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Article Type

Professional Proficiency

Article Abstract

Related Issue:

In recent years we have developed a data analytics pipeline using artificial neural networks to predict prospective student matriculation for university admissions using very limited demographic data. Predictions are generated at the earliest stages of the admissions process and successfully inform recruiting and admissions staff about the likelihood of matriculation. Results over numerous years of matriculation predictions are highly predictive and reliably consistent. We provide a detailed account of data collection, formatting, and transformation processes used, enabling others to replicate the process and results.

Author(s)

hansen-d--smDavid M. Hansen received his Ph.D. in Computer Science and Engineering in 1995 from the Oregon Graduate Institute of Science & Technology. Dr. Hansen is currently an Associate Professor of Computer Science and Information Systems at George Fox
University where he teaches a wide range of undergraduate courses that span the gamut from AI to Architecture & Assembly Language. Recent research has included Computer Science pedagogy, applied machine learning, and partition-tolerant
distributed information systems.

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Endnotes

  1. The program used to access the ACS database retrieves and caches zip code data with a web service call such as the Python code fragment in Figure 1. A free “API Key” must be obtained and embedded in the call to use the ACS web service; see <census.gov/developers/>.

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