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From theory to action: developing and evaluating learning analytics for learning design

Published:23 March 2020Publication History

ABSTRACT

The effectiveness of using learning analytics for learning design primarily depends upon two concepts: grounding and alignment. This is the primary conjecture for the study described in this paper. In our design-based research study, we design, test, and evaluate teacher-facing learning analytics for an online inquiry science unit on global climate change. We design our learning analytics in accordance with a socioconstructivism-based pedagogical framework, called Knowledge Integration, and the principles of learning analytics Implementation Design. Our methodology for the design process draws upon the principle of the Orchestrating for Learning Analytics framework to engage stakeholders (i.e. teachers, researchers, and developers). The resulting learning analytics were aligned to unit activities that engaged students in key aspects of the knowledge integration process. They provided teachers with actionable insight into their students' understanding at critical junctures in the learning process. We demonstrate the efficacy of the learning analytics in supporting the optimization of the unit's learning design. We conclude by synthesizing the principles that guided our design process into a framework for developing and evaluating learning analytics for learning design.

References

  1. 2013. Next Generation Science Standards: For States, By States. National Academies Press, Washington, D.C. Google ScholarGoogle ScholarCross RefCross Ref
  2. Yoav Bergner, Geraldine Gray, and Charles Lang. 2018. What Does Methodology Mean for Learning Analytics? Journal of Learning Analytics 5, 2 (Aug. 2018), 1--8. Google ScholarGoogle ScholarCross RefCross Ref
  3. Ton de Jong. 2019. Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead. Journal of Computer Assisted Learning 35, 2 (April 2019), 153--167. Google ScholarGoogle ScholarCross RefCross Ref
  4. Vanessa Echeverria, Roberto Martinez-Maldonado, Roger Granda, Katherine Chiluiza, Cristina Conati, and Simon Buckingham Shum. 2018. Driving data storytelling from learning design. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18. ACM Press, Sydney, New South Wales, Australia, 131--140. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Rebecca Ferguson. 2012. Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning 4, 5/6 (2012), 304. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dragan Gašević, Vitomir Kovanović, and Srećko Joksimović. 2017. Piecing the learning analytics puzzle: a consolidated model of a field of research and practice. Learning: Research and Practice 3, 1 (Jan. 2017), 63--78. Google ScholarGoogle ScholarCross RefCross Ref
  7. Pablo A. Haya, Oliver Daems, Nils Malzahn, Jorge Castellanos, and Heinz Ulrich Hoppe. 2015. Analysing content and patterns of interaction for improving the learning design of networked learning environments: Analysing content and patterns of interaction. British Journal of Educational Technology 46, 2 (March 2015), 300--316. Google ScholarGoogle ScholarCross RefCross Ref
  8. Davinia Hernández-Leo, Roberto Martinez-Maldonado, Abelardo Pardo, Juan A. Muñoz-Cristóbal, and María J. Rodríguez-Triana. 2019. Analytics for learning design: A layered framework and tools: Analytics layers for learning design. British Journal of Educational Technology 50, 1 (Jan. 2019), 139--152. Google ScholarGoogle ScholarCross RefCross Ref
  9. Claudia Leacock and Martin Chodorow. 2003. Crater: Automated Scoring of Short-Answer Questions. Language Resources and Evaluation - LRE 37 (2003), 389--405. Google ScholarGoogle ScholarCross RefCross Ref
  10. Hee-Sun Lee and Ou Lydia Liu. 2009. Assessing learning progression of energy concepts across middle school grades: The knowledge integration perspective. Science Education 94, 4 (Nov. 2009), 665--688. Google ScholarGoogle ScholarCross RefCross Ref
  11. Marcia C. Linn and Bat-Sheva Eylon. 2011. Science learning and instruction: taking advantage of technology to promote knowledge integration. Routledge, New York. OCLC: ocn176946367.Google ScholarGoogle Scholar
  12. Lori Lockyer, Elizabeth Heathcote, and Shane Dawson. 2013. Informing Pedagogical Action: Aligning Learning Analytics With Learning Design. American Behavioral Scientist 57, 10 (Oct. 2013), 1439--1459. Google ScholarGoogle ScholarCross RefCross Ref
  13. Katerina Mangaroska and Michail N. Giannakos. 2018. Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies (2018), 1--1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Yishay Mor, Rebecca Ferguson, and Barbara Wasson. 2015. Editorial: Learning design, teacher inquiry into student learning and learning analytics: A call for action: Learning design, TISL and learning analytics. British Journal of Educational Technology 46, 2 (March 2015), 221--229. Google ScholarGoogle ScholarCross RefCross Ref
  15. Donatella Persico and Francesca Pozzi. 2015. Informing learning design with learning analytics to improve teacher inquiry: Informing LD with LA to improve teacher inquiry. British Journal of Educational Technology 46, 2 (March 2015), 230--248. Google ScholarGoogle ScholarCross RefCross Ref
  16. Luis P. Prieto, María Jesús Rodríguez-Triana, Roberto Martínez-Maldonado, Yannis Dimitriadis, and Dragan Gašević. 2018. Orchestrating learning analytics (OrLA): Supporting inter-stakeholder communication about adoption of learning analytics at the classroom level. Australasian Journal of Educational Technology (Nov. 2018). Google ScholarGoogle ScholarCross RefCross Ref
  17. Peter Reimann. 2016. Connecting learning analytics with learning research: the role of design-based research. Learning: Research and Practice 2, 2 (July 2016), 130--142. Google ScholarGoogle ScholarCross RefCross Ref
  18. María Jesús Rodríguez-Triana, Alejandra Martínez-Monés, Juan I. Asensio-Pérez, and Yannis Dimitriadis. 2015. Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations: Scripting and monitoring meet each other. British Journal of Educational Technology 46, 2 (March 2015), 330--343. Google ScholarGoogle ScholarCross RefCross Ref
  19. María Jesüs Rodríguez-Triana, Luis P. Prieto, Alejandra Martínez-Monés, Juan I. Asensio-Pérez, and Yannis Dimitriadis. 2018. The teacher in the loop: customizing multimodal learning analytics for blended learning. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK '18. ACM Press, Sydney, New South Wales, Australia, 417--426. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. William A. Sandoval and Philip Bell. 2004. Design-Based Research Methods for Studying Learning in Context: Introduction. Educational Psychologist 39, 4 (Dec. 2004), 199--201. Google ScholarGoogle ScholarCross RefCross Ref
  21. Anouschka van Leeuwen. 2015. Learning analytics to support teachers during synchronous CSCL: Balancing between overview and overload. Journal of Learning Analytics 2, 2 (Dec. 2015), 138--162. Google ScholarGoogle ScholarCross RefCross Ref
  22. Keisha Varma, Freda Husic, and Marcia C. Linn. 2008. Targeted Support for Using Technology-Enhanced Science Inquiry Modules. Journal of Science Education and Technology 17, 4 (Aug. 2008), 341--356. Google ScholarGoogle ScholarCross RefCross Ref
  23. Tammie Visintainer and Marcia Linn. 2015. Sixth-Grade Students' Progress in Understanding the Mechanisms of Global Climate Change. Journal of Science Education and Technology 24, 2--3 (April 2015), 287--310. Google ScholarGoogle ScholarCross RefCross Ref
  24. Jonathan M. Vitale, Elizabeth McBride, and Marcia C. Linn. 2016. Distinguishing complex ideas about climate change: knowledge integration vs. specific guidance. International Journal of Science Education 38, 9 (June 2016), 1548--1569. Google ScholarGoogle ScholarCross RefCross Ref
  25. Korah J Wiley, Allison Bradford, and Marcia C. Linn. 2019. Supporting Collaborative Curriculum Customizations Using the Knowledge Integration Framework. In A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning, Kristine Lund, Gerald P. Niccolai, Elise Lavoue, Cindy Hmelo-Silver, Gahgene Gweon, and Michael Baker (Eds.), Vol. 1. International Society of the Learning Sciences (ISLS), Lyon, France, 480--487.Google ScholarGoogle Scholar
  26. Alyssa Wise, Yuting Zhao, and Simone Hausknecht. 2014. Learning Analytics for Online Discussions: Embedded and Extracted Approaches. Journal of Learning Analytics 1, 2 (2014), 48--71. Google ScholarGoogle ScholarCross RefCross Ref
  27. Alyssa Friend Wise and Jovita Vytasek. 2017. Learning Analytics Implementation Design. In Handbook of Learning Analytics (first ed.), Charles Lang, George Siemens, Alyssa Wise, and Dragan Gasevic (Eds.). Society for Learning Analytics Research (SoLAR), 151--160. Google ScholarGoogle ScholarCross RefCross Ref

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  1. From theory to action: developing and evaluating learning analytics for learning design

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        cover image ACM Other conferences
        LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
        March 2020
        679 pages
        ISBN:9781450377126
        DOI:10.1145/3375462

        Copyright © 2020 ACM

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        Publication History

        • Published: 23 March 2020

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        LAK '20 Paper Acceptance Rate80of261submissions,31%Overall Acceptance Rate236of782submissions,30%

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