Educational Data Analysis

Anthropic launched three new AI fluency courses alongside research analyzing 74k educator conversations. The data provides concrete insights into how AI is being used in higher education, while the courses address some of the challenges and opportunities these findings reveal. Key findings from the research: • 57% of faculty chats with Claude focus on curriculum development and instructional design • Faculty show a preference for using Claude to augment not automate their work • While faculty rate grading as AI’s least effective application in their practice, nearly half of grading conversations were fully automated • Faculty expressed concerns about "cognitive offload" and students becoming overly dependent on AI, emphasizing the need for students to develop foundational AI literacy skills The newly released courses demonstrate a commitment to higher education and begin to bridge the gap between current practices and effective AI integration. Here are some of the course highlights: • Created three courses—for students, for teachers, and for teaching AI fluency • Strong "human in the loop" approach that aligns with the augmentation patterns educators prefer • 4D framework (Delegate, Describe, Discern, Diligence) offers structured decision-making for when to collaborate vs. automate • Focus on responsible collaboration and academic integrity, particularly relevant given the grading automation concerns • Content designed for college-level learners, though not suitable for K-12 audiences The alignment between the research findings and course principles is encouraging. However, the data also reveals why targeted training matters—educators are innovating in curriculum development, but may need additional support around assessment practices. It's promising to see how these frameworks align with approaches like our SEE model (Safe, Ethical, Effective AI use), pointing toward more consistent standards across the field. Link to the courses and the research in the comments. AI for Education #AIfluency #AILiteracy #AIeducation #K12

Why some kids thrive and others quietly fall behind. A new longitudinal study uncovered four parenting profiles and only one consistently supports both academic and emotional development. Here’s what they found: 1. Autonomy-supportive parents These parents set clear rules and allow for independent decision-making. They communicate expectations—but also validate emotions. This was the only group whose children showed gains in both well-being and achievement over time. 2. Controlling parents Think strict, directive, rule-bound—without much room for dialogue. These kids often performed well academically but had lower self-worth and motivation. 3. Cold parents Low on both structure and emotional warmth. This group’s kids showed the worst outcomes, both emotionally and academically. 4. Inconsistent parents Rules and emotional support were unpredictable. Not surprisingly, these children were more likely to experience anxiety and poor performance. The takeaway? Adolescents thrive when they’re given structure and space. Rules and respect. Guidance and autonomy. For any parent, educator, or leader this is the balance to aim for.

Last time I wrote about results, it was to celebrate the progress of NYC Reads. This time, NAEP results are sounding the alarm: national trends show concerning declines. But as Dan Heath reminds us, there’s power in looking for the bright spots. And a few districts are bucking the trend, showing what’s possible. The big question: What are they doing differently, and how can we replicate those lessons across the country? Because if some students are succeeding, all students can. What This Administration of NAEP Revealed: 1️⃣Across the board, scores were record lows. 2️⃣Few seniors displayed strong skills. Only 22% of seniors scored Proficient in math and 35% in reading; 45% were Below Basic in math and 32% Below Basic in reading. 3️⃣No post-pandemic recovery. Five years after the pandemic, this lack of recovery is a sobering reality check. Potential Drivers of the Outcomes: 1️⃣ Chronic absenteeism is eroding learning. In 2024, nearly one in three 12th graders reported missing three or more days of school in the prior month, up from one in four in 2019. Younger grades show similar trends, meaning millions of students are losing out on learning time. 2️⃣ Recovery has been fragmented and short-lived. Academic outcomes were slipping before COVID, and the pandemic accelerated the decline. Yet recovery efforts have been piecemeal, short-term, underfunded, and uncoordinated. 3️⃣Too few students receive consistent, high-quality instruction. Even when students are in school, many are not exposed to grade-level work or effective teaching. 4️⃣Accountability has weakened just as urgency is needed. Since ESSA, momentum for clear, data-driven accountability has stalled. Bright Spots: 1️⃣ Richmond, VA: Richmond Public Schools has seen notable recovery in reading. In the 2023–24 school year, 50% of RPS students were proficient in reading, up from about 47% two years prior. Reading proficiency for economically disadvantaged students jumped from the mid-30s (percent proficient) in 2021–22 to the mid-40s by 2023–24 – roughly a 10 percentage point gain over two years. 2️⃣Mississippi: Sustained gains in reading and math over the past decade. 2024 results showed Mississippi achieving its highest-ever NAEP proficiency rates, improving across all four main NAEP tests (4th & 8th grade reading and math). 3️⃣Louisiana: Major improvement in 4th-grade reading (above pre-pandemic level). Louisiana was the only state in 2024 to statistically surpass its 2019 fourth-grade reading score. 4️⃣Tennessee: Tennessee’s 2024 NAEP results showed gains in 4th and 8th grade, in both ELA and math, propelling the state’s national rankings upward by 10 or more spots in each category. What’s Working? 1️⃣ Guarantee coherent, evidence-based instruction 2️⃣ Invest in targeted, high-dosage interventions 3️⃣ Build systemwide coherence 4️⃣ Double down on accountability and leadership 5️⃣ Engage families and communities

𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗯𝗲 𝘁𝗵𝗲 𝘂𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲 𝗦𝗸𝗶𝗹𝗹. 𝗡𝗼𝘄, 𝗗𝗮𝘁𝗮 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝘀𝗵𝗮𝗿𝗲𝘀 𝘁𝗵𝗲 𝘁𝗼𝗽 𝘀𝗽𝗼𝘁 ! Is Data Literacy the new Writing? The data says yes. ✍️ If you asked leaders 10 years ago what the most critical day-to-day skill was, the answer was almost universally "communication and writing." Fast forward to today, and the landscape has completely transformed. Data is no longer a niche skill for analysts; it is the new baseline language of business. 📈 A massive 88% of leaders now rate basic data literacy as "important" or "very important" for day-to-day tasks. ⚖️ This officially puts data literacy on par with, and even slightly ahead of, our most trusted foundational skills, including writing (86%), project management (83%), and delivering presentations (81%). 🚨 60% of leaders surveyed admit their organizations currently have internal skill gaps when it comes to AI and data. They warn that this lack of literacy directly leads to slower rates of innovation, poor decision-making, and reduced competitiveness, according to a new interesting research published by DataCamp using data from a survey of 517 US and UK business leaders conducted in partnership with YouGov . ☝️ 𝙈𝙮 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡 𝙫𝙞𝙚𝙬: When I look at these new findings, my mind immediately goes beyond the corporate boardroom and straight into our classrooms. For generations, our education system has been built on a core foundation: reading and writing. We spend over a decade teaching children how to craft the perfect essay, structure their arguments, and communicate clearly. But if data is truly the new language of the modern world, our school curriculums are drastically out of date. We can't wait until people enter the workforce to teach them how to read a chart, spot a statistical bias, or interpret a dataset. If data literacy is now exactly as critical as writing for professionals, we must start teaching it to our kids with the exact same urgency. It is time to add Data to the ABCs... 🙏 Thank you DataCamp researchers team for these insightful findings: Jonathan Cornelissen 🔑Are we training our teams for this new reality, or are we still treating data like a niche technical skill? #DataLiteracy #FutureOfWork

The book "Generative AI in Higher Education: The ChatGPT Effect" examines the profound shift in the academic landscape following the rise of Large Language Models, framing the future as a period of significant educational uncertainty regarding assessment, pedagogy, and the very definition of learning. Uncertainty in Assessment and Academic Integrity A primary concern is the potential collapse of traditional methods used to evaluate student knowledge. -The "Cheating" Wildcard: There is deep uncertainty about how to distinguish between genuine student effort and AI-generated output, leading to a crisis of trust in high-stakes testing. -Obsolescence of Traditional Tasks: Standard assignments, such as the five-paragraph essay, face an uncertain future as AI can produce them in seconds, forcing educators to reconsider what "evidence of learning" looks like. -Detection Efficacy: The report highlights the unpredictable reliability of AI-detection tools, creating a volatile environment where false positives and negatives disrupt the teacher-student relationship. Pedagogical and Curricular Uncertainty The document explores the "unknown" future of how subjects should be taught when AI can serve as a universal tutor. -The Role of the Educator: There is uncertainty regarding the future role of professors—transitioning from "knowledge providers" to "learning facilitators"—and whether institutions can adapt their training fast enough. -Curriculum Lag: A critical uncertainty is the "lag" between the rapid advancement of AI capabilities and the slow pace of institutional curriculum reform, potentially leaving graduates ill-prepared for an AI-integrated workforce. .Standardized Learning Risks: There is a concern that over-reliance on AI-generated content might lead to a "homogenization" of thought, where students lose the ability to engage in unique, critical inquiry. Ethical and Socio-Economic Uncertainty The broader societal implications of AI in education introduce significant strategic wildcards. -The "AI Divide": There is profound uncertainty regarding whether generative AI will democratize education by providing personalized support or exacerbate existing inequalities between those with and without access to premium AI tools. -Data and Bias: The future reliability of AI as an educational resource is shadowed by uncertainty regarding the "black box" nature of its training data and the potential for embedded algorithmic biases to influence student worldviews. In conclusion, the document suggests that higher education is at a pivotal crossroads. The future is defined not by the certainty of AI’s dominance, but by the uncertainty of whether human institutions can reinvent themselves fast enough to harness AI's potential while protecting the core values of critical thinking and academic rigor.

You ran the data meeting on Friday. Everyone nodded. Nothing changed on Monday. Here's what really happened. Data was collected. The team discussed the data. But nobody decided 𝙝𝙤𝙬 𝙩𝙤 𝙩𝙚𝙖𝙘𝙝 𝙙𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩𝙡𝙮. Here's the problem: we've confused 𝘤𝘰𝘭𝘭𝘦𝘤𝘵𝘪𝘯𝘨 data with 𝘶𝘴𝘪𝘯𝘨 it. Data without a clear instructional response isn't a system. It's a filing cabinet. So what does acting on data actually look like? After your next assessment, before your data meeting, ask your team one question: "𝗕𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗱𝗮𝘁𝗮, 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝘄𝗲 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆 𝗻𝗲𝘅𝘁 𝘁𝗶𝗺𝗲?" Not re-teaching the same lesson. Not moving on and hoping it clicks. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆? Here's a simple three-step protocol to make that question actionable: 𝗦𝘁𝗲𝗽 𝟭: 𝗡𝗮𝗺𝗲 𝘁𝗵𝗲 𝗺𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. Don't stop at "students got question 4 wrong." Ask why. Was it a procedural error? A conceptual gap? A language barrier? The misconception tells you how to respond. The mistake only tells you something went wrong. 𝗦𝘁𝗲𝗽 𝟮: 𝗠𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗼𝘃𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻. If students have a conceptual gap, teachers should use the CRA model (Concrete, Representational, Abstract) as a guide. Start with manipulatives or real-world context, move to visuals, then rebuild the abstract. If it's procedural, slow down the steps and make student thinking as visible as possible. The response has to match the root cause, not just re-cover the content. 𝗦𝘁𝗲𝗽 𝟯: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗮𝗻𝗱 𝗮𝘀𝘀𝗶𝗴𝗻 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗯𝗲𝗳𝗼𝗿𝗲 𝗹𝗲𝗮𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗼𝗼𝗺. Every instructional response needs a name attached to it. Who is trying what, in which class, by when and what does that instruction actually look like? Without ownership, the plan dies in the meeting. 𝗗𝗮𝘁𝗮 𝗺𝗲𝗲𝘁𝗶𝗻𝗴𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗲𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗽𝗹𝗮𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁. ♻️ If this idea resonates, repost to help school leaders and math teams turn data into action, not just conversation. 📧 If you're interested in more practical strategies like this, I'm launching a new newsletter called The 3-1-4, where I share practical strategies for improving math instruction and leadership. The first issue goes out on Pi Day (March 14). Link in the comments. _______________________________ Hi, I'm Dwight Williams. A proud first-gen everything, and I help schools and districts strengthen math instruction through coaching, curriculum support, and data-informed systems that drive student confidence and achievement. 👍🏿 Like | 🔔 Follow | 💬 Comment | 🔁 Repost

COVID-19 induced school closures did not result in learning losses everywhere! My new paper with Syedah Aroob Iqbal shows one country where pandemic school closures did not harm student learning. Despite widespread school disruptions in Uzbekistan, grade-5 math scores actually IMPROVED by 0.29 standard deviations during the pandemic period. Even more striking: students tested in 2019 and retested in 2021 showed remarkable gains of 0.72 standard deviations over those 2 years. This suggests that learning continuity was maintained despite COVID-induced disruptions to traditional schooling. Uzbekistan's experience demonstrates that effective responses – perhaps national TV broadcasts of daily lessons by best teachers in the country – can actually support continued academic progress during crisis periods. The findings raise important questions about what policies and practices enabled this success, and how other education systems might learn from Uzbekistan's approach to maintaining learning continuity during unprecedented disruptions. https://shorturl.at/Fxl2c It was with some trepidation that I looked towards distance education done right to alleviate the situation. I am glad I was proven right, but of course, this is all due to the students, families, teachers, administrators, and Ministry of Education of Uzbekistan. (Me on Uzbek TV in 2020 https://lnkd.in/eJQfa3E4. [For background, my blog with Nodira Meliboeva and Janssen Teixeira in 2020 on what Uzbekistan did: https://lnkd.in/eJDy3d7Y.

How well are we preparing our young learners for the demands of the 21st-century workforce? As the 21st century redefines the boundaries of work and technology, the question is no longer if we should change, but how fast teachers and other stakeholders can adapt their strategies to prepare our young learners for the realities of this new era. The integration of digital technology and AI has fundamentally changed how we communicate with one another, work, access information and solve problems. It has become an extension of how we think and operate in the world. As a result, it has become essential for contemporary education to evolve in response to these realities. In the past, teaching and learning centred on the transmission of knowledge to learners and ensuring that they can reproduce the knowledge when required. However, in an era where information is readily available at the click of a button, this approach is no longer productive. Digital technologies and AI tools can now perform many of the tasks that were traditionally taught in schools. Consequently, the purpose of education MUST be redefined. We must stop training learners to compete with machines! Instead, we must cultivate the capacities that technology cannot easily replicate: Higher-Order Thinking Skills (HOTS) like critical reflection, logical reasoning and creative problem-solving. If we fail to teach these skills, we risk preparing learners for a world that no longer exists. Here is how we shift the needle today: For Educators: ▶️ Don’t skip the basics, but don’t linger there either. ▶️Allow students to grapple with complex problems without giving them the answer immediately. This helps build their cognitive “muscle” required for creative problem solving. ▶️Encourage students to build their digital storytelling skills. They should find different ways to design their thoughts and perspectives outside of the traditional essay. For Instructional Designers: ▶️Move beyond multiple-choice quizzes. Design graphic organiser-style exercises and role-playing scenarios for analysis, peer-review forums for evaluation and project-based submissions for creation. For Curriculum Developers: ▶️Create units that connect subjects together. ▶️Ensure that national or school-wide standards place more importance on the application of knowledge than on the volume of content covered. ▶️Explicitly build design thinking into the curriculum as a formal methodology for problem-solving. For School Owners & Administrators: ▶️Shift teacher training away from managing classrooms and towards “facilitating” discussions in the classroom. ▶️Redesign learning spaces to allow for collaborative zones that facilitate group discussion. ▶️Measure school success not just by standardised test scores (these tests lower-level skills), but by student portfolios and projects. #Education #LessonPlanning #EdTech #HigherOrderThinking #BloomsTaxonomy #FutureOfLearning #TeachingStrategies

Despite high enrollment numbers, many states in India are silently battling a learning crisis. In Rajasthan, nearly 88% of Grade 5 students once struggled with basic division. Post-pandemic, foundational literacy and numeracy levels dropped sharply, with many children falling two grade levels behind. The real issue wasn’t access. It was foundational learning. And this is where AI helped the Rajasthan government tackle the challenge. Rajasthan implemented one of the largest AI deployments in its public education system, supported by Boston Consulting Group in collaboration with the Michael & Susan Dell Foundation India. They developed an app called Shikshak App that significantly improved the efficiency and proficiency of teachers. The app does the following: ➜ Teaching was broken into manageable micro-steps. ➜ Short instructional videos guided competency-based delivery. ➜ Assessments were digitized within seconds. ➜ Real-time student data generated personalized teaching recommendations. This AI-powered app empowered teachers in such a way that fewer teachers were able to effectively teach larger groups, without compromising quality. In regions where teacher shortages are acute and classrooms are crowded, this model becomes far more effective. The impact of this initiative has been extraordinary: ✔ 400,000 students moved out of learning poverty. ✔ 18% reduction in students lagging two or more grade levels. ✔ Assessment time reduced from 5–6 minutes to just 30–40 seconds per child. ✔ Real-time classroom data enabled faster, targeted interventions. AI has huge potential to transform every industry, including education, and create meaningful impact. When implemented thoughtfully, especially at the grassroots level, it can bridge gaps in teacher availability, standardize foundational learning, and elevate learning outcomes across socioeconomic backgrounds. If more states adopt such models for rural and regional ecosystems, we can move from high enrollment to high learning outcomes. And that is the real transformation India needs. How are you leveraging AI to improve learning outcomes in your institutions? - Charu Jain #AIinEducation #EducationReform #EdTech #LearningOutcomes #IndiaEducation #PublicPolicy #FutureOfEducation

Mathematics in Higher Education: Between Core Foundations and Civilisational Knowledge The recent debate on the UGC’s draft undergraduate mathematics curriculum, highlighted in The Indian Express, brings to the surface an old but unresolved question: should higher education in India focus narrowly on core disciplinary content, or should it open itself to broader civilisational knowledge systems and contemporary applications? R. Ramanujam, a mathematician and professor at Azim Premji University, warns that the proposed framework risks weakening rigour by compromising on core mathematical structures such as analysis, algebra, and abstract reasoning. He argues that if students cannot confidently navigate calculus or proof-based mathematics, they will be ill-equipped for both academic research and applied sciences. His concern is not new. Globally, scholars such as Niss (2007) have cautioned that weakening mathematical rigour at the undergraduate level diminishes a nation’s long-term intellectual capital in engineering, economics, and data sciences. On the other side, Prof. Mamidala Jagadesh Kumar, chair of the UGC, argues that the framework is not discarding core mathematics but contextualising it within India’s intellectual traditions and contemporary applications. By invoking Madhava’s work on infinite series and Kerala’s Yuktibhasa, he insists that the curriculum could restore historical depth while also integrating modern needs such as computational thinking, data literacy, and applied problem solving. Internationally, many countries have moved toward outcome-based curricula with flexibility across modules, and India’s proposal fits within this trend (see OECD, 2019 on future skills). I believe both positions deserve careful attention. Higher education must not shrink the cognitive horizons of students by diluting foundational rigour. At the same time, the teaching of mathematics should not be reduced to sterile symbol manipulation, detached from culture, history, and lived problem-solving. The challenge is to design a curriculum that can hold both: the precision of linear algebra and real analysis, and the civilisational insights of Madhava’s infinite series or Pingala’s combinatorics. For this, pedagogy matters as much as content. Research in mathematics education (Sfard, 1998; Schoenfeld, 2016) shows that students learn best when conceptual understanding, procedural fluency, and cultural contextualisation are taught in tandem. If the UGC framework is to succeed, it must avoid tokenism and instead train teachers to weave heritage knowledge meaningfully into classroom practice, while protecting the rigour that makes mathematics the language of science. This debate should not be framed as a binary. It is an invitation to imagine mathematics in India as simultaneously rigorous, applied, and rooted in civilisational depth. #MathematicsEducation #HigherEducation #UGC #IndianKnowledgeSystems #STEM #Pedagogy #CurriculumDesign #EducationReform