Jan Luca Bahr

Feedback can play a pivotal role in learning but is only effective when students use it. However, the use of feedback varies greatly from student to student and these differences must be explained. Hence, the construct receptivity to instructional feedback (RIF) was introduced to assess students’ attitudes towards and engagement with human-provided feedback in multiple English-speaking countries. However, RIF scales have not yet been used in computer-based assessment contexts nor in German-speaking countries; their application is thus limited. This study examined an adapted and translated RIF scale using confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM). We capitalized on data sets comprising German-speaking university ( N = 464) and secondary school ( N = 1,207) students. The bifactor-CFA model showed a good model fit and revealed a general receptivity factor alongside the four specific factors: experiential attitudes, instrumental attitudes, cognitive engagement, and behavioral engagement with feedback. Scalar measurement invariance allowed for cross-gender and educational level comparisons. The relationships with broader personality characteristics were consistent with other contexts, thus indicating discriminant validity. General receptivity, cognitive engagement, and experiential attitudes positively correlated with GPA, suggesting convergent validity. Results support the stability of the RIF scale’s adaptation and translation. Assessment implications are discussed.

In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentationquality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according toa fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular featureslike argumentation coverage/reach and argumentative discourse units like claims and warrants. The features haveinter-annotator agreements up to 0.83 Krippendorff’s α. The corpus and dataset are publicly available for furtherresearch in argument mining.

AERA Online Paper Repository Viewer