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Download full textCohausz, Lea; Tschalzev, Andrej; Bartelt, Christian; Stuckenschmidt, Heiner – International Educational Data Mining Society, 2023
Demographic features are commonly used in Educational Data Mining (EDM) research to predict at-risk students. Yet, the practice of using demographic features has to be considered extremely problematic due to the data's sensitive nature, but also because (historic and representation) biases likely exist in the training data, which leads to strong…
Descriptors: Information Retrieval, Data Processing, Pattern Recognition, Information Technology
Rodriguez, Sheila M.; Estacion, Angela – Regional Educational Laboratory Northeast & Islands, 2014
As the name indicates, the College Readiness Data Catalog Tool focuses on identifying data that can indicate a student's college readiness. While college readiness indicators may also signal career readiness, many states, districts, and other entities, including the U.S. Virgin Islands (USVI), do not systematically collect career readiness…
Descriptors: College Readiness, Data, Educational Indicators, Data Collection
Shettle, Carolyn; Cubell, Michele; Hoover, Katylee; Kastberg, David; Legum, Stan; Lyons, Marsha; Perkins, Robert; Rizzo, Lou; Roey, Stephen; Sickles, Diane – US Department of Education, 2008
This technical report documents the procedures used to collect and summarize data from the 2005 High School Transcript Study (HSTS 2005). The transcript studies serve as a barometer for changes in high school graduates' course-taking patterns; these patterns provide information about the rigor of high school curricula followed across the nation.…
Descriptors: Check Lists, High Schools, School Activities, Course Selection (Students)
