Anthony De Prato, Chief Information Officer, St. Andrew's Episcopal School

As a seasoned Director of Technology and Chief Technology Officer with nearly two decades of experience, Anthony De Prato has successfully led numerous enterprise-level IT projects, fostered strong vendor relationships, and developed cutting-edge curricula in robotics, STEAM, and computer science. His expertise in data analysis enables him to make informed decisions that drive organizational success. In addition to his practical experience, Anthony holds a Master’s in Educational Technology from Pepperdine University and a Bachelor of Arts in Communications and International Studies from St. Thomas More College. Moreover, his Principals’ Training Center Admin Certification, earned in 2016, attests to his commitment to ongoing professional development. As a future-thinking and detail-oriented professional, he excels in program design and implementation, as well as in tracking and measuring outcomes to ensure their success. Anthony brings a strategic mindset, strong leadership skills, and a passion for leveraging technology to improve educational outcomes to every project he undertakes.


Micro-analytics – for this article, it refers to the in-depth analysis of granular or small-scale data. It’s a highly targeted approach to data analysis that focuses on detailed, micro-level information to gain insights into specific aspects or components of a more extensive system or process. A particular definition of microanalysis could not be sourced, so I created one.

In the context of education, micro-analytics could involve analyzing individual student performance data across different assignments, subjects, or even questions within a single test. By closely examining these smaller, detailed pieces of data, educators can gain a nuanced understanding of student learning patterns, strengths, weaknesses, and areas that need improvement.

While traditional macro-level analytics provide a broad overview or generalized conclusions, micro-analytics allow a more detailed and personalized understanding of the data. This targeted information is often crucial to making precise changes, improving processes, or personalizing learning experiences, which can be particularly beneficial in iterative methods where continual refinement is paramount.

Micro-analytics is about digging deep into the data to uncover nuanced insights and patterns that can drive more precise, targeted actions or improvements. As K-12 administrators, our decisions shape the future of education. We are the architects of systems that mold the minds of tomorrow, the enablers of innovation and progress in the classroom. One of the prevailing debates in educational management is about the choice between implementing sporadic, expensive innovations and adopting regular, consistent iterations.

Spontaneous innovation has an allure. It’s captivating, groundbreaking, and the change it promises can often be irresistible. The “new” can indeed lead to significant improvements in educational outcomes. However, it also carries substantial risks. Unforeseen complications can arise from the untested and experimental nature of such innovations. In addition, high upfront costs and the potential for system-wide disruption are also significant concerns. On the other hand, regular iteration – the consistent and intentional modification and improvement of our systems and practices – can provide a more sustainable and manageable pathway for progress.

The value of iteration can be compared to compound interest in financial investing, where small, consistent investments accrue into a substantial sum over time. Similarly, constant, incremental improvements in teaching methodologies, curriculum, administrative processes, and technological adoption can result in significant long-term benefits for our students and institutions.

One advantage of regular iteration is that it allows us to test, adapt, and improve on a small scale before fully implementing a change. This model of progress can mitigate the risks associated with spontaneous, large-scale innovation, as it provides opportunities for real-time adjustment and minimizes disruptions to the educational ecosystem. It empowers teachers and administrators to actively participate in the improvement process actively, fostering a culture of continuous learning and adaptation.

Regular iteration is generally less financially demanding than sporadic, large-scale innovation. Reducing the initial financial burden allows for more manageable budget allocation and planning. It also promotes a more equitable distribution of resources, enabling all schools within a system, regardless of size or budget, to implement progressive changes and reap their benefits.

Most importantly, iteration is more of a student-centric approach. It centers on understanding the needs of students and responding with measured changes. It allows us to respond more quickly and effectively to the ever-evolving needs of our students rather than waiting for the next big innovation to address those needs. This way, we can ensure that our students receive the best possible education at all times.

While spontaneous and expensive innovations can provide notable improvements, the power of regular iteration holds the promise of consistent, sustainable progress in our education system. As K-12 administrators, we can leverage this approach to manage risk, optimize budget utilization, foster an environment of continuous learning, and, above all, keep our commitment to providing the best possible education for our students.

The role of artificial intelligence (AI) in enhancing micro-analytics cannot be overstated, particularly in the context of fostering growth and attainment through iterative processes. With their data-crunching capabilities, AI tools make digesting large volumes of information more manageable, efficient, and precise. They allow us to dig into the minutiae of day-to-day operations, providing insights into trends and patterns that would otherwise go unnoticed.

These processes are unique. Artificial Intelligence (AI) has fundamentally revolutionized our relationship with data. Unlike traditional analytical systems, AI does not require labor-intensive data preparation and gatekeeping to make sense of the data.  The conventional approach typically involves substantial time and resources to clean, prepare, and structure the data before any meaningful analysis can begin. This process often requires manual coding to parse the data, identify relevant metrics, and generate reports.

Contrarily, AI has the innate ability to ingest and analyze large volumes of data directly from various sources in its raw, unstructured form. One of the most exciting capabilities of AI is its ability to express insights conversationally, making data analysis accessible and understandable to a broader audience. The AI tool can process a continuous stream of data, extract relevant insights, and relay them in a natural, human-like dialogue.

This AI-fueled process allows for real-life, interactive analysis, where users can ask questions and receive responses in the language, they understand without needing data science or programming expertise. In the educational sector, AI-powered micro-analytics can be instrumental in driving iterative improvement. By analyzing the nuances of student performance data, curriculum effectiveness, and teacher-student interactions, AI tools can pinpoint specific areas where changes can lead to significant improvements. These insights enable administrators and educators to implement slight, targeted modifications and assess their impact, leading to a cycle of continuous, data-driven improvement.

Moreover, AI tools are capable of real-time data analysis, allowing for immediate feedback on implemented changes. This immediacy can accelerate the iteration cycle, facilitating quicker adjustments and fostering a more responsive educational environment. As a result, schools can better adapt to the dynamic needs of their students, promoting growth and attainment more effectively and efficiently.

AI tools, through their advanced micro-analytics capabilities, have the potential to revolutionize the iterative process in education. By providing granular insights and real-time feedback, they empower educational institutions to optimize operations, enhance student learning outcomes, and ensure sustainable growth. In a rapidly evolving educational landscape, the power of AI in driving iterative improvement is a resource we must tap into. Iterative work is routine, rarely controversial, and doesn’t usually require a significant budgetary commitment.

In essence, iteration is a controlled and reversible force multiplier for change. A system’s design is its destiny, and iterative systems are designed to grow at a rate people can adapt to while ensuring significant problems and failure of mission are not realized. To move K-12 education out of the quagmire, it often finds itself in, we do not need an innovation budget; we need an iterative mandate.

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