AASA What Works: Data Analytics to Assess Learning Loss and Recovery
Join AASA’s guest, data analytics expert Dr. John Gatta in examining how to quantify learning loss and develop personalized learning recovery plans
Leveraging predictive models to measure learning loss and growth recovery during COVID-19 school closures
Measuring learning loss requires that we recognize that student growth is personal in the sense that every individual student’s learning trajectory leading up to the pandemic was different.
Measuring and Recovering Learning Losses During the COVID-19 Pandemic
Documenting learning loss is important as the effects of school closures on learning loss are likely differential and asymmetric, resulting in large losses for some students, and negligible or negative losses for other students.
The Benefits of Personal Statements and Capturing Student Voice in a Blended Learning Era
To effectively measure whole child growth, communication and engagement are vital. Having students write personal statements is an exercise that can aid districts in engaging students and measuring what matters.
Measuring Social and Emotional Learning
Social and emotional learning is about understanding who students are, not what they know. It’s internalizing an awareness that our biological systems are wired so that our emotions and interests drive our attention and, ultimately, our progress toward goals.
For chief executives, prioritizing SEL is a strategic issue. Effective implementation of SEL policies starts with the school district developing a clear and compelling vision for SEL that defines tangible outcomes the organization is striving to achieve.
Ready or Not: A Broader Definition
Measuring student success requires moving beyond standardized test scores toward a multidimensional and personalized set of indicators that collectively capture a more modern definition of readiness, empowering students to take ownership of their learning by aligning competencies, interests, motivations and aspirations.