Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…

State education data systems help policymakers use data to evaluate the impact of their efforts to improve education. By disaggregating the data -- that is, breaking it out by different student subgroups -- policymakers can ensure that their efforts address the needs of students who have been traditionally underserved in educational settings. Yet…

Science instructors are increasingly incorporating teaching techniques that help students develop core competencies such as critical-thinking and communication skills. These core competencies are pillars of career readiness that prepare undergraduate students to successfully transition to continuing education or the workplace, whatever the field.…

The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…

Data from the National Education Longitudinal Study of 1988-2000 (NELS: 88) were used to examine the characteristics of students who see their school counselor about general, academic, career, and academic issues. Study results indicated that overall, school counselors were more likely to have contact with students who are identified as at-risk…

Overreliance on enrollment figures for comparative analysis of higher education systems may create misunderstanding because data often lump together what is institutionally different, and the notion of "higher education student" may conceal completely different conditions, even within the same sector. (MSE)

Data-mining technology's predictive modeling was applied to enhance the prediction of enrollment behaviors of admitted applicants at a large state university. (Contains 4 tables and 6 figures.)

Focusing on student retention and time to degree completion, this study illustrates how institutional researchers may benefit from the power of predictive analyses associated with data-mining tools. The following are appended: (1) Predictors; and (2) Variable Definitions. (Contains 5 figures.)

This report discusses the outcomes of a study that investigated the relationship between student scores on the five sub-scales of the School Attitude Assessment Survey-Revised (SAAS-R) and the academic achievement of known groups of gifted achievers and gifted underachievers. The study examined whether gifted achievers and gifted underachievers…