Sher Downing PhD

Are all forms of work-based learning equal? The State Opportunity Index (SOI) Survey provides a data driven framework designed to help policymakers, system leaders, and institutions strengthen the connection between postsecondary education and economic opportunity. In this webinar, Strada Education Foundation and Trellis Strategies will share institution focused insights from the 2025 SOI Survey, with a special emphasis on work-based learning—an area closely tied to career mobility and student success. We’ll walk through key findings, highlight actionable strategies, and conclude with details on how campuses can participate in the upcoming no cost SOI Survey administration this spring.

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Jeff Goumas and I are learning so much through this research with American Institutes for Research's Stephanie Cronen, Neha Nanda and others. Some intended, some not. For example, yes, we are learning about the features that make apps for organizing #OER useful. We are ALSO learning about how to partner with developers to ensure a smooth yet flexible process. Check out our post on the CREATE Adult Skills Network page for more info!

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📊 Our Data and Computing Education Report is officially live — and free to read and download! The The National Academies of Sciences, Engineering, and Medicine committee is sharing research-based findings and recommendations that will shape the future of K-12 computing education. A few highlights of what the field needs to prioritize, especially in a world where K-12 needs to prepare studenst for a world filled with data, AI, and more computing: 🔗 Better within-subject integration of data and computing content and practices and a stronger focus at the elementary level 🧩 More emphasis on problem-posing and problem-solving processes — not just answers or decontextualized ideas 📈 A meaningful push for probabilistic and inferential reasoning, including how students understand and use models 🤖 Explicit attention to abstraction, algorithmic thinking, and automation ⚖️ More innovation in ethical frameworks and how we help students think critically about technology and society This report includes 14 research-based recommendations directed at funders, state and local education organizations, professional orgs that produce standards documents, researchers, and pre-service teacher education programs. These reports are the most authoritative research-based national syntheses on the topics available. We're hosting a webinar TODAY to walk through the findings — and will have info sessions at upcoming conferences too. 📥 Read and download the full report for free. #ComputingEducation #K12Education #EdTech #DataLiteracy #CSEducation #AIliteracy #AIEducation

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#HigherEd is undergoing a transformative shift with AI and digital tools redefining learning experiences. From personalized instruction to streamlined operations, these insights unveil the future of education. 💡See how tech is leveling the playing field and enhancing student success: https://ow.ly/s5tu30sQmyZ

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Community Colleges Chart Path Forward on AI Integration as Students Outpace Faculty Adoption, via Diverse: Issues In Higher Education https://lnkd.in/gJJhxmap

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Teaching is changing fast — and so are the tools and ideas that shape it.     Our UMass Amherst Instructional Design team has pulled together what we’ve learned this year into a Special Edition of the EdTech IDEAS Digest — a collection of insights, examples, and quick wins to make teaching more engaging, inclusive and effective. Check it out to see:     - What students really want from classroom technology  - How to use AI to develop sharper, more effective learning objectives  - Design tips for creating truly accessible course syllabi  - An Accessibility Starter Kit, perfect for beginners and pros  - A Canvas Course Checklist to ensure your online course is ready to launch  …and much more!     We’re proud to share this with the broader teaching community — because good ideas grow when shared.     To learn more about other innovative teaching practices and stay up to date with future editions, visit https://lnkd.in/e8MAsAv4 

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The LEARN Commission’s new report on Learning Evaluation for the 21st Century deserves serious attention from higher education leaders. For decades, we have treated transfer credit and prior learning evaluation as administrative processes. In reality, they are infrastructure decisions that shape whether learners move forward or give up entirely. The report highlights what many institutions quietly know: 1) Learning evaluation is still largely manual, decentralized, and inconsistent. 2) Decisions about prior learning often require staff to review transcripts, syllabi, portfolios, and credentials one case at a time. 3) The result is slow decisions, unclear rules, and unnecessary repetition of work. Meanwhile, learner mobility has exploded. Students move between institutions, workforce training programs, military education, dual enrollment, online providers, and employer-based learning. The traditional course-by-course transfer model was never designed for this level of complexity. The LEARN Commission proposes an important shift in mindset: Instead of asking “Should we accept this learning?” Institutions should begin by asking, “Is the learner prepared to continue?” This is a subtle but powerful change. But implementing this vision requires something the report only hints at: data infrastructure. Today, most learning evidence still lives in PDFs, transcripts, syllabi, and course catalogs. These artifacts were built for humans to read, not for systems to compare or evaluate. If institutions want faster, fairer, and more transparent learning evaluation, we will need to move toward machine-readable learning data, including: • course learning outcomes • program competencies • credential metadata • skills mappings • machine-readable transcripts When learning data becomes structured and interoperable, several things become possible: • automated outcome alignment across institutions • AI-assisted credit evaluation • real-time transfer guidance for students • transparent credential pathways • stronger connections between education and workforce skills In other words, the real transformation is not simply policy reform. It is the creation of a national learning mobility infrastructure. The LEARN Commission report opens the door to that conversation. The next step is building the systems that make it real. The question for higher education leaders is simple: Are we redesigning transfer processes…Or finally redesigning the learning system itself?

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The Future of Higher Ed Financial Management As artificial intelligence (AI) transforms how universities plan, manage, and report financial performance, it also introduces new challenges in governance, accountability, and ethical oversight. This webinar brings together thought leaders in higher education financial modelling, as well as experts in AI risk and readiness to explore how institutions can responsibly leverage AI to enhance financial decision-making and resource optimization. We will explore: Data Governance and Quality - Establishing Governance frameworks and data quality assessments AI Ethics and Accountability: Embedding ethical principles and clear lines of accountability in AI development and deployment. Organizational Readiness - Establishing an AI Financial Management Council to support and manage responsible AI adoption AI driven financial insights - How existing AI tools can support financial management. Speakers: Shaji Obeidullah - Head of AI & CX - INTELLIGEN Harish Suresh - Senior Partner Solutions Engineer - Snowflake Adam Gallard - Chief Product Officer - Pilbara Group Lea Patterson - CEO - Pilbara Group

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Higher Ed Needs Reinvention - Embracing Controversy & AI Innovation Universities must adapt to AI and changing learner needs. At Career-Bond, we show how career training responds with agility and relevance. This video explores careers in technology and healthcare that benefit from career training designed for the modern workplace. Career training emphasizes applied skills, workforce readiness, and measurable outcomes. Career training helps learners succeed where institutions are slow to change. Don’t wait—explore your career options now at https://zurl.co/WSzIK #HigherEdInnovation #career training #career-bond

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Most IAM tools weren’t built for Higher Ed — and it shows. Navigate IAM changes that. ✅ Built for Higher Ed from the ground up ✅ Open-source foundation for visibility + control ✅ Flexible enough to meet you where you are If your IAM solution feels like its working against you instead of with you this is worth a look.

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Canvas LMS might not be the biggest learning management system provider out there, but with a reported 11.000+ customers they do bring good volume to the Instructure-OpenAI partnership. The article includes many helpful related sources/links, and is worth a read if you're following the topic of AI deployment in higher education.

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What do learners really need from micro-credentials today?  At the 1EdTech 2026 Digital Credentials Summit, Cate Tolnai, Director of Microcredentials and Innovation Credentials Strategy at University of Phoenix, shared how institutions and industry can better align learning to workforce needs.  Key takeaways from her session:  - Micro-credentials should map to real-world job skills  - Learners need ways to demonstrate capabilities as they build them  - Credentials must be portable, verifiable and employer-aligned  Her session emphasized a shift from standalone badges to stackable, career-connected pathways that signal readiness.  Read more:  https://lnkd.in/gpdeijrf  #FutureOfWork #Upskilling #Microcredentials #Innovation 

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Tyton Partners - Strategy Consulting Top Ten List - oops - make that Top Nine List of higher education trends for 2026. 9️⃣ Institutions prioritize CRM and lifecycle engagement platforms to improve yield and retention. 8️⃣ Data interoperability becomes a strategic advantage — and a prerequisite for unlocking AI’s full value 7️⃣ Institutions will shift their approach to AI by actively managing the technology as an enterprise asset 6️⃣ Shared services and institutional mergers will accelerate across both small colleges and regional publics 5️⃣ Elimination of GradPLUS loans will reduce enrollment in select healthcare graduate programs, and lead to new institution-employer partnerships to mitigate impact on labor shortages 4️⃣ Non-degree credentials will become a core enrollment and employability strategy Institutions will accelerate adoption of platforms that deliver non-degree credentials such as micro-credentials and digital badges. These stackable offerings will strengthen students’ employable competencies and improve their ability to signal job readiness, supported by the National Association of Colleges and Employers (NACE) data showing that 65% of employers now prioritize skills-based hiring practices. To remain competitive as enrollment pressures intensify, institutions will increasingly rely on these credentials, a trend reflected in the National Student Clearinghouse finding that undergraduate certificate earners grew by a record 11% year over year. 3️⃣  Interdisciplinary pathways will outpace traditional humanities in student demand 2️⃣ Heightened federal and state accountability pressures make paychecks and pathways the new rules of student success 1️⃣ As the demographic cliff arrives, non-traditional pathways become a lifeline With the number of 18-year-olds peaking in 2025, 2026 will mark the first year of a 15-year slide in first-time undergraduates; the demographic cliff is no longer a metaphor but a math problem. New international enrollment is falling fast (down 17% this fall), while dual-enrollment pipelines show early promise with 6% growth. A softening labor market may send more adults back to school. The momentum, however, may tilt toward certificates and non-degree credentials, especially with Workforce Pell arriving in 2026 to raise the bar on quality. Institutions that lean into dual enrollment, high-quality non-degree credentials, and demonstrable workforce outcomes will be better positioned to withstand the cliff. 🏈 Pittsburgh Steelers 21, Baltimore Ravens 20 Bonus points if you got the Letterman reference. #education | #innovation | #continuingeducation | #microcredentials | #highered | #pittsburghsteelers | #baltimoreravens https://lnkd.in/euTivuPr

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How is AI impacting education systems? It's aiding in systems thinking around the transformation for MTSS. Platforms are emerging that provide the rich data educators have long sought. We're seeing seamless integration of multiple data sources, encompassing academics, attendance, and behavior. Many systems in education are antiquated, often relying on multiple platforms that don't communicate effectively, existing in isolation. The integration and advancement in AI could be a great benefit to the education system. #AI #education #MTSS #edtech #datascience

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Why institutions are investing millions in curriculum tools that don’t improve outcomes, and how to fix it. Every year, institutions invest heavily in: • Learning platforms • Publisher content • AI tools • Faculty training initiatives The expectation… ·        Better learning outcomes ·        Higher retention ·        Stronger program performance But for many… those results never fully materialize. Here’s the uncomfortable truth: Tools don’t fix curriculum problems. They amplify them. If your curriculum is fragmented, unclear, or misaligned… Adding more tools just makes the gaps harder to see, and more expensive to sustain. I’ve worked with programs that had: * Best-in-class technology * Engaged faculty * Strong leadership * Abundant student support systems ALL the things… but they still struggled with: • Inconsistent student outcomes • Courses that didn’t build on each other • Assessments that didn’t measure real learning The issue wasn’t effort. It wasn’t resources. It was STRUCTURE. This is exactly why I developed the CLEAR Curriculum Framework Not as another layer… but as the foundation everything else should sit upon. Because when curriculum is CLEAR--- • Outcomes are defined before content is delivered • Courses build logically across the program • Assessments measure what students are actually expected to learn • Tools and technology finally have a purpose, and a return Here’s the shift institutions need to make--- Before investing in another tool, ask---  * What specific learning problem are we trying to solve? * Is this a resource issue, or a curriculum structure issue? * Are our courses designed to work together, independently, or somewhere in between? Because no amount of technology, including AI, can fix a system that was never designed to support learning in the first place. If you’re evaluating new tools, struggling with inconsistent outcomes, or questioning ROI w/ your current resources… It may be time to examine the foundation. I’m currently working with institutions to: ·         Audit curriculum structure ·        Align programs for measurable outcomes ·        Integrate AI without adding complexity ·        Strengthen faculty effectiveness through better design ·        Certifying curriculum personnel on CLEAR principles If this resonates, send me a message. I’m happy to share what this looks like in practice.

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