Why Use Automated Machine Learning?

Machine learning is involved in search, translation, detecting depression, likelihood of college dropout, finding lost children, and to sell all kinds of products. While barely beyond its inception, the current machine learning revolution will affect people and organizations no less than the Industrial Revolution’s effect on weavers and many other skilled laborers. Machine learning will automate hundreds of millions of jobs that were considered too complex for machines ever to take over even a decade ago, including driving, flying, painting, programming, and customer service, as well as many of the jobs previously reserved for humans in the fields of finance, marketing, operations, accounting, and human resources. This section explains how automated machine learning addresses exploratory data analysis, feature engineering, algorithm selection, hyperparameter tuning, and model diagnostics. The section covers the eight criteria considered essential for AutoML to have significant impact: accuracy, productivity, ease of use, understanding and learning, resource availability, process transparency, generalization, and recommended actions.

Reframing Achievement Setbacks: A Motivation Intervention to Improve 8-Year Graduation Rates for Students in Science, Technology, Engineering, and Mathematics (STEM) Fields

Despite increased emphasis on educating students in science, technology, engineering, and mathematics (STEM) disciplines, nearly half of U.S. college students who enroll in these programs fail to graduate with STEM degrees. Using archival data from the Motivation and Academic Achievement Database, we tested whether a motivation intervention to reframe causal attributions for academic setbacks improved graduation rates for college students in STEM disciplines ( N = 496). Results showed that the intervention increased the odds of 8-year graduation for students who were at risk of college dropout. Findings highlight the potential of theory-informed psychological interventions to increase persistence to graduation for at-risk students in STEM fields.

Reducing community college dropout through comprehensive supports

Providing community college students with a wide range of comprehensive supports, such as counseling, tutoring, and financial assistance, can improve low rates of persistence and graduation. These support programs address many simultaneous barriers that students face, which may be a key driver behind their effectiveness.

Drop Out Students Identification Using Knowledge Base

The purpose of this study is modeling for the initial identification of college dropout students. Samples were taken from drop out student data for the past 4 years. Information from this sample will be acquired as a knowledge base in system modeling. The research aims to provide a knowledge-based system approach using Dempster Shafer for the management of student drop outs at universities especially in Yogyakarta. The symptoms of DO students are obtained from knowledge about DO that appears on campus in Yogyakarta. The system output is in the form of 3 groups of classification namely initial potential DO, enough potential and once potential. The results of the study produced a system that could help university managers deal with drop-out problems early.