Mining LMS Data to Develop an "Early Warning System" for Educators: A Proof of Concept

Autor/inn/en	Macfadyen, Leah P.; Dawson, Shane
Titel	Mining LMS Data to Develop an "Early Warning System" for Educators: A Proof of Concept
Quelle	In: Computers & Education, 54 (2010) 2, S.588-599 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0360-1315
DOI	10.1016/j.compedu.2009.09.008
Schlagwörter	Network Analysis; Academic Achievement; At Risk Students; Prediction; Predictor Variables; Grades (Scholastic); Models; Learner Engagement; Predictive Validity; Data; Data Analysis; Evaluation Methods; Research Methodology; Educational Strategies; College Students; College Instruction; Teaching Methods; Correlation; Discourse Analysis; Content Analysis; Educational Technology; Computer Assisted Instruction; Electronic Learning; Computer Assisted Testing; Technology Integration; Integrated Learning Systems; Web Based Instruction; Computer Software; Computer Software Evaluation; Computer System Design + Suchen Sie Ihr Suchwort? Netzplantechnik; Schulleistung; Vorhersage; Prädiktor; Notenspiegel; Analogiemodell; Daten; Auswertung; Research method; Forschungsmethode; Lehrstrategie; Collegestudent; Hochschullehre; Teaching method; Lehrmethode; Unterrichtsmethode; Korrelation; Diskursanalyse; Inhaltsanalyse; Unterrichtsmedien; Computer based training; Computerunterstützter Unterricht; Web Based Training; Softwareanalyse
Abstract	Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international research project investigating which student online activities accurately predict academic achievement. Analysis of LMS tracking data from a Blackboard Vista-supported course identified 15 variables demonstrating a significant simple correlation with student final grade. Regression modelling generated a best-fit predictive model for this course which incorporates key variables such as "total number of discussion messages posted", "total number of mail messages sent", and "total number of assessments completed" and which explains more than 30% of the variation in student final grade. Logistic modelling demonstrated the predictive power of this model, which correctly identified 81% of students who achieved a failing grade. Moreover, network analysis of course discussion forums afforded insight into the development of the student learning community by identifying disconnected students, patterns of student-to-student communication, and instructor positioning within the network. This study affirms that pedagogically meaningful information can be extracted from LMS-generated student tracking data, and discusses how these findings are informing the development of a customizable dashboard-like reporting tool for educators that will extract and visualize real-time data on student engagement and likelihood of success. (Contains 5 figures and 6 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10