Valentina Nachtigall

The present study addresses a critical and underexplored area in learning research: how learners conceptualize scientists beyond the natural sciences. While previous studies have extensively examined learners' perceptions of natural scientists, little is known about their understanding of social and educational scientists – fields often overlooked as scientific domains and not associated with competence. Given the potential influence of such conceptions on learners’ academic engagement, study choices, and career aspirations, the present interview study provides an in-depth exploration of learners’ conceptions of the work and personal characteristics of educational scientists. The results of a qualitative content analysis show that the predominant image of the educational scientist among learners refers to a pedagogical practitioner who needs interpersonal skills to help children and families in educational and social service institutions. An additional typological content analysis reveals that learners who associate educational scientists with scientific activities and tasks tend to have uncertain, diffuse, and inaccurate ideas about the actual nature of this scientific work, putting into question whether this work can really be called research and imagining that educational scientists just sit together and think. Our findings also suggest that such misconceptions are rooted in learners’ school experiences. These findings point to a significant gap in the understanding of educational scientists and its potential consequences for engagement with the social sciences. This study emphasizes the need for targeted interventions to foster accurate conceptions about educational scientists, which ultimately could enhance learners’ appreciation of the social sciences and support more informed academic and career choices.

Out-of-school labs (OSLs) are considered a promising opportunity to provide students with authentic experiences by engaging them in authentic activities that emulate the work of professionals of a certain discipline. However, such authentic learning activities may cognitively overwhelm learners and are often not perceived as authentic by students. Thus, the hypothesized positive motivational and cognitive effects of authentic learning are often not evoked. Therefore, the present paper focuses on providing students with first authentic and not too cognitively demanding insights into mathematical modelling with experimental data. We conducted a quasi-experimental study with 111 9th and 10th graders who observed a video of either peer or scientist models performing a mathematical modelling task with experimental data followed by conducting a similar task themselves in an OSL. We investigated the effects on students’ perceived authenticity, situational interest, and knowledge acquisition. Thereby, we aimed to conceptually replicate a previous study that took place at the students’ school, in an OSL. As found in our school study, students in the OSL perceived the scientist models more authentically than the peer models. In contrast, in the OSL study, students who observed peer models reported higher situational interest than students who observed scientist models. Our results further indicate differential effects on students’ perceived authenticity during subsequent mathematical modelling with experimental data themselves. We discuss the results in light of previous research on learning in authentic contexts.

Background: With the increasing availability of immersive technologies such as 360° videos for educational purposes, research needs to shift from media comparison studies to value‐added studies in order to identify conditions for effective learning with such technologies. For the educational use of history‐related virtual reality media, which are characterized by immersion and emotionalization, instructional approaches that promote cognitive and critical rather than emotional processing of the content are required. Objectives: Drawing on research on self‐regulated learning, emotion regulation, and collaborative learning, the present value‐added study examines whether (1) strategy training in cognitive and emotion regulation strategies and (2) collaboration can enhance students' cognitive processing of history‐related 360° videos. Methods: In a quasi‐experimental study with school students ( N = 157), we compared the effects of training addressing cognitive and emotion regulation strategies with training focusing on cognitive strategies alone. Before and during each of the two types of training, students were asked to either collaboratively or individually analyse a history‐related 360° video. Results and Conclusions: Training in cognitive strategies alone promotes cognitive processing of 360° videos, while combined training leads to more emotional processing. Collaboration helps students to analyse a history‐related 360° video in a cognitive and reflective way before training and to acquire the cognitive strategies during the training. After training, however, students who had previously collaborated processed the video more emotionally than students who had previously worked alone. This study highlights the importance of tailored instructional approaches to maximize the educational potential of immersive technologies in history education.

A highly authentic learning setting is likely to trigger positive motivational and emotional reactions due to its emphasis on promoting the acquisition of knowledge that is connected and transferable to real-world phenomena outside the learning environment. However, a high level of authenticity is usually accompanied by a high level of complexity due to the complexity inherent in the real world. This complexity can be overwhelming for learners and can hamper or even prevent cognitive learning outcomes. Consequently, to help learners cope with this complexity, they need some kind of instructional support. By building a high level of support into the learning setting in order to promote cognitive learning outcomes, the level of authenticity and thereby the effects of authenticity on motivational outcomes may, however, in turn be reduced. In the present conceptual paper, we refer to this tension between authenticity and complexity, on the one hand, and instructional support, on the other hand, as the “authenticity dilemma”. Based on existing empirical evidence from previous studies, we (1) outline this dilemma, (2) discuss ways to reconceptualize it, and (3) derive implications regarding the conditions and effects of authentic learning. Finally, we discuss the findings of the studies included in the special issue “Perspectives on Authentic Learning” through the lens of the authenticity dilemma.

Zusammenfassung: International entwickelte sich unter dem Begriff „Learning Analytics“ in den letzten Jahren ein Forschungsfeld, das sich auf das Sammeln, Auswerten und Anwenden komplexer, häufig multi-modaler und digitaler Verhaltensspuren fokussiert. Diese Verhaltensspuren, die Lernende und Lehrende in digitalen Kontexten hinterlassen, werden mit Hilfe computerbasierter Modelle oder mittels Verfahren des maschinellen Lernens ausgewertet, um Erkenntnisse über Lehr- und Lernprozesse zu gewinnen. Die Lehr-Lernforschung nähert sich derzeit langsam dem Feld der Learning Analytics an. Ein Potenzial von Learning Analytics für die Lehr-Lernforschung wurde demnach bereits erkannt, sodass ein Weiterdenken an dieser Stelle äußerst lohnenswert erscheint. Der vorliegende Beitrag skizziert daher die inzwischen bereits stark fortgeschrittene Forschung zu Learning Analytics und stellt anhand konkreter Beispiele das Potenzial von Learning-Analytics-Ansätzen für die Weiterentwicklung von Lern- und Instruktionstheorien dar. Insbesondere wird hier auf selbstreguliertes und kooperatives Lernen eingegangen sowie auf die Gestaltung von Lernumgebung und Unterstützung von Lehrkräften. Dabei mit- und weitergedacht werden sowohl Risiken und Herausforderungen von Learning Analytics (wie fehlende Kopplung zwischen Theorie und Empirie sowie ethische Aspekte) als auch Chancen (wie Erfassung der Komplexität und Zeitlichkeit von Lehr-Lernprozessen) von Learning Analytics für die Unterrichtswissenschaft und -praxis.