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Autor*innen: Drachsler, Hendrik; Goldhammer, Frank
Titel: Learning analytics and eAssessment. Towards computational psychometrics by combining psychometrics with learning analytics
Aus: Burgos, Daniel (Hrsg.): Radical solutions and learning analytics: Personalised learning and teaching through big data, Singapore: Springer, 2020 (Lecture Notes in Educational Technology), S. 67-80
DOI: 10.1007/978-981-15-4526-9_5
URL: https://link.springer.com/chapter/10.1007/978-981-15-4526-9_5
Dokumenttyp: 4. Beiträge in Sammelbänden; Sammelband (keine besondere Kategorie)
Sprache: Englisch
Abstract: From a psychometric point of view, assessment means to infer what a learner knows and can do in the real world from limited evidence observed in a standardized testing situation. From a learning analytics perspective assessment means to observe real behavior in digital learning environments to conclude the learner status with the intent to positively influence the learning process. Although psychometrics and learning analytics share similar goals, for instance, formative assessment, while applying different methods and theories, the two disciplines are so far highly separated. This chapter aims at paving the way for an advanced understanding of assessment by comparing and integrating the learning analytics and the psychometric approach of assessment. We will discuss means to show this new way of assessment of educational concepts such as (meta-) cognition, motivation, and reading comprehension skills that can be addressed either from data-driven approach (learning analytics) or from a theory-driven approach (psychometrics). Finally, we show that radically new ways of assessment are located in the middle space where both disciplines are combined into a new research discipline called 'Computational Psychometrics'. (DIPF/Orig.)
DIPF-Abteilung: Bildungsqualität und Evaluation; Informationszentrum Bildung