The Learning Analytics Cycle: Closing the Loop Effectively. Paper presented at the International Conference on Learning Analytics and Knowledge, New York, NY, USA. doi:10.1145/2330601.2330636

Colvin, C., Rogers, T., Wade, A., Dawson, S., Gašević, D., Buckingham Shum, S., . . . Fisher, J. (2016). Student retention and learning analytics: a snapshot of Australian practices and a framework for advancement. Caberra, ACT: Australian Government Office for Learning and Teaching.

Cooper, A. (2014). Learning Analytics Interoperability-The Big Picture In Brief. Learning Analytics Community Exchange.

Corrin, L., & de Barba, P. (2014). Exploring students' interpretation of feedback delivered through learning analytics dashboards. Paper presented at the ASCILITE conference, Dunedin, New Zealand.

Corrin, L., & de Barba, P. (2015). How Do Students Interpret Feedback Delivered via Dashboards? Paper presented at the International Conference on Learning Analytics and Knowledge, Poughkeepsie, NY, USA. doi:10.1145/2723576.2723662

Crossley, S. A., Kyle, K., & McNamara, D. S. (2015). The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion. Behavior Resesearch Methods, 1-11. doi:10.3758/s13428-015-0651-7

Echeverria, V., Martinez-Maldonado, R., Granda, R., Chiluiza, K., Conati, C., & Shum, S. B. (2018). Driving data storytelling from learning design. Paper presented at the Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18. doi:10.1145/3170358.3170380

Evans, C. (2013). Making Sense of Assessment Feedback in Higher Education. Review of Educational Research, 83(1), 70-120. doi:10.3102/0034654312474350

Experience API. (2016). Github repository. Retrieved from https://github.com/adlnet/-xAPI-Spec/blob/master/xAPI.md

Ferguson, R., Macfadyen, L., Clow, D., Tynan, B., Alexander, S., & Dawson, S. (2014). Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption. Journal of Learning Analytics, 1(3), 120-144.

Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84. doi:10.1016/j.iheduc.2015.10.002

Gašević, D., Dawson, S., & Siemens, G. (2015). Let's not forget: Learning analytics are about learning. TechTrends, 59(1), 64-75.

Hattie, J., & Timperley, H. (2007). The Power of Feedback. Review of Educational Research, 77(1), 81-112. doi:10.3102/003465430298487

Hegazi, M. O., & Abugroon, M. A. (2016). The State of the Art on Educational Data Mining in Higher Education. International Journal of Computer Trends and Technology (IJCTT), 31, 46–56.

IMS Consortium. (2016). Caliper Analytics. Retrieved from http://www.imsglobal.org/activity/caliperram

Jayaprakash, S. M., Moody, E. W., Eitel, J. M., Regan, J. R., & Baron, J. D. (2014). Early Alert of Academically At-Risk Students : An Open Source Analytics Initiative. Journal of Learning Analytics, 1, 6-47.

Kahn, I., & Pardo, A. (2016). Data2U: Scalable Real time Student Feedback in Active Learning Environments. Paper presented at the International Conference on Learning Analytics and Knowledge, Edinburgh, UK. doi:10.1145/2883851.2883911

Laurillard, D. (2013). Rethinking university teaching: A conversational framework for the effective use of learning technologies (2nd ed.): Routledge.

Lewkow, N., Zimmerman, N., Riedesel, M., & Essa, A. (2015). Learning Analytics Platform, towards an open scalable streaming solution for education. Paper presented at the International Conference on Educational Data Mining, Madrid, Spain.

Liu, D. Y. T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017). Data-driven Personalization of Student Learning Support in Higher Education. In A. Peña-Ayala (Ed.), Learning analytics: Fundaments, applications, and trends: A view of the current state of the art: Springer. doi:10.1007/978-3-319-52977-6_5

Liu, R., Patel, R., & Koedinger, K. R. (2016). Modeling common misconceptions in learning process data. Paper presented at the International Conference on Learning Analytics and Knowledge, Edinburgh, UK. doi:10.1145/2883851.2883967

Lonn, S., Aguilar, S. J., & Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90-97. doi:10.1016/j.chb.2014.07.013

Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901-918. doi:10.1037/a0037123

Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149-163.

McDonald, J., Liu, D. Y. T., Moskal, A., Zeng, R., Blumenstein, M., Gunn, C., . . . Pardo, A. (2016). Cross-institutional collaboration to support student engagement: SRES version 2. Paper presented at the ASCILITE Conference, Adelaide, Australia.

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. Paper presented at the International Conference on Learning Analytics and Knowledge, Edinburgh, UK. doi:10.1145/2883851.2883945

Nicol, D. (2010). From monologue to dialogue: improving written feedback processes in mass higher education. Assessment & Evaluation in Higher Education, 35(5), 501-517. doi:10.1080/02602931003786559

Nicol, D., Thomson, A., & Breslin, C. (2013). Rethinking feedback practices in higher education: a peer review perspective. Assessment & Evaluation in Higher Education, 39(1), 102-122. doi:10.1080/02602938.2013.795518

Niculescu, C. (2016). Intelligent Tutoring Sytems - Trends on Design, Development and Deployment. Paper presented at the International Scientific Conference eLearning and Software in Education, Bucharest. doi:10.12753/2066-026X-16-218

Nye, B. D. (2014). Intelligent Tutoring Systems by and for the Developing World: A Review of Trends and Approaches for Educational Technology in a Global Context. International Journal of Artificial Intelligence in Education, 25(2), 177-203. doi:10.1007/s40593-014-0028-6

Obrenovic, Z., & Gasevic, D. (2008). End-User Service Computing: Spreadsheets as a Service Composition Tool. IEEE Transactions on Services Computing, 1(4), 229-242. doi:10.1109/tsc.2008.16

Pardo, A., Jovanović, J., Dawson, S., Gašević, D., & Mirriahi, N. (2017). Using Learning Analytics to Scale the Provision of Personalised Feedback. British Journal of Educational Technology. doi:10.1111/bjet.12592

Pelánek, R., Rihák, J., & Papoušek, J. (2016). Impact of data collection on interpretation and evaluation of student models. Paper presented at the International Conference on Learning Analytics and Knowledge, Edinburgh, UK. doi:10.1145/2883851.2883868

Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27. doi:10.1002/widm.1075

Romero Zaldívar, V. A., Pardo, A., Burgos, D., & Delgado Kloos, C. (2012). Monitoring Student Progress Using Virtual Appliances: A Case Study. Computers & Education, 58(4), 1058-1067. doi:10.1016/j.compedu.2011.12.003

Scaffidi, C., Shaw, M., & Myers, B. (2005). Estimating the Number of End Users and End User Programmers. Paper presented at the IEEE Symposium on Visual Languages and Human-Centric Computing, Dallas, TX, USA.

Sclater, N., Peasgood, A., & Mullan, J. (2016). Learning Analytics in Higher Education: A review of UK and international practice. United Kingdom: Joint Information Systems Committee (JISC).

Shacklock, X. (2016). From bricks to clicks. The potential of data and analytics in higher education. London, UK: Higher Education Commission.

Shamah, D., & Ohlsen, S. (2013). Student Success Plan: Constructing an evidence-based student support system that promotes college completion. Portland, Oregon: Gateway to College National Network.

Shum, S. B., Knight, S., McNamara, D., Allen, L., Bektik, D., & Crossley, S. (2016). Critical perspectives on writing analytics. Paper presented at the International Conference on Learning Analytics and Knowledge, Edingburg, UK. doi:10.1145/2883851.2883854

Siemens, G., Gašević, D., Haythornthwaite, C., Dawson, S., Buckingham Shum, S., Ferguson, R., . . . Baker, R. (2011). Open Learning Analytics: an integrated & modularized platform: Society for Learning Analytics and Research.

Tinto, V. (2006). Research and Practice of Student Retention: What Next? Journal of College Student Retention, 8(1), 1-19.

Tinto, V. (2012). Completing college: Rethinking institutional action. Chicago: University of Chicago Press.

Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning Analytics Dashboard Applications. American Behavioral Scientist, 57(10), 1500-1509. doi:10.1177/0002764213479363

West, D., Huijser, H., Lizzio, A., Toohey, D., Miles, C., Searle, B., & Bronnimann, J. (2015). Learning Analytics: Assisting Universities with Student Retention, Final Report (Part 1): Australian Government Office for Learning and Teaching.

Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. Paper presented at the International Conference on Learning Analytics and Knowledge, Indianapolis, IN, USA.

Wise, A. F., Vytasek, J. M., Hausknecht, S., & Zhao, Y. (2016). Developing Learning Analytics Design Knowledge in the "Middle Space": The Student Tunning Model and Align Design Framework for Learning Analytics Use. Online Learning Journal, 20(2).

Wright, M. C., McKay, T., Hershock, C., Miller, K., & Tritz, J. (2014). Better Than Expected: Using Learning Analytics to Promote Student Success in Gateway Science. Change: The Magazine of Higher Learning, 46(1), 28-34. doi:10.1080/00091383.2014.867209


DOI: https://doi.org/10.18608/jla.2018.53.15

Share this article: