Digitalization of Multistep Chemistry Exercises with Automated Formative Feedback

Autor/inn/en	Eitemüller, Carolin; Trauten, Florian; Striewe, Michael; Walpuski, Maik
Titel	Digitalization of Multistep Chemistry Exercises with Automated Formative Feedback
Quelle	In: Journal of Science Education and Technology, 32 (2023) 3, S.453-467 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Eitemüller, Carolin) ORCID (Trauten, Florian) ORCID (Striewe, Michael) ORCID (Walpuski, Maik)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1059-0145
DOI	10.1007/s10956-023-10043-2
Schlagwörter	Chemistry; Science Instruction; Teaching Methods; Error Patterns; Feedback (Response); Formative Evaluation; Learning Processes; Learning Management Systems; Intelligent Tutoring Systems; Science Achievement; Task Analysis; Undergraduate Students + Suchen Sie Ihr Suchwort? Chemie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Teaching method; Lehrmethode; Unterrichtsmethode; Fehlertyp; Learning process; Lernprozess; Intelligentes Tutorsystem; Aufgabenanalyse
Abstract	For various reasons, students receive less formative feedback at post-secondary institutions compared to secondary school. Considering feedback as one of the most important influencing factors on learning processes, formative feedback is a promising approach to improving students' performances. In this context, new technologies, such as learning management systems (LMS) or intelligent tutoring systems (ITS), can make a valuable contribution to improving higher education teaching by providing automated and individualized error-specific just-in-time (JIT) feedback. However, the digitalization especially of paper-based open-ended tasks that can be used by LMS is currently still associated with a loss of quality. In this paper, we present an approach that allows us to transfer open-ended paper-based tasks in the field of chemistry into online tasks without losing quality and provide large university courses with automated and individualized error-specific JIT feedback. Results of a study of 238 first-year chemistry students reveal that the automated individualized error-specific JIT feedback had a significant positive influence on students' performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01