Abstract
Designing for effective and efficient pedagogical interventions and orchestration in complex technology-enhanced learning (TEL) ecosystems is an increasingly challenging issue. Learning analytics (LA) solutions are very promising for purposes of understanding and optimizing learning and the environments in which it occurs. Moreover, LA solutions may contribute to an improved evidence-based Teacher Inquiry into Student Learning. However, it is still unclear how can LA be designed to position teachers as designers of effective interventions and orchestration actions. This chapter argues for human-centered design (HCD) and orchestration of actionable learning analytics, and it proposes three HCD principles for LA solutions, i.e., agentic positioning of teachers and other stakeholders, integration of the learning design cycle and the LA design process, and reliance on educational theories to guide the LA solution design and implementation. The HCD principles are illustrated and discussed through two case studies in authentic learning contexts. This chapter aims at contributing to move the research community in relation to the design and implementation of Human-Centered Learning Analytics solutions for complex technology-enhanced learning ecosystems.
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References
Ahn, J., Campos, F., Hays, M., & DiGiacomo, D. (2019). Designing in context: Reaching beyond usability in learning analytics dashboard design. Journal of Learning Analytics, 6(2), 70–85.
Alvarez, C. P., Martinez-Maldonado, R., & Shum, S. B. (2020). LA-DECK: A card-based learning analytics co-design tool. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 63–72).
An, P., Holstein, K., d’Anjou, B., Eggen, B., & Bakker, S. (2020). The TA framework: Designing real-time teaching augmentation for K-12 classrooms. In Proceedings of CHI ’20 conference on human factors in computing systems (pp. 1–17). Retrieved from https://doi.org/10.1145/3313831.3376277.
ANZCOR. (2016). ARC Resuscitation guidelines. Melbourne: Australian and New Zealand Resuscitation Council.
Asensio-Pérez, J. I., Dimitriadis, Y., Pozzi, F., Hernández-Leo, D., Prieto, L. P., Persico, D., & Villagrá-Sobrino, S. L. (2017). Towards teaching as design: Exploring the interplay between full-lifecycle learning design tooling and teacher professional development. Computers & Education, 114, 92–116. Retrieved from http://www.sciencedirect.com/science/article/pii/S0360131517301471. https://doi.org/10.1016/j.compedu.2017.06.011.
Bannon, L. J., & Ehn, P. (2012). Design matters in participatory design. In J. Simonsen & T. Robertson (Eds.), Routledge handbook of participatory design (pp. 37–63). New York: Routledge.
Beer, C., Jones, D., & Lawson, C. (2019). The challenge of learning analytics implementation: Lessons learned. In Proceedings of the international conference on innovation, practice and research in the use of educational technologies in tertiary education (pp. 39–49).
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9.
Cohen, A., Toft Nørgård, R., & Mor, Y. (2020). Hybrid learning spaces—Design, data, didactics. British Journal of Educational Technology, 51(4), 1039–1044. Retrieved 01 July 2020, from http://doi.wiley.com/10.1111/bjet.12964.
de Quincey, E., Briggs, C., Kyriacou, T., & Waller, R. (2013). Student centred design of a learning analytics system. In Proceedings of the ninth international conference on learning analytics & knowledge (pp. 353–362).
Dillenbourg, P., Nussbaum, M., Dimitriadis, Y., & Roschelle, J. (2019). Design for classroom orchestration. Computers & Education, 69, 485–492.
Dollinger, M., Liu, D., Arthars, N., & Lodge, J. (2019). Working together in learning analytics towards the co-creation of value. Journal of Learning Analytics, 6(2), 10–26.
Echeverria, V., Martinez-Maldonado, R., & Buckingham Shum, S. (2019). Towards collaboration translucence: Giving meaning to multimodal group data. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1–16).
Echeverria, V., Martinez-Maldonado, R., Shum, S. B., Chiluiza, K., Granda, R., & Conati, C. (2018). Exploratory versus explanatory visual learning analytics: Driving teachers’ attention through educational data storytelling. Journal of Learning Analytics, 5(3), 72–97.
Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64–71.
Gašević, D., Kovanović, V., & Joksimović, S. (2017). Piecing the learning analytics puzzle: A consolidated model of a field of research and practice. Learning: Research and Practice, 3(1), 63–78.
Gasevic, D., Tsai, Y.-S., Dawson, S., & Pardo, A. (2019). How do we start? An approach to learning analytics adoption in higher education. The International Journal of Information and Learning Technology, 36(4), 342–353. Retrieved from https://doi.org/10.1108/IJILT-02-2019-0024.
Goodyear, P. (2015). Teaching as design. HERDSA Review of Higher Education, 2, 27–50. Retrieved from www.herdsa.org.au/herdsa-review-higher-education-vol-2/27-50.
Goodyear, P., & Dimitriadis, Y. (2013). In medias res: Reframing design for learning. Research in Learning Technology, 21. https://doi.org/10.3402/rlt.v21i0.19909.
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52.
Hunziker, S., Johansson, A. C., Tschan, F., Semmer, N. K., Rock, L., Howell, M. D., & Marsch, S. (2011). Teamwork and leadership in cardiopulmonary resuscitation. Journal of the American College of Cardiology, 57(24), 2381–2388.
Jørnø, R. L., & Gynther, K. (2015). What constitutes an ‘actionable insight’ in learning analytics? Journal of Learning Analytics, 5(3), 198–221. https://doi.org/10.18608/jla.2018.53.13.
Kali, Y., McKenney, S., & Sagy, O. (2015). Teachers as designers of technology enhanced learning. Instructional Science, 43(2), 173–179. Retrieved from https://doi.org/10.1007/s11251-014-9343-4.
Klerkx, J., Verbert, K., & Duval, E. (2017). Learning Analytics dashboards. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The Handbook of Learning Analytics (pp. 143–150). SoLAR. Retrieved from http://solaresearch.org/hla-17/hla17-chapter1.
Knight, S., Gibson, A., & Shibani, A. (2020). Implementing learning analytics for learning impact: Taking tools to task. The Internet and Higher Education, 45, 100729.
Koehler, M. J., Mishra, P., & Cain, M. W. (2013). What is technological pedagogical content knowledge (TPACK)? Journal of Education, 193(3), 13–19. Retrieved from https://doi.org/10.1177/002205741319300303.
Linn, M. C., & Eylon, B.-S. (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration. New York: Routledge.
Luckin, R. (2010). Re-designing learning contexts: Technology-rich, learner-centred ecologies. London: Routledge.
Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018). Mining theory-based patterns from big data: Identifying self-regulated learning strategies in massive open online courses. Computers in Human Behavior, 80, 179–196. Retrieved from http://www.sciencedirect.com/science/article/pii/S0747563217306477, https://doi.org/10.1016/j.chb.2017.11.011.
Mangaroska, K., & Giannakos, M. (2018). Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516–534.
Martinez-Maldonado, R., Pardo, A., Mirriahi, N., Yacef, K., Kay, J., & Clayphan, A. (2015). LATUX: An iterative workflow for designing, validating and deploying learning analytics visualisations. Journal of Learning Analytics, 2(3), 9–39.
Martinez-Maldonado, R., Power, T., Hayes, C., Abdiprano, A., Vo, T., Axisa, C., & Buckingham Shum, S. (2019). Analytics meet patient manikins: Challenges in an authentic small-group healthcare simulation classroom. In Proceedings of the seventh international conference on learning analytics & knowledge (pp. 90–94).
Mavrikis, M., Karkalas, S., Cukurova, M., & Papapesiou, E. (2019). Participatory design to lower the threshold for intelligent support authoring. In Proceedings of 20th international conference on artificial intelligence in education, part II (pp. 185–189). Retrieved from https://doi.org/10.1007/978-3-030-23207-8_35.
McPherson, J., Tong, H. L., Fatt, S. J., & Liu, D. Y. (2016). Student perspectives on data provision and use: Starting to unpack disciplinary differences. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 158–167).
Mor, Y., Ferguson, R., & Wasson, B. (2015). Learning design, teacher inquiry into student learning and learning analytics: A call for action. British Journal of Educational Technology, 46(2), 221–229.
Prestigiacomo, R., Hadgraft, R., Hunter, J., Locker, L., Knight, S., van den Hoven, E., & Martinez-Maldonado, R. (2020). Learning-centred translucence: An approach to understand how teachers talk about classroom data. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 100–105).
Prieto, L. P., Rodríguez-Triana, M. J., Martínez-Maldonado, R., Dimitriadis, Y., & Gašević, D. (2019). Orchestrating learning analytics (OrLA): Supporting interstakeholder communication about adoption of learning analytics at the classroom level. Australasian Journal of Educational Technology, 35(4), 14–33.
Prieto-Alvarez, C. G., Martinez-Maldonado, R., & Shum, S. B. (2018). Mapping learner-data journeys: Evolution of a visual co-design tool. In Proceedings of the 30th Australian conference on computer-human interaction (pp. 205–214).
Ranjeeth, S., Latchoumi, T., & Paul, P. V. (2020). A survey on predictive models of learning analytics. Procedia Computer Science, 167, 37–46. Retrieved from http://www.sciencedirect.com/science/article/pii/S1877050920306451, https://doi.org/10.1016/j.procs.2020.03.180.
Reimann, P. (2016). Connecting learning analytics with learning research: The role of design-based research. Learning: Research and Practice, 2(2), 130–142. Retrieved 26 Sept 2019, from https://www.tandfonline.com/doi/full/10.1080/23735082.2016.1210198.
Rodríguez-Triana, M. J., Martínez-Monés, A., Asensio-Pérez, J. I., & Dimitriadis, Y. (2015). Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. British Journal of Educational Technology, 46(2), 330–343. Retrieved 01 Oct 2018, from http://doi.wiley.com/10.1111/bjet.12198.
Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. Co-design, 4(1), 5–18.
Sandoval, W. A., & Bell, P. (2004). Design-Based Research methods for studying learning in context: Introduction. Educational Psychologist, 39(4), 199–201. Retrieved 07 Apr 2019, from http://www.tandfonline.com/doi/abs/10.1207/s15326985ep3904_1.
Schuler, D., & Namioka, A. (1993). Participatory design: Principles and practices. London: CRC Press.
Sergis, S., & Sampson, D. G. (2017). Teaching and learning analytics to support teacher inquiry: A systematic literature review. In A. Peña-Ayala (Ed.), Learning analytics: Fundaments, applications, and trends (pp. 25–63). Cham: Springer.
Sharples, M. (2013). Shared orchestration within and beyond the classroom. Computers & Education, 69, 504–506.
Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. In Proceedings of the second international conference on learning analytics and knowledge (pp. 4–8). Retrieved from https://doi.org/10.1145/2330601.2330605.
Soller, A., Martínez, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education, 15(4), 261–290.
Verbert, K., De Laet, T., Millecamp, M., Broos, T., Chatti, M. A., & Muslim, A. (2020). XLA: Explainable learning analytics. In Adjunct proceedings of the tenth international conference on learning analytics & knowledge (pp. 477–479).
Vezzoli, Y., Mavrikis, M., & Vasalou, A. (2020). Inspiration cards workshops with primary teachers in the early co-design stages of learning analytics. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 73–82).
Vitale, J. M., McBride, E., & Linn, M. C. (2016). Distinguishing complex ideas about climate change: Knowledge integration vs. specific guidance. International Journal of Science Education, 38(9), 1548–1569. Retrieved 01 Oct 2018, from http://www.tandfonline.com/doi/full/10.1080/09500693.2016.1198969.
Wiley, K. J., Bradford, A., & Linn, M. C. (2019). Supporting collaborative curriculum customizations using the Knowledge Integration framework. In Proceedings of the 13th international conference on computer supported collaborative learning (Vol. 1, pp. 480–487).
Wiley, K. J., Dimitriadis, Y., Bradford, A., & Linn, M. C. (2020). From theory to action: Developing and evaluating learning analytics for learning design. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 569–578).
Wise, A. F., & Jung, Y. (2019). Teaching with analytics: Towards a situated model of instructional decision-making. Journal of Learning Analytics, 6(2), 53–69.
Wise, A. F., & Vytasek, J. (2017). Learning Analytics Implementation Design. In C. Lang, G. Siemens, A. Wise, & D. Gasevic (Eds.), Handbook of learning analytics (pp. 151–160). SoLAR. Retrieved 06 Apr 2019, from https://solaresearch.org/hla-17/hla17-chapter13, https://doi.org/10.18608/hla17.013.
Acknowledgements
The research of the first author was partially funded by the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science, Innovations, and Universities (TIN2017-85179-C3-2-R), the European Regional Development Fund and the Regional Council of Education of Castile and Leon (VA257P18), and the European Commission (588438-EPP-1-2017-1-EL-EPPKA2-KA). This material is partially based upon the work supported in part by the National Science Foundation (DRL-1813713). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Dimitriadis, Y., Martínez-Maldonado, R., Wiley, K. (2021). Human-Centered Design Principles for Actionable Learning Analytics. In: Tsiatsos, T., Demetriadis, S., Mikropoulos, A., Dagdilelis, V. (eds) Research on E-Learning and ICT in Education. Springer, Cham. https://doi.org/10.1007/978-3-030-64363-8_15
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