Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

Autor/inn/en	Lu, Owen H. T.; Huang, Anna Y. Q.; Huang, Jeff C. H.; Lin, Albert J. Q.; Ogata, Hiroaki; Yang, Stephen J. H.
Titel	Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning
Quelle	In: Educational Technology & Society, 21 (2018) 2, S.220-232 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Blended Learning; Educational Technology; Technology Uses in Education; Data Collection; Data Analysis; Calculus; Mathematics Instruction; Regression (Statistics); Factor Analysis; Video Technology; Homework; Mathematics Tests; Scores; Tutoring; Predictor Variables; Mathematics Achievement; At Risk Students; College Students; College Mathematics; Large Group Instruction; Foreign Countries; Statistical Analysis; Taiwan + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Data capture; Datensammlung; Auswertung; Analysis; Differenzialrechnung; Infinitesimalrechnung; Integralrechnung; Mathematics lessons; Mathematikunterricht; Regression; Regressionsanalyse; Faktorenanalyse; Hausaufgabe; Förderkonzept; Nachhilfeunterricht; Prädiktor; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Collegestudent; Ausland; Statistische Analyse
Abstract	Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and is a part of our Precision education used to analyze and predict students' performance and provide timely interventions based on student learning profiles. This study applied learning analytics and educational big data approaches for the early prediction of students' final academic performance in a blended Calculus course. Real data with 21 variables were collected from the proposed course, consisting of video-viewing behaviors, out-of-class practice behaviors, homework and quiz scores, and after-school tutoring. This study applied principal component regression to predict students' final academic performance. The experimental results show that students' final academic performance could be predicted when only one-third of the semester had elapsed. In addition, we identified seven critical factors that affect students' academic performance, consisting of four online factors and three traditional factors. The results showed that the blended data set combining online and traditional critical factors had the highest predictive performance. (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Available from: National Sun Yat-sen University. Department of Information Management, 70, Lien-Hai Rd, Kaohsiung, 80424, Taiwan. Web site: http://www.ifets.info
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01