Eitemüller, Carolin; Trauten, Florian; Striewe, Michael; Walpuski, Maik:
Digitalization of Multistep Chemistry Exercises with Automated Formative Feedback
In: Journal of Science Education and Technology, Jg. 32 (2023), Heft 3, S. 453 - 467
2023Artikel/Aufsatz in ZeitschriftOA Hybrid
ChemieInformatikForschungszentren » paluno - The Ruhr Institute for Software TechnologyFakultät für Chemie » Didaktik der Chemie
Damit verbunden: 1 Publikation(en)
Titel in Englisch:
Digitalization of Multistep Chemistry Exercises with Automated Formative Feedback
Autor*in:
Eitemüller, CarolinUDE
LSF ID
53926
ORCID
0000-0001-7302-3607ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
korrespondierende*r Autor*in
;
Trauten, FlorianUDE
LSF ID
60008
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Striewe, MichaelUDE
GND
173940544
LSF ID
48192
ORCID
0000-0001-8866-6971ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
;
Walpuski, MaikUDE
GND
131968599
LSF ID
11384
ORCID
0000-0002-3761-1409ORCID iD
Sonstiges
der Hochschule zugeordnete*r Autor*in
Erscheinungsjahr:
2023
Open Access?:
OA Hybrid
Scopus ID
Sprache des Textes:
Englisch
Schlagwort, Thema:
Automated formative feedback ; Chemistry education ; E-assessment ; Higher education

Abstract in Englisch:

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.