Institutional Analytics for Transformation - Baker, Ryan S. (Rastros Rostros, 2026)
Learning analytics—the systematic use of educational data to understand and improve learning—has emerged as a promising approach to addressing persistent challenges in higher education, including student dropout, uneven learning outcomes, and structural inequities. This article examines how learning analytics can support institutional improvement, with particular attention to multi-campus universities and contexts where such approaches remain underdeveloped. Drawing on examples from higher education and K–12 systems worldwide, the article adopts a socio-technical perspective, arguing that the effective use of analytics depends as much on human practices, governance, and ethics as on technological capability.The article reviews applications of learning analytics across key stakeholder groups. Instructors use dashboards to identify at-risk students and inform pedagogical decisions; students receive personalized feedback, nudges, and course recommendations that support persistence; digital learning platforms adapt content and detect disengagement; advisors prioritize outreach through integrated data systems; and academic leaders use analytics to identify curricular gaps and instructional weaknesses. Across these applications, the article foregrounds core ethical principles—transparency, privacy, consent, fairness, de-biasing, and accountability—as essential foundations for responsible analytics practice.At the same time, the article highlights significant implementation challenges that frequently undermine analytics initiatives. These include insufficient data infrastructure, alert fatigue when predictions are not embedded in actionable workflows, limited instructor and IT engagement, and difficulties scaling pilot projects across diverse campuses and programs. Addressing these obstacles requires framing learning analytics not as a technical add-on but as a form of organizational change.To support sustainable implementation, the article advocates for iterative, evidence-informed design processes grounded in the learning sciences, human–computer interaction, and universal design. Key enabling conditions include feedback loops for continuous improvement, professional development in data literacy, clear governance structures with cross-campus representation, defined roles for learning designers and implementation coordinators, and robust data stewardship practices. For multi-campus institutions, balancing standardization with local adaptation is critical to ensuring equitable impact.The article concludes by considering emerging challenges associated with generative AI and by outlining practical next steps for institutional leaders. By treating learning analytics as a long-term, ethically grounded commitment centered on equity and student agency, universities can harness data to support meaningful improvement and sustain public trust in the responsible use of educational technology.
Keywords: learning analytics; student success; implementation; multi-campus university; socio-technical systems