Improving Early Warning Systems with Categorized Course Resource Usage

Autor/inn/en	Waddington, R. Joseph; Nam, SungJin; Lonn, Steven; Teasley, Stephanie D.
Titel	Improving Early Warning Systems with Categorized Course Resource Usage
Quelle	In: Journal of Learning Analytics, 3 (2016) 3, S.263-290 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Dropout Prevention; Data Analysis; STEM Education; Core Curriculum; Educational Resources; Grades (Scholastic); Regression (Statistics); Correlation; College Students; Differences + Suchen Sie Ihr Suchwort? Auswertung; STEM; Kerncurriculum; Bildungsmittel; Notenspiegel; Regression; Regressionsanalyse; Korrelation; Collegestudent; Unterscheiden
Abstract	Early Warning Systems (EWSs) aggregate multiple sources of data to provide timely information to stakeholders about students in need of academic support. There is an increasing need to incorporate relevant data about student behaviors into the algorithms underlying EWSs to improve predictors of students' success or failure. Many EWSs currently incorporate counts of course resource use, although these measures provide no information about which resources students are using. We use seven years of data from seven core STEM courses at a large university to investigate the associations between students' use of categorized course resources (e.g., lecture or exam preparation resources) and their final course grade. Using logistic regression, we find that students who use exam preparation resources to a greater degree than their peers are more likely to receive a final grade of B or higher. In contrast, students who use more lecture-related resources than their peers are less likely to receive a final grade of B or higher. We discuss the implications of our results for developers deciding how to incorporate categories of course resource usage data into EWSs, for academic advisors using this information with students, and for instructors deciding which resources to include on their LMS site. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01