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Anyone who uses a smartphone or shops online has had their habits tracked, click by telling click. Big companies comb through that data to find patterns in human behavior and to understand, anticipate, and offer up goods and services we are most likely to purchase. Through predictive analytics, they identify trends and forecast our future choices.
This high-tech data crunch has become increasingly common in higher education, too. Colleges and universities are facing mounting pressure to raise completion rates and have embraced predictive analytics to identify which students are at risk of failing courses or dropping out. An estimated 1,400 institutions nationwide have invested in predictive analytics technology, with spending estimated in the hundreds of millions of dollars. Colleges and universities use these analyses to identify at-risk students who may benefit from additional support.
How accurate and stable are those predictions? In most cases, college researchers and administrators don’t know. Most machine-learning models used in higher education are proprietary and operated by private companies that provide little, if any, transparency about the underlying data structure or modeling they use. Different models could vary substantially in their accuracy, and the use of predictive analytics could lead institutions to intervene disproportionately with students […]