understand what their numbers mean in context, and push forward a collaborative, research-driven dialogue based on those real-world insights. We are working to disrupt teacher feedback by using AI conversational dialog with every student separately. Until there is a humongous database of millions and millions of essays, this problem will most likely be hard to work around. The IEA can be applied for both distance education and for training in the classroom. The essays have ranged in grade level, including middle school, high school, college and college graduate level essays. Over the past two years, the Intelligent Essay Assessor has been used in a course in Psycholinguistics at New Mexico State University. The issue is just that Google uses millions of data samples for their approximations. This solution has worked in many other applications. "Can we write during recess?" Some students were asking that question at Anna. The idea is to give teachers useful diagnostic information on each writer and give them more time to address problems and assist students with things no machine can comprehend content, reasoning and, especially, the young writer at work.
Research has shown that on-demand, low-barrier online resources create a disclosure miracle for people in these hard situations: they write more, and more often. There exist a variety of applications within education to which the IEA can be applied. The IEA uses Latent Semantic Analysis (LSA which is both a computational model of human knowledge representation and a method for extracting semantic similarity of words and passages from text. Finally, the IEA can be integrated with other software tools for education. Scoring essays for high-stakes exams is a reliable but utilitarian use of machine learning.
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