Data analytics and psychometrics. Informing assessment practices.

Sonst. Personen	Jiao, Hong (Hrsg.); Lissitz, Robert W. (Hrsg.); Van Wie, Anna (Hrsg.)
Titel	Data analytics and psychometrics. Informing assessment practices.
Quelle	Charlotte, NC: Information Age Publishing, Inc (2019), vi, 261 S.Verfügbarkeit
Reihe	The MARCES book series
Beigaben	Illustrationen
Sprache	englisch
Dokumenttyp	gedruckt; Monographie
ISBN	1641133260; 1641133279; 9781641133265; 9781641133272; 9781641133289
Schlagwörter	Kompetenz; Bildungsforschung; Evaluation; Methodologie; Problemlösen; Psychometrie; Schülerleistung; Lernprozess; Soziale Software; Text; Datenanalyse; Ranking; Computerunterstütztes Verfahren; Messung; Technologiebasiertes Testen; E-Learning; Analyse; Leistungsmessung; Informationswissenschaft; PIAAC (Programme for the International Assessment of Adult Competencies); PISA (Programme for International Student Assessment)
Abstract	The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large-scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student's learning and guide teacher's instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2019/4