Developing a Growth Learning Data Mindset
A Secondary School Approach to Creating a Culture of Data Driven Improvement
Keywords:data mindset, growth learning, secondary school, learning analytics adoption, practical report
While Learning Analytics (LA) have gained momentum in higher education, there are still few examples of application in the school sector. Even fewer cases are reported of systematic, organizational adoption to drive the support of student learning trajectories that includes teachers, pastoral leaders, and academic managers. This paper presents one such case — at the intersection of praxis, governance, and evaluation — from a practitioner perspective. The paper describes the added value of data-driven approaches to create a culture of improvement in students and teachers in a comprehensive coeducational independent day school in Sydney. Evaluating the work done over the past five years to develop LA dashboards, the authors reflect on the process, the inspirations coming from theory, and the impact of the dashboards in the secondary school context. The data presented is not experimental in nature but supplies tangible evidence for the systematic evaluation scaffolded using the SHEILA policy framework. The main contribution of the paper is a practical demonstration of how managers in a secondary school drew from existing literature and observed data to 1) reflect on the adoption of LA in schools and 2) connect the dots between theory and practice to support teachers grappling with the trajectories of student learning and development, thus encouraging students to self-regulate their learning
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. https://doi.org/10/f4rw4b
Allison, S., & Harbour, M. (2009). The coaching toolkit: A practical guide for your school. Thousand Oaks, CA: Sage Publications.
Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ʼ12), 29 April–2 May 2012, Vancouver, BC, Canada (pp. 267–270). New York: ACM. https://doi.org/10/ggwgdm
Arroway, P., Morgan, G., O’Keefe, M., & Yanosky, R. (2016). Learning analytics in higher education. Louisville, CO: Educause Center for Analysis and Research.
Bass, R. (1999). The scholarship of teaching: What’s the problem? Inventio: Creative Thinking about Learning and Teaching, 1(1). Retrieved from http://web.archive.org/web/20120206142123/http://doit.gmu.edu//Archives/feb98/rbass.htm
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, DC: US Department of Education, Office of Educational Technology.
Boekaerts, M. (1997). Self-regulated learning: A new concept embraced by researchers, policy-makers, educators, teachers, and students. Learning and Instruction, 7(2), 161–186. https://doi.org/10/djhtk4
Boyer, E. (1990). Scholarship reconsidered: Priorities of the professionals. San Francisco, CA: Jossey-Bass.
Bray, B., & McClaskey, K. (2015). Making learning personal: The what, who, wow, where, and why. Thousand Oaks, CA: Corwin Press.
Burnette, J. L., O’Boyle, E. H., VanEpps, E. M., Pollack, J. M., & Finkel, E. J. (2013). Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139(3), 655–701. https://doi.org/10/f4v2f8
Catlin, K. S., Lewan, G. J., & Perignon, B. J. (1999). Increasing student engagement through goal-setting, cooperative learning & student choice. Retrieved from https://eric.ed.gov/?id=ED433100
Charleer, S., Klerkx, J., & Duval, E. (2014). Learning dashboards. Journal of Learning Analytics, 1(3), 199–202. https://doi.org/10/ghbgqj
Corrigan, O., Smeaton, A. F., Glynn, M., & Smyth, S. (2015). Using educational analytics to improve test performance. In G. Conole, T., Klobučar, C., Rensing, J., Konert & E. Lavoué (Eds.), Design for teaching and learning in a networked world (pp. 42–55). EC-TEL 2015. Lecture Notes in Computer Science, vol 9307. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-24258-3_4
Cruz, H. L., & Zambo, D. (2013). Student data portfolios give students the power to see their own learning. Middle School Journal, 44(5), 40–47. https://doi.org/10/ghbgqw
Dawson, S., Poquet, O., Colvin, C., Rogers, T., Pardo, A., & Gašević, D. (2018). Rethinking learning analytics adoption through complexity leadership theory. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia (pp. 236–244). New York: ACM. https://doi.org/10/ghbgq4
Department of Education and Training. (2018, March). Through growth to achievement: Report of the review to achieve educational excellence in Australian schools. Canberra: Australian Government. Retrieved from https://www.dese.gov.au/quality-schools-package/resources/through-growth-achievement-report-review-achieve-educational-excellence-australian-schools
Dollinger, M., & Lodge, J. (2019). What learning analytics can learn from students as partners. Educational Media International, 56(3), 218–232. https://doi.org/10/gh4vrq
Dollinger, M., & Lodge, J. M. (2018). Co-creation strategies for learning analytics. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia (pp. 97–101). New York: ACM. https://doi.org/10/ghdg8f
Donoghue, G. M., & Horvath, J. C. (2016). Translating neuroscience, psychology and education: An abstracted conceptual framework for the learning sciences. Cogent Education, 3(1), 1267422. https://doi.org/10/gg9ccf
Dweck, C. (2012). Mindset: Changing the way you think to fulfil your potential. London, UK: Hachette UK.
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256–273. https://doi.org/10/g9b
Ferguson, R., Clow, D., Macfadyen, L., Essa, A., Dawson, S., & Alexander, S. (2014). Setting learning analytics in context: Overcoming the barriers to large-scale adoption. Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 251–253). New York: ACM. https://doi.org/10/gfsnxc
Freeman, A., Becker, S. A., & Cummins, M. (2017). NMC/CoSN Horizon Report: 2017 K–12 Edition. The New Media Consortium. Retrieved from http://www.learntechlib.org/p/182003/
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71. https://doi.org/10/gfxd5s
Goh, T.-T., Seet, B.-C., & Chen, N.-S. (2012). The impact of persuasive SMS on students’ self-regulated learning. British Journal of Educational Technology, 43(4), 624–640. https://doi.org/10/c3qbt4
Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493-503. https://doi.org/10/bv8qnq
Goodman, F. R., Disabato, D. J., Kashdan, T. B., & Kauffman, S. B. (2018). Measuring well-being: A comparison of subjective well-being and PERMA. The Journal of Positive Psychology, 13(4), 321–332. https://doi.org/10/gfvrkt
Goss, P., & Hunter, J. (2015). Targeted teaching: How better use of data can improve student learning. Grattan Institute. Retrieved from https://grattan.edu.au/report/targeted-teaching-how-better-use-of-data-can-improve-student-learning/
Hadwin, A., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In P. A. Alexander, D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance, 2nd ed. (pp. 83–106). Abingdon-on-Thames, UK: Routledge/Taylor & Francis Group.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Abingdon-on-Thames, UK: Routledge.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Hattie, J., & Yates, G. (2014). Visible learning and the science of how we learn. Abingdon-on-Thames, UK: Routledge/Taylor & Francis Group.
Henderson, M., Ajjawi, R., Boud, D., & Molloy, E. (Eds.) (2019). The impact of feedback in higher education: Improving assessment outcomes for learners. Cham, Switzerland: Springer Nature. https://doi.org/10.1007/978-3-030-25112-3
Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., & Mangafa, C. (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 50(6), 3064–3079. https://doi.org/10/ghgv72
Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education, 45, 100725. https://doi.org/10/gh2p5m
Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y.-S., Muñoz-Merino, P. J., Broos, T., Whitelock-Wainwright, A., Gašević, D., & Pérez-Sanagustín, M. (2020). Towards learning analytics adoption: A mixed methods study of data-related practices and policies in Latin American universities. British Journal of Educational Technology, 51(4), 915–937. https://doi.org/10/gjkp3n
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin & M. Pérez-Sanagustín (Eds.), Data driven approaches in digital education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12-15, 2017, Proceedings (pp. 82–96). Cham, Switzerland: Springer International Publishing AG. Lecture Notes in Computer Science (LNCS) Vol. 10474. https://doi.org/10.1007/978-3-319-66610-5_7
Jivet, I., Scheffel, M., Schmitz, M., Robbers, S., Specht, M., & Drachsler, H. (2020). From students with love: An empirical study on learner goals, self-regulated learning and sense making of learning analytics in higher education. The Internet and Higher Education, 47, 100758. https://doi.org/10/gg4jzt
Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia (pp. 31–40). New York: ACM. https://doi.org/10.1145/3170358.3170421
Kennedy, G., Corrin, L., Lockyer, L., Dawson, S., Williams, D., Mulder, R., Khamis, S., & Copeland, S. (2014). Completing the loop: Returning learning analytics to teachers. In B. Hegarty, J. McDonald & S-K. Loke (Eds.), Proceedings of the 31st Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE 2014), 23–26 November 2014, Dunedin, New Zealand (pp. 436–440). Australasian Society for Computers in Learning in Tertiary Education.
Knight, S., Gibson, A., & Shibani, A. (2020). Implementing learning analytics for learning impact: Taking tools to task. The Internet and Higher Education, 45, 100729. https://doi.org/10/gg4j2f
Krueger, R. A. (2014). Focus groups: A practical guide for applied research. Thousand Oaks, CA: SAGE Publications.
Lim, L., Dawson, S., Joksimovic, S., & Gašević, D. (2019a). Exploring students’ sensemaking of learning analytics dashboards: Does frame of reference make a difference? Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK ’19), 4–8 March 2019, Tempe, AZ, USA (pp. 250–259). New York: ACM. https://doi.org/10/ghbgqk
Lim, L.-A., Gentili, S., Pardo, A., Kovanović, V., Whitelock-Wainwright, A., Gašević, D., & Dawson, S. (2019b). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 72, 101202. https://doi.org/10/gf82qq
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. Studies in Systems, Decision and Control, vol. 94 (pp. 143–169). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-52977-6_5
Lodge, J. M., Horvath, J. C., & Corrin, L. (Eds.). (2019). Learning analytics in the classroom: Translating learning analytics research for teachers. Abingdon-on-Thames, UK: Routledge.
Macfadyen, L., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149–163.
Martin, P. A. (2010). Building classroom success: Eliminating academic fear and failure. New York, NY: Continuum International Publishing Group.
Matcha, W., Uzir, N. A., Gašević, D., & Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226–245. https://doi.org/10/ghbgqq
Merceron, A., Blikstein, P., & Siemens, G. (2015). Learning analytics: From big data to meaningful data. Journal of Learning Analytics, 2(3), 4–8. https://doi.org/10.18608/jla.2015.23.2
Molenaar, I., & Knoop-van Campen, C. (2017). Teacher dashboards in practice: Usage and impact. In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin & M. Pérez-Sanagustín (Eds.), Data driven approaches in digital education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12-15, 2017, Proceedings (pp. 125–138). Cham, Switzerland: Springer International Publishing AG. Lecture Notes in Computer Science (LNCS) Vol. 10474. https://doi-org.proxy.bnl.lu/10.1007/978-3-319-66610-5_10
Muller, J. Z. (2018). The tyranny of metrics. Princeton, NJ: Princeton University Press.
Norris, D. M., & Baer, L. L. (2013). Building organizational capacity for analytics. Educause Learning Initiative, 2013, 5–58. Retrieved from https://library.educause.edu/-/media/files/library/2013/2/pub9012-pdf.pdf
Nussbaum, A. D., & Dweck, C. S. (2008). Defensiveness versus remediation: Self-theories and modes of self-esteem maintenance. Personality and Social Psychology Bulletin, 34(5), 599–612. https://doi.org/10.1177/0146167207312960
Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: When measurement and intervention come hand in hand. Scandinavian Journal of Educational Research, 60(6), 723–735. https://doi.org/10.1080/00313831.2015.1066436
Pardo, A. (2018). A feedback model for data-rich learning experiences. Assessment & Evaluation in Higher Education, 43(3), 428–438. https://doi.org/10.1080/02602938.2017.1356905
Pardo, A., Bartimote, K., Shum, S. B., Dawson, S., Gao, J., Gašević, D., Leichtweis, S., Liu, D., Martínez-Maldonado, R., Mirriahi, N., Moskal, A. C. M., Schulte, J., Siemens, G., & Vigentini, L. (2018). OnTask: Delivering data-informed, personalized learning support actions. Journal of Learning Analytics, 5(3), 235–249. https://doi.org/10.18608/jla.2018.53.15
Pincham, L. (2006). Individualized goal setting for at-risk students. In National Middle School Association (NJ3), 10(1), 39–40. National Middle School Association.
Preece, J., Sharp, H., & Rogers, Y. (2015). Interaction design: Beyond human–computer interaction. Hoboken, NJ: John Wiley & Sons.
Prieto-Alvarez, C. G., Martinez-Maldonado, R., & Anderson, T. (2018). Co-designing learning analytics tools with learners. In J. M. Lodge, J. C. Horvath & L. Corrin (Eds.), Learning analytics in the classroom: Translating learning analytics research for teachers (pp. 93–110). Abingdon-on-Thames, UK: Routledge https://doi.org/10/ghdg8d
Sahin, M., & Ifenthaler, D. (Eds.). (2021). Visualizations and dashboards for learning analytics. Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-81222-5
Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A. (2020). Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation. Computers in Human Behavior, 107, 105512. https://doi.org/10.1016/j.chb.2018.05.004
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 30–32.
Stober, D. R., & Grant, A. M. (Eds.) (2010). Evidence based coaching handbook: Putting best practices to work for your clients. Hoboken, NJ: John Wiley & Sons.
Suthers, D., & Verbert, K. (2013). Learning analytics as a “middle space.” Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK ’13), 8–12 April 2013, Leuven, Belgium (pp. 1–4). New York: ACM. https://doi.org/10.1145/2460296.2460298
Teasley, S. D. (2017). Student facing dashboards: One size fits all? Technology, Knowledge and Learning, 22(3), 377–384. https://doi.org/10.1007/s10758-017-9314-3
Thaler, R. H. (2018). Nudge, not sludge. Science, 361(6401), 431–431. https://doi.org/10.1126/science.aau9241
Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. New York, NY: Penguin.
Tsai, Y.-S., Kovanović, V., & Gašević, D. (2021). Connecting the dots: An exploratory study on learning analytics adoption factors, experience, and priorities. The Internet and Higher Education, 50, 100794. https://doi.org/10/gjkp3t
Tsai, Y.-S., Moreno-Marcos, P. M., Jivet, I., Scheffel, M., Tammets, K., Kollom, K., & Gašević, D. (2018). The SHEILA framework: Informing institutional strategies and policy processes of learning analytics. Journal of Learning Analytics, 5(3), 5–20. https://doi.org/10.18608/jla.2018.53.2
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509. https://doi.org/10.1177/0002764213479363
Vigentini, L., Liu, D. Y. T., Arthars, N., & Dollinger, M. (2020). Evaluating the scaling of a LA tool through the lens of the SHEILA framework: A comparison of two cases from tinkerers to institutional adoption. The Internet and Higher Education, 45, 100728. https://doi.org/10.1016/j.iheduc.2020.100728
West, D., Huijser, H., & Heath, D. (2019). Blurring the boundaries: Developing leadership in learning analytics. In J. M. Lodge, L. Corrin, & J. Cooney Horvath (Eds.), Learning analytics in the classroom: Translating learning analytics research for teachers, 1st ed. (pp. 267–283). Abingdon-on-Thames, UK: Routledge.
West, D., Luzeckyj, A., Toohey, D., Vanderlelie, J., & Searle, B. (2020). Do academics and university administrators really know better? The ethics of positioning student perspectives in learning analytics. Australasian Journal of Educational Technology, 36(2), 60–70. https://doi.org/10.14742/ajet.4653
Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). Cambridge, MA: Academic Press. https://doi.org/10.1016/B978-012109890-2/50045-7
Young, J., & Mendizabel, E. (2009). Helping researchers become policy entrepreneurs, Briefing Paper 53. London, UK: Overseas Development Institute. Retrieved from https://cdn.odi.org/media/documents/1730.pdf
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2
How to Cite
Copyright (c) 2022 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Authors who publish with this journal agree to the following terms:
- 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).