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Pytlarz, Ian; Pu, Shi; Patel, Monal; Prabhu, Rajini – International Educational Data Mining Society, 2018
Identifying at-risk students at an early stage is a challenging task for colleges and universities. In this paper, we use students' oncampus network traffic volume to construct several useful features in predicting their first semester GPA. In particular, we build proxies for their attendance, class engagement, and out-of-class study hours based…
Descriptors: College Freshmen, Grade Point Average, At Risk Students, Academic Achievement
Dietz-Uhler, Beth; Hurn, Janet E. – Journal of Interactive Online Learning, 2013
Learning analytics is receiving increased attention, in part because it offers to assist educational institutions in increasing student retention, improving student success, and easing the burden of accountability. Although these large-scale issues are worthy of consideration, faculty might also be interested in how they can use learning analytics…
Descriptors: Technology Uses in Education, College Students, Academic Achievement, Prediction
Aguiar, Everaldo; Ambrose, G. Alex; Chawla, Nitesh V.; Goodrich, Victoria; Brockman, Jay – Journal of Learning Analytics, 2014
As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out, or who may be following a suboptimal path to success, allows those in charge not only to understand the…
Descriptors: Academic Persistence, Engineering Education, Portfolios (Background Materials), Dropouts
Smith, Vernon C.; Lange, Adam; Huston, Daniel R. – Journal of Asynchronous Learning Networks, 2012
Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student…
Descriptors: Academic Achievement, At Risk Students, Prediction, Community Colleges
Data Quality Campaign, 2010
Now that all 50 states and the District of Columbia are building statewide longitudinal data systems, the next step is to ensure that the information in these systems is used to improve student learning. The Data Quality Campaign (DQC) has identified 10 actions that states can take to ensure that the right data are available and accessible and…
Descriptors: Academic Achievement, Feedback (Response), High School Graduates, Graduation Rate
Campbell, John P.; DeBlois, Peter B.; Oblinger, Diana G. – EDUCAUSE Review, 2007
In responding to internal and external pressures for accountability in higher education, especially in the areas of improved learning outcomes and student success, IT leaders may soon become critical partners with academic and student affairs. IT can help answer this call for accountability through "academic analytics," which is emerging…
Descriptors: Accountability, Higher Education, Information Technology, Outcomes of Education