Innovative learning analytics for evaluating instruction. A big data roadmap to effective online learning.

Autor/inn/en	Frick, Theodore Wayn; Myers, Rodney Dean; Dagli, Cesur; Barrett, Andrew F.
Titel	Innovative learning analytics for evaluating instruction. A big data roadmap to effective online learning.
Quelle	London; New York: Routledge (2022), xxii, 131 S.Verfügbarkeit
Reihe	Routledge focus
Beigaben	Illustrationen
Sprache	englisch
Dokumenttyp	gedruckt; Monographie
ISBN	1-03-200018-X; 978-1-03-200018-3
Schlagwörter	Analytische Methode; Empirische Untersuchung; Evaluation; Methodologie; Lernerfolg; Lernen; Lernforschung; Lernverhalten; Lernmethode; Datenanalyse; E-Learning; Zeitbudget; Innovation; Instruktion; Qualität; Online; USA
Abstract	Innovative Learning Analytics for Evaluating Instruction covers the application of a forward-thinking research methodology that uses big data to evaluate the effectiveness of online instruction. Analysis of Patterns in Time (APT) is a practical analytic approach that finds meaningful patterns in massive data sets, capturing temporal maps of students' learning journeys by combining qualitative and quantitative methods. Offering conceptual and research overviews, design principles, historical examples, and more, this book demonstrates how APT can yield strong, easily generalizable empirical evidence through big data; help students succeed in their learning journeys; and document the extraordinary effectiveness of First Principles of Instruction. It is an ideal resource for faculty and professionals in instructional design, learning engineering, online learning, program evaluation, and research methods. (Verlag).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2022/2