I've always believed that assessment is the unlock for systemic education transformation. What you measure IS what matters. Healthcare was transformed by a diagnostic revolution and now we are about to enter a golden era of AI-powered diagnostics in education. BUT we have to figure out WHAT we are assessing! Ulrich Boser's article in Forbes points the way for math: rather than assessing right answer vs wrong answer, assessments can now drill down to the core misconceptions in a matter of 8-12 questions. Instead of educators teaching the curriculum or "to standards" we now have tools that allow them teach to and resolve foundational misunderstandings of the core building blocks of math. When a student misses an algebra question is it due to algebraic math skills or is it multiplying and dividing fractions? Now we will know! Leading the charge is |= Eedi - they have mapped millions of data points across thousands of questions to build the predictive model that can adaptively diagnose misconceptions (basically each question learns from the last question), and then Eedi suggests activities for the educator or tutor to do with the student to address that misconception. This is the same kind of big data strategy used by Duolingo, the leading adaptive language learning platform. It's exciting to see these theoretical breakthroughs applied in real classrooms with real students! Next time we should talk about the assessment breakthroughs happening in other subjects. Hint: performance assessment tasks - formative & summative - are finally practical to assess!! #ai #aieducation Edtech Insiders Alex Kumar Schmidt Futures Eric The Learning Agency Meg Tom Dan #math Laurence Norman Eric https://lnkd.in/gxjj_zMW
Using Data to Create Tailored Learning Experiences
Explore top LinkedIn content from expert professionals.
Summary
Using data to create tailored learning experiences allows educators to personalize instruction by identifying students' unique needs, knowledge gaps, and strengths. This data-driven approach leverages tools such as AI and machine learning to adapt teaching methods, improve student outcomes, and make learning more engaging and effective.
- Focus on diagnostics: Use data to pinpoint specific misconceptions or gaps in understanding, enabling targeted interventions to address foundational issues.
- Adapt instruction dynamically: Implement adaptive learning platforms that adjust content to match students' competency levels, boosting retention and motivation.
- Tailor support strategies: Analyze student performance data to create personalized action plans, provide timely interventions, and improve overall learning experiences.
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"...Digital Personalized Learning (DPL) emerges as a promising and cost-effective alternative for math remediation. DPL leverages Artificial Intelligence (AI) and machine learning to provide students with adaptive instruction tailored to their competency levels, known as "Teaching at the Right Level" (TARL). The basic principle of TARL is to adapt instruction to match students' needs based on their prior knowledge. This adaptation enhances knowledge retention and motivation, while providing a strong foundation for future learning. Adaptive Learning is a promising mechanism to improve student skills and their perceptions about those skills, known as perceived self-efficacy, which is often associated with academic performance, especially in mathematics. DPL also offers pedagogical strategies and regular data for assessment, accessible through various devices with internet access." https://lnkd.in/dM5YBRti
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Hey #highered leaders - if you're still using static pivot tables to inform strategy, this post is for you ⤵ Take a peak at the below screenshot. This example, which shows two "paired predictors", is just one way you can turn data into action: 📈 ▶ The top right quadrant are “high achievers”. They have a high GPA + high credit earn ratio. These students might simply receive a message of encouragement. ▶ The top left quadrant are “strivers”. They have lower GPAs, but higher credits earned. These students might receive a nudge related to maximizing their use of available academic resources. ▶ The bottom right quadrant are “setbacks”. They have higher overall GPA, likely from good grades in their early coursework, but are earning fewer credits towards graduation requirements in key courses in their major. These students should probably receive messaging about the need for high-touch interaction with their advisors to stay on track and not lose their early momentum. ▶ The students in the bottom left quadrant are in "survival mode”. They are below average in both areas. These students are probably due for some real human-to-human conversation to better understand their needs. They may need in-depth intervention, with accompanied supports for finding the most successful path towards goals that match the students’ strengths and interests. You may consider nudging and re-nudging them throughout a term. ⤵ There's so many more examples of how Civitas Learning partners are disaggregating data to close equity gaps. If you're curious to learn more, let's connect 💌 #studentsuccessanalytics