Interaction with learners

Schools are ready to innovate. To succeed, they need a better approach to data

In the wake of classroom disruption caused by the pandemic, schools across the country are recognizing the need for lasting structural change.

The lingering effects of learning loss have made it clear that whether schools are focused on meeting the needs of individual students, empowering educators to succeed, or making systemic changes to district-wide instructional models , such as the implementation of competency-based education, then it is time to commit to personalized education on a large scale.

But from application in a student’s learning journey to large-scale instructional redesign, any degree of impactful personalization requires clear, accurate, and actionable learning data.

McGraw Hill has developed technology that can transform a district’s approach to leveraging student data, empowering teachers to chart a unique path to each student’s growth and empowering administrators to illuminate those ways for all learning through lasting structural change. Here’s a look at the innovations on the horizon.

The State of Student Learning Data Today

In most districts, valuable data on student learning is fragmented across various assessments and instructional solutions. Quality data certainly exists – a plethora of information is collected in every digital interaction students have with online learning systems, and each of those interactions can tell us something important about their growth and needs. . But that data is split across classes, accounts, and platforms. As educators continue to leverage more solutions to reach more learners, such as intervention and acceleration programs, this data risks becoming more disconnected.

Agam Altyyev, director of studies at LISA Academy in Arkansas, describes the state of student data in his district: “Most of the time, raw data is what was sent to teachers, without colors, shapes or dynamic elements, and they’re expected to derive meaningful data points from them and use them frequently. For example, I’m a data guru in my district. In parent-teacher conferences, I don’t want my teachers looking for information on different software to share benchmark grades and assessments. To solve this problem, I created holistic, dynamic and teacher-friendly reports using multiple data points and data analytics. After manually collecting and interpreting the data, Agam sends reports to his staff in the form of meticulously created graphs.

Agam’s work is extremely valuable – his staff use the information he collects to drive conversations with students and parents and to individualize instruction. But in a time when all educators are constantly being asked to do more with less, the kind of manual labor beyond expectations that Agam undertakes to collect, manipulate, and make sense of disparate data to illustrate student needs n That’s just not a realistic expectation to ask of any educator.

In this fragmented environment where educators take on the role of data scientists, technology must fill the void to translate data into meaningful stories about student learning journeys.

McGraw Hill’s vision for the next era of personalized learning

McGraw Hill Plus for PreK – 12an innovative new tool that connects fragmented data sources from multiple digital solutions into a holistic view of every student, leverages advanced automation and data science to transform the current state of student data. McGraw Hill Plus for PreK – 12 combines data from baseline, intervention, acceleration, and even summative assessment sources into a single view of student progress, comparing it to state standards and corresponding competencies.

As students chart their own paths exploring concepts and demonstrating their growth in a digital learning ecosystem, teachers can watch those paths unfold with remarkable clarity.

McGraw Hill Plus for PreK – 12 goes beyond making data accessible to making it actionable. By drawing on learning resources from an extensive library of content, the tool’s recommendation engine offers enrichment and intervention resources that teachers can assign to students with just a few clicks. The richness and precision of the data collected allow learning scientists and data scientists behind McGraw Hill Plus for PreK – 12 to map suggested remedial and enrichment skills directly into Vygotsky’s Zone of Proximal Development, meeting students where they need to be to achieve mastery.

Dr Shawn Smith, director of innovation at McGraw Hill School, believes McGraw Hill Plus for PreK – 12 will be an empowering tool for educators looking to personalize learning at scale. “Educators need to be able to validate their own instincts in the classroom. They know better than anyone the context of the complex needs of their students. If we can give them the right data at the right time and automate the information they need to make informed instructional decisions, the sky’s the limit for the innovation any district can pursue.

One step closer to district-wide teaching and learning transformations

At the individual or class level, McGraw Hill Plus for PreK – 12 will open up personalized journeys, foster student action, and enhance the knowledge available to educators. But on a larger scale, this kind of innovation could be a key factor in driving the transformative systemic changes that district leaders seek post-pandemic.

Mastery and competency-based learning models are getting a lot of attention, and for good reason. It’s clearer than ever that time spent sitting is a poor indicator of where students fit into their learning journey and that learning needs vary more widely across student populations than ever before. But moving an entire school, let alone a district, to a competency-based model is no small feat. This requires a scalable and efficient approach to personalized learning and rapid access to actionable data.

Dr. Katie McClarty, director of studies at McGraw Hill School, believes that new approaches to data are essential for schools to pursue these innovative teaching models.

“Competency-based education requires educators to target clearly defined skills, use specific measures of skills, and tailor instruction to help each student achieve mastery.

Most of the time, however, a lesson will address several skills at the same time and several approaches are used to determine if a student has truly achieved mastery. McGraw Hill Plus for PreK – 12 provides a point-of-use visual of individual student skill growth through proficiency level, using multiple data sources to provide detailed information on what a student has mastered and where further learning is needed necessary. This allows educators to focus their time on teaching and helping students, rather than evaluating, combining, and interpreting data.

Implementing a mastery or competency-based model is inherently dependent on evolutionary personalization, which Dr. McClarty says comes down to data. “To achieve true personalization at scale, there needs to be an easy way to integrate information from a variety of sources and provide quick insight into student progress. »

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