Accurately matching person records is a critical task in education, ensuring that every student and staff’s data is correctly attributed and maintained over time, location, and systems. At eScholar, we’re well-versed in the challenges of this area with our experience of developing and deploying eScholar Uniq-ID and eScholar DirectMatch in over a dozen states in the past twenty years. eScholar Uniq-ID v2024 combines traditional and modern matching techniques to ensure accurate matching.
Why Is Name Matching So Complex
One of the most challenging tasks of matching person records is matching names. This is because there isn’t a true right or wrong way to spell names. Take my name, Elissa, for example. I can count at least five ways to spell my name and none of them are wrong: Elyssa, Ellissa, Alisa, Alyssa, Elisa etc. Uniq-ID helps determine if Elyssa Seto is the same as Elissa Seto and that’s where matching techniques come into play.
Traditional vs. Modern Name Matching Techniques
Traditionally, name matching relied on straightforward string comparison methods, such as exact matching or phonetic algorithms. These methods are still useful and used in eScholar Uniq-ID, but they are not enough to get the most accurate and thorough matches, especially when dealing with variations in spelling, cultural differences, or typographical errors.
Modern name-matching techniques have evolved to address these limitations. Advanced algorithms consider multiple factors, including variations in spelling, transliterations, and even cultural naming conventions. For instance, eScholar Uniq-ID uses a two-pass methodology: an initial high-speed pass to identify potential matches, followed by a more detailed analysis using a multitude of matching algorithms, such as different types of deterministic, probabilistic, fuzzy, and phonetic matching techniques. In v2024, eScholar Uniq-ID now uses Machine Learning models trained on real-world data in the second, more detailed analysis of potential matches.
The Role of AI in Matching
Artificial Intelligence (AI) has revolutionized matching by introducing sophisticated natural language processing (NLP) algorithms that can understand and process names and other data across different languages and scripts. eScholar Uniq-ID uses a specific type of AI called Machine Learning, which is the development and deployment of statistical algorithms that learn from data to perform certain tasks. In this case, the task is matching. The Machine Learning capabilities of Uniq-ID also help generate the Match Scores, which are key in understanding how close of a match two records may be. These scores help automate decision-making processes by setting thresholds for what constitutes a match, thereby reducing false positives and negatives.
Why Accurate Name Matching Matters
As agencies are building out their longitudinal data systems (LDS) to include more than just K-12 student data, accurate matches and unique identifiers are more important than ever. Depending on how data from these types of systems are used, long and short-term benefits of eScholar Uniq-ID include:
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