In this week’s issue, we tackle one of my favorite topics – how to effectively implement a learning analytics solutions on your campus! And since you’re reading our blog, I’m going to guess you have an interest in it too. From there, we round out the issue with a look at the current status of online learning before closing with an EDUCAUSE podcast on AI literacy.   

After reading today’s issue, share your thoughts about effective learning analytics implementations in the comments! 

Effective Analytics Implementation 

From Six Steps for Effective Learning Analytics Implementation | 1EdTech Consortium 

Based upon insights and best practices from its Learning Analytics Builders Coalition, 1EdTech Consortium has released a six-step guide for successful learning analytics programs. 

Our Thoughts

From my perspective as someone completing a PhD focused on learning analytics, this guide from 1EdTech offers practical and well-grounded guidance. Too often, learning analytics is discussed as a technical project rather than a cultural shift, but this piece gets it right by grounding its recommendations in real institutional examples and emphasizing collaboration across roles. For anyone navigating digital transformation efforts on campus, there is a lot to appreciate here. 

The first step, forming a cross-functional committee, stood out immediately. Building a diverse team is not just a good project management move. It is backed by research. Co-designed dashboards and analytics tools are far more likely to address the real questions stakeholders have and, more importantly, to be used by the people they are intended to support. When faculty, advisors, technologists, and institutional researchers come together at the start, the result is more thoughtful design and greater buy-in. I’ve seen this firsthand in my own work, and it makes a difference. 

I also appreciated the focus on starting small. One of the best lessons I learned while launching Argos at a previous institution was that traction comes from proving value early. Whether it’s improving DFW rates in gateway courses or optimizing the course scheduling process, keeping the scope narrow helps build momentum. The spotlight examples from places like Kennesaw State and UMBC show how focused pilots can eventually lead to broader cultural shifts. These aren’t just tech upgrades. They’re entry points to rethinking how we understand and support student success.  

If there is one takeaway I hope readers hold onto, it is that learning analytics is not just about data. It is about culture, trust, and the ability to act on insight. This guide does a great job of reminding us that the real work starts with people and not platforms, and that successful digital transformation happens when we build structures that bring those people together. 

Students Expect More from Online Learning 

From Online Learning: Past the Tipping Point | Inside Higher Ed 

Two surveys of chief online learning officers indicate that students’ expectations for online learning exceed what many institutions are currently able to provide.  

Our Thoughts

If enrollment pressure had a face, it would look like a student who wants flexible options and a clear return on investment. The new CHLOE 10 and UPCEA BOnES reports describe exactly that moment. Demand for online and hybrid keeps rising across traditional and adult learners, even as many campuses admit they are still catching up on strategy, faculty readiness, and academic continuity. In other words, students are voting with their feet, and the market is moving faster than our operating models. 

These reports also surface a readiness gap that matters for student trust. Too few campuses report that faculty are fully prepared for online course design, and many lack a coordinated plan for AI, data governance, and equitable access to tools. Students will continue to expect flexible formats and modern supports, but they will also notice when the experience feels uneven. Building capacity, aligning student services, and setting clear design standards aren’t extras. They are the essentials for quality at scale. 

So what should leaders take from this moment? First, meet flexibility head on with program mixes that reflect real learner demand, not legacy assumptions. Second, view lifelong learning as a portfolio strategy, where nondegree and degree pathways reinforce one another over time. Third, price and position online with the same rigor you bring to any competitive market, and be ready to show how the experience translates to outcomes. Finally, invest in people and process. Online success comes from clear strategy, cross-campus collaboration, and a sustained commitment to quality that students can feel on day one. The tipping point is real. Whether it becomes a lasting advantage will depend on how well we align what students expect with what we consistently deliver. 

AI Literacy Podcast    

From A Practical Guide to AI Literacy | EDUCAUSE Shop Talk 

Sophie and Jenay talk with Leo S. Lo, Jeanne Beatrix Law, and Anissa Vega about practical strategies for guiding student and faculty AI use and literacy. 

Our Thoughts

What I valued most from this conversation was hearing colleagues describe what is working on their campuses right now. The shift from abstract debates about AI to concrete examples of course policies, faculty upskilling, cohort models, and student-facing guidance is exactly what our sector needs. Recent data show that student use of generative AI is already mainstream, yet many institutions are still aligning policy, training, and support. Hearing librarians, writing faculty, and academic leaders share the specific practices they’re using now helps bridge the gap between policy conversations and real-world implementation. 

I also appreciated the practical frameworks discussed on the podcast. Whether it is a prompting mindset like CLEAR, a rhetorical prompting process in writing, or cohort-based faculty development, these are approachable starting points that teams can adopt and adapt. The research community is pointing us in the same direction. UNESCO’s guidance urges multi-stakeholder, cross-functional work so that AI literacy is built with, not just for, the people who teach and learn (just like learning analytics!). Librarians’ expertise in information literacy remains essential as we help students verify sources, interpret AI-generated summaries, and navigate hallucinations with care.  

The episode also reinforced the value of collecting many perspectives as we figure out what AI means for higher education. Students want clear rules and practical help. Faculty need time, tools, and room to experiment. Leaders need policies that are flexible enough to evolve. Sector surveys echo this tension. The CHLOE 10 report (see our second article) highlights rising demand for online and AI-supported learning, alongside uneven faculty readiness and limited coordination on AI strategy and data practices. That combination tells me we should keep prioritizing small pilots, shared design standards, and collaborative governance that includes teaching and learning, libraries, IT, and student services.  

So, after an hour of conversation, what did I learn from the EDUCAUSE Shop Talk guests? First, keep listening to peers and borrowing what works. We’ve always been good at sharing and collaboration in higher ed. Second, pick a shared framework for AI literacy and iterate in the open. Perfection can wait; clarity and consistency cannot. Third, invite multiple voices to the table, including skeptics, because critique is part of literacy and will make our policies better. Finally, meet students where they already are. Most are using AI for explanations and summaries, not shortcuts, and they respond well when we pair permission with guardrails and teach verification skills. That is how we turn practical advice into daily practice.