What Learning Analytics Tells Us: Group Behavior Analysis and Individual Learning Diagnosis Based on Long-Term and Large-Scale Data

Autor/inn/en	Zhang, Jia-Hua; Zhang, Ye-Xing; Zou, Qin; Huang, Sen
Titel	What Learning Analytics Tells Us: Group Behavior Analysis and Individual Learning Diagnosis Based on Long-Term and Large-Scale Data
Quelle	In: Educational Technology & Society, 21 (2018) 2, S.245-258 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Data Collection; Data Analysis; Educational Research; Diaries; Use Studies; Tests; Interaction; Academic Achievement; Statistical Analysis; Visualization; Network Analysis; Social Networks; Correlation; Learning Processes; Educational Technology; Technology Uses in Education; Group Behavior; Time Management; Feedback (Response); Integrated Learning Systems; Online Courses; Foreign Countries; Educational Resources; China + Suchen Sie Ihr Suchwort? Data capture; Datensammlung; Auswertung; Bildungsforschung; Pädagogische Forschung; Diary; Tagebuch; Benutzerschulung; Examination; Prüfung; Examen; Interaktion; Schulleistung; Statistische Analyse; Visualisation; Visualisierung; Netzplantechnik; Social network; Soziales Netzwerk; Korrelation; Learning process; Lernprozess; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Gruppenverhalten; Zeitmanagement; Online course; Online-Kurs; Ausland; Bildungsmittel
Abstract	The practice and application of education data mining and learning analytics has become the focus of educational researchers. However, it is still a difficult task to explore the law of group learning and the characteristics of individual learning. In this study, the online learning logs of 1,088 students from 22 classes were analyzed from the aspects of their login behaviors, resource utilization, quizzes, interactive behaviors, and academic achievement. To address these issues, multiple methods, including statistical analysis, visualization social network analysis and correlation analysis, were used to analyze the process and results of online learning. The results reveal the characteristics of group behavior of online learners and highlight the key factors that influence the learning process and outcomes of individual learners. From the view of students, these factors include the length and allocation of online time, the effective utilization of resources, social interaction, online learning support and services, etc. From the perspective of teachers, the factors include the management of online teaching, the appropriateness of learning resources, the effectiveness of online intervention strategies, the accurate feedback for online learners, etc. Therefore, learning analysis technology can not only standardize the assessment of learning outcomes, but can also focus more attention on the standardization of learning process assessment. It also identifies the main factors that affect the online learning outcomes and the group characteristics of online learners. At the same time, it provides the learners with personalized learning diagnosis reports which can both help learners understand their own learning status and promote instructors' accurate teaching and reasonable evaluation. (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