Testing is an effective learning method, and it is the basis of mobile quiz apps. Quiz apps have the potential to facilitate remote and self-regulated learning. In this context, automatized feedback plays a crucial role. In two experimental studies, we examined the effects of two feedback types of quiz apps on performance, namely, the standard corrective feedback of quiz apps and a feedback that incorporates additional information related to the correct response option. We realized a controlled lab setting (n= 68, (...) Study 1) and an unsupervised mobile setting (n= 150, Study 2). In the learning phase, participants used the quiz app and received feedback. They also completed a subsequent test as well as a follow-up test 1 week later by using the same quiz app. Irrespective of feedback type and setting, cognitive outcomes (quiz scores) and metacognitive outcomes (response certainty) increased similarly in the short term and long term. Feedback effects were not moderated by participants' overall response certainty during learning, their prior knowledge, and the difficulty of quiz items. Moreover, we found that participants perceived the quiz app to be similarly attractive, interesting, and enjoyable in both feedback conditions and that they spent slightly more time to process quiz items in the lab setting. We discuss these results in detail, including the role of moderating and mediating factors and prospects for further research and practice. Overall, our results underline that quiz apps are useful and effective tools that can support the acquisition and retention of semantic knowledge in different learning settings. (shrink)

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science research has played a key role in policymaking during this global crisis. This study addresses two key research questions: How FAIR ( findable, accessible, interoperable and reusable) are social science data (...) on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n = 1131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019 and 2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n = 45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research. (shrink)

In the last years, there has been an increasing interest in the Christian poet Prudentius and his literary technique. This article wants to show that Propertius’s elegy 4.11, the so-called regina elegiarum, is a crucial intertext for Prudentius, Peristephanon 13. Numerous lexical and motivic references to the Propertian text regarding the matrone Cornelia render it plausible that Prudentius wanted his recipients to read his poem about the Carthagian martyr bishop Cyprian while bearing in mind the Propertian intertext. Furthermore, by choosing (...) a rare metre, Prudentius might also allude to a specific Horatian poem (carm. 1.4) as another important intertext. (shrink)