Julia Eberle

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.

Im Bereich der beruflichen Weiterbildung sind interaktive Online-Kurse, die selbstgesteuertes digitales Lernen ermöglichen, seit Jahren erfolgreich in Verwendung. In den meisten Berufsschulen sind allerdings meist weder Lehrende noch Lernende mit der Verwendung interaktiver Kurse vertraut. In einer explorativen Studie (n = 21) wurden zwei Gruppen von Schülern einer Berufsschule in einem Technikkurs verglichen (ausschließlich männliche Teilnehmende). Die Kontrollgruppe erhielt direkten Unterricht mittels einer Präsentation. Die Experimentalgruppe führte selbstgesteuertes Lernen mit Hilfe eines multimedialen, interaktiven Web Based Trainings zum selben Thema durch. Die Ergebnisse zeigten keine signifikanten Unterschiede zwischen Experimental- und Kontrollgruppe hinsichtlich des Wissenserwerbs und der kognitiven Belastung. Die Experimentalgruppe wies jedoch eine deutlich positivere Einstellung zum digitalen Lernen auf.

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.

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.

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.

Collaborations are essential in research, especially in answering increasingly complex questions that require integrating knowledge from different disciplines and that engage multiple stakeholders. Fostering such collaboration between newcomers and established researchers helps keep scientific communities alive while opening the way to innovation. But this is a challenge for scientific communities, especially as little is known about the onset of such collaborations. Prior social network research suggests that face-to-face interaction at scientific events as well as both network-driven selection patterns (reciprocity and transitivity) and patterns of active selection of specific others (homophily / heterophily) may be important. Learning science research implies, moreover, that selecting appropriate collaboration partners may require group awareness. In a field study at two scientific events on technology-enhanced learning (Alpine Rendez-Vous 2011 and 2013) including N = 5736 relations between 287 researchers, we investigated how researchers selected future collaboration partners, looking specifically at the role of career level, disciplinary background, and selection patterns. Face-to-face contact was measured using RFID devices. Additionally, a group awareness intervention was experimentally varied. Data was analyzed using RSiena and meta-analyses. The results showed that transitivity, reciprocity and contact duration are relevant for the identification of new potential collaboration partners. PhD students were less often chosen as new potential collaboration partners, and researchers with a background in Information Technology selected fewer new potential collaboration partners. However, group awareness support balanced this disciplinary difference. Theoretical, methodological, and practical implications of these findings 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.