Finding ways to support learners in becoming effective collaborators is a key challenge in higher education. Educational technologies can help to achieve this goal. However, the effectiveness of pedagogical design principles underlying these technologies needs to be tested empirically to inform evidence-based teaching in higher education. In the present study, we examine the effect of a technologically-supported collaborative reflection activity on learners’ knowledge gain about effective collaboration and about the quality of their interaction. To this end, we compare the results of a field study that was conducted in a course for civil engineering students (n = 66), with results of a laboratory study with n = 57 university students. Both field and laboratory study consisted of two collaborative problem-solving phases, in which students worked in small groups to solve information pooling problems. Multilevel modeling indicates that the technology-supported collaborative reflection activity between the two collaborative problem-solving phases increased explicit knowledge about effective collaboration. The quality of collaboration during subsequent collaboration, however, was not affected. Further, we found that groups’ self-assessments were in line with expert ratings of their collaboration quality. We discuss these findings in terms of the learning mechanisms behind technology-supported collaborative reflection and the extent to which these forms of support foster collaboration skills. Thus, our study adds insights on how to join educational technologies with pedagogical design principles to support collaborative learning.

A key challenge in CSCL research is to find ways to support learners in becoming effective collaborators. While the effectiveness of external collaboration scripts is well established, there is a need for research into support that acknowledges learners’ autonomy during collaboration. In the present study, we compare an external collaboration script and a reflection scaffold to a control condition and examine their effects on learners’ knowledge about effective collaboration and on their groups’ interaction quality. In an experimental study that employed a 1× three-factorial design, 150 university students collaborated in groups of three to solve two information pooling problems. These groups either received an external collaboration script during collaboration, no support during collaboration but a reflection scaffold before beginning to collaborate on the second problem, or no support for their collaboration. Multilevel modeling suggests that learners in the reflection condition gained more knowledge about effective collaboration than learners who collaborated guided by an external collaboration script or learners who did not receive any support. However, we found no effect of the script or the reflection scaffold on the quality of interaction in the subsequent collaboration. Explorative analyses suggest that learners acquired knowledge particularly about those interactions that are required for solving information pooling tasks (e.g., sharing information). We discuss our findings by contrasting the design of the external collaboration script and the reflection scaffold to identify potential mechanisms behind scripting and collaborative reflection and to what extent these forms of support foster collaboration skills and engagement in productive interaction.

During the COVID-19 pandemic, emergency online learning impeded the pursuit of in-person activities that usually foster successful socialization in higher education. To investigate the effects of online learning on socialization, we asked two exploratory research questions: (1) How and to what extent does the level of socialization change during the first online semester? and (2) To what extent does level of change predict course dropout and academic performance? In our case study, using a sample of new students at a large German university, we ran an autoregressive three-factorial model of socialization (role, relationships, organization) with three measurements taken during the new students’ first semester, which was the second semester in which emergency online learning took place. Our results show that the relationships component of socialization did not increase over the semester, while the role and organization components increased. Furthermore, our results support a negative effect of the organization component of socialization on course dropout and a positive effect of the relationship component of socialization on academic performance.

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.

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.

The role of collaboration self-efficacy for interdisciplinary computer-supported collaboration has not received much attention so far, although it may be a relevant factor.In a laboratory study that simulated interdisciplinary collaborative problem-solving with N = 72 university students, we investigated what experiences shape the development of collaboration self-efficacy and what effect a collaboration script has.The results show that experiences of technical coordination were negatively associated with collaboration self-efficacy and that a collaboration script was not particularly beneficial for fostering collaboration self-efficacy.

Background: When viewed internationally, Germany boasts a high rate of doctoral candidates. Fields such as medicine and life sciences have a notably high proportion of doctoral students, a trend rooted in historical factors. Despite this, comprehensive empirical studies concerning the doctoral phase and early-career researchers, especially in relation to the rise of structured doctoral programmes, have only recently gained traction.Methods: We present findings from a project investigating young scientists in medicine and life sciences. Postdoctoral graduates from these disciplines were examined both quantitatively and qualitatively within the E-Prom projects, emphasizing the primary domain of research.Results: Our analysis indicates some benefits of structured doctoral programmes over traditional individual doctorates. However, the disparities between these doctoral approaches are less pronounced than anticipated. We also identified discrepancies between the programme descriptions and their actual execution. Integration into the scientific community and research-related self-efficacy are potential indicators of publication output and inclination towards a scientific career. Physicians exhibited lower research-related self-efficacy and a lesser tendency towards a scientific career than biologists. Notably, we found gender disparities disadvantaging female graduates, with these disparities being more marked in medicine.Conclusions: There is evidence to suggest that official representations of structured doctoral programmes do not always align with their practical applications, limiting their potential effectiveness. Therefore, resources should be allocated to ensure the consistent execution of these programmes. Given the empirical evidence supporting the benefits of community integration for junior researchers, efforts should be made to facilitate their networking. Additionally, our findings emphasize the necessity of providing enhanced support for young female scientists.

Exploring ways to support learners in becoming effective collaborators is a core challenge of CSCL research.Collaboration scripts have been shown to be quite supportive, but they are also directive and can cause reactance in learners.Research on more implicit forms of support, such as promoting reflection, is still scarce.In the present study we compare the effectiveness of a collaboration script and a collaborative reflection phase utilizing a group awareness tool, on learners' explicit knowledge about beneficial interaction and the quality of their collaboration.In a laboratory experiment, 150 higher education students collaborated in groups of three on solving two information-pooling problems.The results suggest that collaboratively reflecting about the previous collaboration fosters explicit knowledge more than collaborating with a collaboration script.However, the conditions did not differ regarding collaboration quality.We discuss these findings against the learning mechanisms behind scripting and collaborative reflection.

The lack of support in academia, especially during doctoral studies, is a widely debated issue. Such experiences can be expected to be highly relevant to young researchers for their developing identity as scholars and their career aspirations. According to self-determination theory, support for the three basic psychological needs for competence, autonomy, and social relatedness in the doctoral context should foster the development of scholarly identity, which in turn is linked to career aspirations. In this longitudinal study, we investigated how doctoral graduates (N = 180) perceived such support during their doctoral studies and how these perceptions were related to their scholarly identity and career aspirations one year after finishing their doctorate. Our findings showed that only social relatedness to the scientific community during the doctorate was positively related to graduates' aspiration to stay in academia later on. Scholarly identity mediates this effect. We found no effects of competence and autonomy support. We discuss theoretical implications and the need for further research on the relations between the three basic needs. We discuss the applicability of these findings beyond the national context.

Apprenticeship learning is a central macro-approach to how learning and teaching can take place, standing in contrast to schooling. In the apprenticeship learning approach, learning takes place in the real-life context in which the content to be learned is of immediate relevance. Learning is understood as developing expertise in practices relevant in the learner's current life, takes place in a fully contextualized way, and is situated in a meaningful social context of practitioners Apprenticeship learning is mostly applied to observable practices but can also be applied to cognitive skills. Bringing apprenticeship learning and schooling together to benefit from their unique strengths is a current trend in educational research and practice.

IntroductionWays to improve the quality of doctoral education are debated internationally. In Europe, the United States, and other countries, there have been policy initiatives to address these. One approach has been the implementation of so-called structured doctoral training programs (doctoral programs) including formal structures such as courses, supervision agreements, external examiners for grading the thesis. However, there is little known about how doctoral programs implement the debated structures. As a result, the question arises whether existing programs already address the challenges of doctoral education and implement policy demands. MethodsIn this study, we evaluated the structure of 82 life science doctoral programs in Germany in a document analysis and a survey of program experts. We focused on (1) interdisciplinary aspects and (2) the international orientation of these programs. We evaluated the (3) courses offered, (4) formal characteristics of supervision, and (5) examination regulations of the doctoral programs. ResultsThe results showed that the doctoral programs already address these five aspects to some extent. However, there is variability as a function of institution and details of policy demand realizations are very heterogeneous. Some doctoral programs provide opportunities for interdisciplinary cooperation, but only few promote international orientation. Offered courses cover some relevant academic skills, but courses on, e.g., teaching, open access and public outreach are still rare. Structured regulations on supervision, e.g., through regular meetings and supervision agreements, are also rarely implemented. Lastly, most supervisors remain strongly involved in examining doctoral theses. DiscussionWe conclude that there is still a crucial need for improvement of doctoral programs through more extensive implementation of policy demands. We detail cross-national and -disciplinary practical implications for coordinators of doctoral programs.

Emotions are a crucial factor in daily research of academic staff and, accordingly, affect scientific progress. Already before but especially during the COVID-19 pandemic, the strong connection between working conditions and work-related emotional states as antecedents for mental health of academic staff gained more and more attention. However, in depths investigations of researchers’ emotions in academia are still rare. In the highly competitive field of academia, experiencing the working environments as supportive may be an important influential factor for researchers’ emotions. On a structural level, academic positions may also be tied to different emotional experiences. Taking a Self-Determination Theory approach, we therefore investigate, whether a basic need-supportive environment (regarding perceived competence and autonomy support, and social relatedness to the scientific community) and the academic position (research assistants without leading responsibility and principle investigators with leading responsibility) predict activity-related achievement emotions (enjoyment, anger, frustration, and boredom) during daily research activities. However, measurements on basic needs support and achievement emotions tailored to the specific academic research context are lacking. Therefore, this study is aimed at developing fitted scales on these constructs. In a cross-sectional survey, we questioned N = 250 life scientists in 13 German universities. Results of multiple linear regression analyses suggest that supportive environments in academia were positively associated to the level of experienced enjoyment and negatively to the level of experienced frustration. Surprisingly, social relatedness to the scientific community does not affect frustration. Principle investigators report a more favorable emotional pattern with higher levels of enjoyment than research assistants. However, the level of experienced frustration was not affected by the academic position. The scales on anger and boredom seemed not to differentiate emotional experiences on these two negative achievement emotions in the research context accurately. Therefore, we needed to exclude anger and boredom from analyzes. Further research on these achievement emotions is needed. We discuss our findings on enjoyment and frustration and derive both theoretical and practical implications, taking an international and interdisciplinary perspective.

This paper explores how first-year students experienced emergency online teaching during COVID-19 and aims at understanding individual experiences related to basic psychological need satisfaction, considering different levels of contextual facilitators for learning activities involving technology in higher education derived from the C-flat model. Employing a case study approach, interviews of 15 chemistry students were qualitatively analyzed. The results show negative effects of lacking internet connectivity and concurrence of learning and home spaces but positive effects of ceased commute between home and campus. Teachers' implementation of digital learning opportunities was perceived as adequate but did not sufficiently address the overwhelming increase in students' autonomy and decrease in social relatedness. Students' self-regulation skills as well as skills to initiate and maintain social contacts for interactive learning activities and for motivational support emerged as crucial aspects. Many students were not able to cope appropriately and students' need satisfaction during emergency online teaching appeared to be related to students' prior need satisfaction resulting in five groups of students, with two being relatively resilient and three being vulnerable to the disruptions of regular onsite teaching. Implications for further research and practice are discussed.

Structured doctoral education is increasingly preferred compared to the individual model. Several science policy organisations give recommendations on how to structure doctoral education. However, there is little research on to what extent these recommendations find their way into practice. In our study, we first compared European and German recommendations on doctoral education with, second, the institutional regulations of structured doctoral programmes (N= 98) in the life sciences at twelve different German universities. Additionally, we third asked doctoral graduates (N= 1796) of these structured doctoral programmes and graduates of individual doctoral studies about their experience in doctoral education. Fourth, we contrasted the regulations of structured doctoral programmes with the reported experiences of their graduates. We found significant deviations of the reported practices of graduates from the regulations of their organisations, regarding the student admission, supervision and curricular activities of doctoral candidates. The efficacy of structured versus traditional doctoral education should be examined based on reported practice rather than on the respective written regulations.

Educational technologies play an essential role in supporting the seamless integration between formal and informal, physical and virtual learning spaces (Leander, Phillips, & Taylor, 2010). These characteristics of the networked society demand refined instructional approaches to make the best use of the emerging opportunities that bring together meaningful sociocultural practices and canonical disciplinary knowledge while taking advantage of differences between learners (Collins & Halverson, 2010). These ideas are at the heart of the learning communities approach (Hod, Bielaczyc, & Ben-Zvi, 2018). Centrally concerned with community-driven learning experiences in complex spaces fostered by the use of educational technology, this special section focuses on Future Learning Spaces for Learning Communities. It aims to enhance conceptual frameworks, supported by empirical study, that can guide educational researchers and practitioners on how to design technology-supported learning environments for productive student engagement (Kali, Baram-Tsabari, & Schejter, 2019). Previous research in this area is fragmented and dispersed across different disciplines such as human–computer interaction, architecture, environmental psychology and computer-supported collaborative learning (Ellis & Goodyear, 2016). With an eye on bringing multiple perspectives under one roof, this special section draws on scholarship from the interdisciplinary field of the learning sciences (Sawyer, 2014). These efforts are motivated by the redesign or construction of learning spaces, which is a major, worldwide trend in contemporary education. Currently, financial resources equivalent to billions of dollars are being spent on educational infrastructure in higher education alone (Ellis & Goodyear, 2016; Johnson, Adams Becker, Cummins, Estrada, Freeman, & Hall, 2016). With surges such as makerspaces, virtual reality and other smart, connected communities of learning, the idea of future learning spaces adds a largely unexplored layer of research on educational technology. Future learning spaces are an emerging, and still loosely defined concept that has gained popularity in recent years as a new line of research in education. It responds to contemporary challenges by taking into account societal changes brought out in the age of innovation (Hod et al., 2019; Scardamalia & Bereiter, 2014; Sutherland & Fischer, 2014), what is known about human learning (National Academies of Sciences, Engineering, & Medicine, 2018), and rapidly advancing networked, digital technologies that are altering the space–time relationship of learning (Eberle, Lund, Tchounikine, & Fischer, 2015; Leander et al., 2010). At the same time, learning communities have been one of the most significant re-conceptualizations of schooling in the past several decades (Hod et al., 2018). Learning communities support the growth of its members in a culture of learning where every learner's contribution is legitimized and plays an important role in advancing the collective knowledge of the community (Bielaczyc & Collins, 1999). Because learning communities necessitate a variety of participation structures and activities, they benefit strongly from the use of educational technologies. Future learning spaces take these affordances into account, allowing for dynamic and flexible implementation of any one or more physical and virtual activities required to support the emerging needs of a learning community. Given these trends and the enormous investments being made to implement future learning spaces and learning communities in educational settings, the combination of both approaches represents a large opportunity for practitioners and researchers interested in educational technologies to help rethink the way schooling and learning is organized in the 21st century (Hod, 2017). This special section brings together international perspectives from a variety of theoretical and methodological orientations. The six papers included provide empirical studies in a range of educational settings, populations and content areas, contributing in three different ways to our understanding about how applications of educational technology systems can be understood and developed. Specifically, this special section provides (1) novel conceptualizations and theoretical approaches to the way we think of future learning spaces for learning communities; (2) explanations as to how physical and digital spaces shape learning experiences and how learners modify these spaces during learning; and (3) insights into how to successfully bring scientific knowledge on future learning spaces for learning communities into real-world educational settings. Damşa, Nerland, and Andreadakis (2019) take a bird's eye view on the topic, sharpening the conceptualization of future learning spaces. Proposing an ecological perspective on learning and technology, the authors propose five defining principles of learning spaces and illustrate them empirically. This ecological perspective cuts across all papers of this special section in that they are neither space-centered nor techno-centered, but consider educational technologies and spaces as part of the ecology to foster robust learning communities. The next four papers provide empirical findings on the question of how specific elements of future learning spaces can shape interactions and participation in computer-supported learning communities and how learners shape learning spaces in reverse. Two of these papers examine how designed aspects of technology-enhanced physical spaces affect social dynamics and attention. Specifically, Yeoman and Wilson (2019) investigated intended and unintended effects of different setups of technology-enhanced spaces on emergent learning activities. The authors emphasize that the physical environment needs to enable and support physical activities, related to human sense-making and social interaction, as this is their strength in comparison to virtual spaces. Along similar lines, Sopher, Fisher, Gewirtzman, and Kalay (2019) aggregated the multiple design decisions of students in a dual-space architectural learning community, creating development graphs about students' knowledge construction activities. Their careful focus on a cutting-edge, immersive space in this context sheds light on the different types of learning trajectories that high-tech spaces can afford. The following two contributions center on virtual spaces and how they can mediate collective knowledge building processes. Hod, Yaari, and Eberle (2019) assume that technologically created spaces are part of community life that must be nurtured and cared for. Their paper elucidates the interconnectedness between responsibility-taking over learning spaces and knowledge building processes by showing how they are entangled in one another. Yuan and Zhang (2019) examined how a particular educational technology, the Idea Thread Mapper, can be used to support cross-space interactions by connecting multiple knowledge building communities working across different classrooms. Finally, Kali, Sagy, Benichou, Atias, and Levin-Peled (2019) propose a way to help educational practitioners meaningfully integrate new technologies and pedagogies into their teaching practices. By proposing the TPeCS framework, this paper widens previous conceptions of teachers' knowing that consider pedagogies, content knowledge and educational technologies together. The additional dimension of space adds a new, relevant facet regarding what it takes to foster productive learning environments in the networked society. To sum, the six papers in this BJET special section present empirical findings and conceptual frameworks rooted in the design of future learning spaces for learning communities. The contribution of this international research effort can not only be a guide for academic scholarship, but can be a valuable resource for educational practitioners on how to conceptualize, design and best make use of this growing worldwide phenomenon.

The digital age has fostered the rapid dissemination of Future Learning Spaces within the educational sector. Empirical studies examining learning processes relevant for the digital age within FLSs are needed, as scholarship has heretofore been overly reliant on anecdotal evidence or has struggled to keep up with innovative pedagogies. In an effort to advance this goal, we examined a knowledge building community that took place in a future learning space. Using responsibility-taking as an underlying concept, our findings are twofold. First, we applied a grounded methodology to elucidate the way students take responsibility over the online space (The Knowledge Forum), a vital structure that supports collective knowledge building. This resulted in a spatial responsibility-taking framework that includes 16 action-tool combinations. The second finding resulted from applying this framework alongside a knowledge building analysis of a group of students. We present the results from our micro-analysis, shedding light on different ways that spatial infrastructures and knowledge building can co-mediate one another. Practitioner Notes What is already known about this topic Collective cognitive responsibility is a vital component of knowledge building communities. A range of infrastructures play an important role throughout the evolving stages involved in knowledge building. Multi-tiered approaches must be taken for future learning spaces to be used effectively. It is not the space itself, but the way that users take responsibility over the space that is a key determinant in the success of a future learning space. What this paper adds A novel framework for systematically capturing the way participants take responsibility over their online learning spaces. New insights into the ways that spaces and idea-advancements co-mediate one another throughout the inquiry process. Implications for practice and/or policy Teachers can use a toolkit to observe and identify desired practices related to responsibility taking in online spaces. Teachers can better discern when and how to intervene based on the way students use online spaces as they learn.