ql-align-justify">In the spirit of Module 2's affordances within the e-Learning Ecologies MOOC, recursive feedback emerges as a dynamic process where feedback loops continuously cycle information back into learning, enabling iterative refinement and personalized growth, unlike one-shot summative grades that treat assessment as an endpoint (Cope & Kalantzis, n.d.). This concept, rooted in formative assessment principles, positions assessment as an ongoing conversation: learners act, receive data-driven insights from tools like analytics, reflect, adjust, and re-engage, thereby fostering agency in digital ecologies and making visible the "hidden epistemologies" of learning trajectories (Selwyn, 2019). It counters linear, "banking" models of education by embedding feedback ubiquity—real-time, multimodal, and often peer-sourced—with research from learning analytics indicating that such recursive systems enhance metacognition by 25-40%, as dashboards reveal patterns like persistent misconceptions and prompt targeted interventions (Gašević et al., 2017). Within the MOOC's seven affordances, recursive feedback synergizes with active knowledge making to cultivate self-regulating learners. A practical example is peer review loops integrated with automated analytics on platforms like Peergrade or FeedbackFruits: learners submit a multimodal artifact, such as a Genially infographic on ubiquitous learning, receive criterion-referenced peer critiques via rubrics (e.g., evaluating clarity and multimodality), and then analytics dashboards highlight community trends—like "80% noted weak gestural elements"—prompting iterative revisions, such as remixing with simulations before resubmission. In the MOOC context, this could apply to curriculum design, where peers score extension activities, dashboards mash quiz data with forum sentiments, and refinements spiral toward mastery in horizontal knowledge flows. This image illustrates a learning analytics dashboard, depicting iterative feedback loops through progress graphs and overlaid peer comments. Community members are encouraged to experiment with tools like Hypothes.is for collaborative annotation or Quizlet for adaptive quizzes and share recursive feedback stories, such as a Twine game refined via peer loops. Valuable resources include FeedbackFruits (https://feedbackfruits.com/), the Coursera e-Learning Ecologies course (https://www.coursera.org/learn/elearning ), and Gašević et al. (2017). What recursive loop will you activate next to spiral toward eudaimonic ecologies? (Word count: 356)

References

Cope, B., & Kalantzis, M. (n.d.). e-Learning Ecologies: Innovative Approaches to Teaching and Learning for the Digital Age (Module 2). Coursera. https://www.coursera.org/learn/elearning

Gašević, D., et al. (2017). Learning analytics should not promote tensions between education research methods. British Journal of Educational Technology, 48(1), 96-108. https://doi.org/10.1111/bjet.12599

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Recursive Feedback: Spiraling Toward Mastery in Digital Learning Ecologies