Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis.

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Autor/Urheber	Alex J. Bowers
Institution	Practical Assessment, Research & Evaluation
Titel	Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis.
Quelle	In: 1531-7714; Practical Assessment, Research & Evaluation, Vol 15, Iss 7, Pp 1-18 (2010)(2010) PDF als Volltext kostenfreie Datei
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
Schlagwörter	Elementary School Students; Secondary School Students; School Districts; Grades (Scholastic); Multivariate Analysis; Data; Decision Making; Dropouts; Identification; Data Analysis; Theory and practice of education
Abstract	School personnel currently lack an effective method to pattern and visually interpret disaggregated achievement data collected on students as a means to help inform decision making. This study, through the examination of longitudinal K-12 teacher assigned grading histories for entire cohorts of students from a school district (n=188), demonstrates a novel application of hierarchical cluster analysis and pattern visualization in which all data points collected on every student in a cohort can be patterned, visualized and interpreted to aid in data driven decision making by teachers and administrators. Additionally, as a proof-of-concept study, overall schooling outcomes, such as student dropout or taking a college entrance exam, are identified from the data patterns and compared to past methods of dropout identification as one example of the usefulness of the method. Hierarchical cluster analysis correctly identified over 80% of the students who dropped out using the entire student grade history patterns from either K-12 or K-8.
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