Publikationen

Despite the ubiquity of learning in workplace and professional settings, the learning analytics (LA) community has paid significant attention to such settings only recently. This may be due to the focus on researching formal learning, as workplace learning is often informal, hard to grasp and not unequivocally defined. This paper summarizes the state of the art of Workplace Learning Analytics (WPLA), extracted from a two-iteration systematic literature review. Our in-depth analysis of 52 existing proposals not only provides a descriptive view of the field, but also reflects on researcher conceptions of learning and their influence on the design, analytics and technology choices made in this area. We also discuss the characteristics of workplace learning that make WPLA proposals different from LA in formal education contexts and the challenges resulting from this. We found that WPLA is gaining momentum, especially in some fields, like healthcare and education. The focus on theory is generally a positive feature in WPLA, but we encourage a stronger focus on assessing the impact of WPLA in realistic settings.

Künstliche Intelligenz (KI) ist zum Gegenstand gesellschaftlicher Debatten geworden. Die Beratung durch KI unterstützt uns in der Schule, im Alltag beim Einkauf, bei der Urlaubsplanung und beim Medienkonsum, manipuliert uns allerdings auch gezielt bei Entscheidungen oder führt durch Filter-Bubble-Phänomene zur Realitätsverzerrung.Eine der jüngsten Aufregungen hierzulande galt der Nutzung moderner Algorithmik durch das österreichische Arbeitsmarktservice AMS. Der sogenannte "AMS-Algorithmus" soll Beratende bei der Entscheidung über Fördermaßnahmen unterstützen.Wenn KI in einem so erheblichen Ausmaß in menschliches Handeln eingreift, bedarf sie im Hinblick auf ethische Prinzipien einer sorgfältigen Bewertung. Das ist notwendig, um unethische Folgen zu vermeiden. Üblicherweise wird gefordert, KI bzw. Algorithmen sollen fair sein, was bedeutet, sie sollen nicht diskriminieren und transparent sollen sie sein, also Einblick in ihre Funktionsweise ermöglichen

PurposeIntroducing technology at work presents a special challenge as learning is tightly integrated with workplace practices. Current design-based research (DBR) methods are focused on formal learning context and often questioned for a lack of yielding traceable research insights. This paper aims to propose a method that extends DBR by understanding tools as sociocultural artefacts, co-designing affordances and systematically studying their adoption in practice.Design/methodology/approachThe iterative practice-centred method allows the co-design of cognitive tools in DBR, makes assumptions and design decisions traceable and builds convergent evidence by consistently analysing how affordances are appropriated. This is demonstrated in the context of health-care professionals’ informal learning, and how they make sense of their experiences. The authors report an 18-month DBR case study of using various prototypes and testing the designs with practitioners through various data collection means.FindingsBy considering the cognitive level in the analysis of appropriation, the authors came to an understanding of how professionals cope with pressure in the health-care domain (domain insight); a prototype with concrete design decisions (design insight); and an understanding of how memory and sensemaking processes interact when cognitive tools are used to elaborate representations of informal learning needs (theory insight).Research limitations/implicationsThe method is validated in one long-term and in-depth case study. While this was necessary to gain an understanding of stakeholder concerns, build trust and apply methods over several iterations, it also potentially limits this.Originality/valueBesides generating traceable research insights, the proposed DBR method allows to design technology-enhanced learning support for working domains and practices. The method is applicable in other domains and in formal learning.

In this paper, we propose the Social Semantic Server (SSS) as a service-based infrastructure for workplace andprofessional Learning Analytics (LA). The design and development of the SSS has evolved over 8 years, startingwith an analysis of workplace learning inspired by knowledge creation theories and its application in differentcontexts. The SSS collects data from workplace learning tools, integrates it into a common data model based ona semantically-enriched Artifact-Actor Network and offers it back for LA applications to exploit the data. Further,the SSS design promotes its flexibility in order to be adapted to different workplace learning situations. Thispaper contributes by systematizing the derivation of requirements for the SSS according to the knowledge creationtheories, and the support offered across a number of different learning tools and LA applications integrated to it.It also shows evidence for the usefulness of the SSS extracted from four authentic workplace learning situationsinvolving 57 participants. The evaluation results indicate that the SSS satisfactorily supports decision making indiverse workplace learning situations and allow us to reflect on the importance of the knowledge creation theoriesfor such analysis.

Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next (i.e., maintain ideational fluency) and stay consistent in annotating resources (i.e., maintain organization). Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e.g., the reuse of tags for topically similar resources. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. An experimental field study (N = 18) in a workplace context finds evidence of the expected tradeoff and an advantage of SoMe over a conventional recommender in the collaborative setting. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.

There is increasing interest in Barcamps and Unconferences as an educational approach during traditional medical education conferences. Ourgroup has now accumulated extensive experience in these formats over a number of years in different educational venues. We present asummary of observations and lessons learned about what works and what doesn’t.

Background: Teamworking, within and acrosshealthcare organisations, is essential to deliverexcellent integrated care. Drawing upon an alternationof collaborative and cooperative phases, we exploredthis teamworking and respective technologicalsupport within UK Primary Care. Participants usedBits&Pieces (B&P), a sensemaking tool for tracedexperiences that allows sharing results and mutuallyelaborating them: i.e. cooperating and/orcollaborating.Summary of Work: We conducted a two month-longcase study involving six healthcare professionals. InB&P, they reviewed organizational processes, whichrequired the involvement of different professions ineither collaborative and/or cooperative manner. Weused system-usage data, interviews and qualitativeanalysis to understand the interplay of teamworkingpracticeand technology.Summary of Results: Within our analysis we mainlyidentified cooperation phases. In a f2f-meeting,professionals collaboratively identified subtasks andassigned individuals leading collaboration on them.However, these subtasks were undertaken asindividual sensemaking efforts and finally combined(i.e. cooperation). We found few examples ofreciprocal interpretation processes (i.e. collaboration):e.g. discussing problems during sensemaking ormonitoring other’s sensemaking-outcomes to makesuggestions.Discussion: These patterns suggest that collaborationin healthcare often helps to construct a minimalshared understanding (SU) of subtasks to engage incooperation, where individuals trust in other’scompetencies and autonomous completion. However,we also found that professionals with positivecollaboration history and deepened SU were willing toundertake subtasks collaboratively. It seems thatacquiring such deepened SU of concepts andmethods, leads to benefits that motivate professionalsto collaborate more.Conclusion: Healthcare is a challenging environmentrequiring interprofessional work across organisations.For effective teamwork, a deepened SU is crucial andboth cooperation and collaboration are required.However, we found a tendency of staff to rely mainlyon cooperation when working in teams and not fullyexplore benefits of collaboration.Take Home Messages: To maximise benefits ofinterprofessional working, tools for teamworkingshould support both cooperation and collaborationprocesses and scaffold the move between them

Between April 2015 and June 2016, members of the Open Access Network Aus- tria (OANA) working group “Open Access and Scholarly Communication” met in Vienna to discuss a fundamental reform of the scholarly communication system.By scholarly communication we mean the processes of producing, reviewing, organising, disseminating and preserving scholarly knowledge1. Scholarly communication does not only concern researchers, but also society at large, especially students, educators, policy makers, public administrators, funders, librarians, journalists, practitioners, publishers, public and private organisations, and interested citizens.

Social learning networks enable the sharing, transfer and enhancement of knowledge in the workplace that builds the ground to exchange informal learning practices. In this work, three healthcare networks are studied in order to understand how to enable the building, maintaining and activation of new contacts at work and the exchange of knowledge between them. By paying close attention to the needs of the practitioners, we aimed to understand how personal and social learning could be supported by technological services exploiting social networks and the respective traces reflected in the semantics. This paper presents a case study reporting on the results of two co-design sessions and elicits requirements showing the importance of scaffolding strategies in personal and shared learning networks. Besides, the significance of these strategies to aggregate trust among peers when sharing resources and decision-support when exchanging questions and answers. The outcome is a set of design criteria to be used for further technical development for a social tool. We conclude with the lessons learned and future work.

Today's complex scientific problems often require interdisciplinary, team-oriented approaches: the expertise of researchers from different disciplines is needed to collaboratively reach a solution. Interdisciplinary teams yet face many challenges such as differences in research practice, terminology, communication , and in the usage of tools. In this paper, we therefore study concrete mechanisms and tools of two real-world scientific projects with the aim to examine their efficacy and influence on interdisciplinary teamwork. For our study, we draw upon Bronstein's model of interdisciplinary collaboration. We found that it is key to use suitable environments for communication and collaboration, especially when teams are geographically distributed. Plus, the willingness to share (domain) knowledge is not a given and requires strong common goals and incentives. Besides, structural barriers such as financial aspects can hinder interdisciplinary work, especially in applied, industry funded research. Furthermore, we observed a kind of cold-start problem in interdisciplinary collaboration, when there is no work history and when the disciplines are rather different, e.g. in terms of wording. HowTo: Scientific Work in Interdisciplinary and Distributed Teams (PDF Download Available). Available from: https://www.researchgate.net/publication/282813815_HowTo_Scientific_Work_in_Interdisciplinary_and_Distributed_Teams [accessed Jul 13, 2017].