NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
ERIC Number: ED582891
Record Type: Non-Journal
Publication Date: 2018-Apr
Pages: 20
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-
Data Visualization in Public Education: Longitudinal Student-, Intervention-, School-, and District-Level Performance Modeling
Lacefield, Warren E.; Applegate, E. Brooks
Online Submission, Paper presented at the Annual Meeting of the American Educational Research Association (New York, NY, Apr 13-17, 2018)
Accountability seems forever engrained into the K-12 environment, as has been the expectation of delivering quality education to school aged children and adolescents. Yet, repeated failure of this expectation has focused the public's and policy maker's attention on the limitations of major accountability systems. This paper explores applications of machine learning, predictive analytics, and data visualization to student information available to educational decision makers. In particular, we demonstrate how to use individual academic performance histories to identify "at-risk" students in real time for advising, academic coaching, and other support services and how to aggregate longitudinal data at the school or district level for system modeling, profiling, comparison, and intervention evaluation.
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Elementary Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Michigan; Ohio; Illinois