Lindstaedt Stefanie , Pammer-Schindler Viktoria, Mörzinger Roland, Kern Roman, Mülner Helmut, Wagner Claudia
2008
Imagine you are member of an online social systemand want to upload a picture into the community pool. In currentsocial software systems, you can probably tag your photo, shareit or send it to a photo printing service and multiple other stuff.The system creates around you a space full of pictures, otherinteresting content (descriptions, comments) and full of users aswell. The one thing current systems do not do, is understandwhat your pictures are about.We present here a collection of functionalities that make a stepin that direction when put together to be consumed by a tagrecommendation system for pictures. We use the data richnessinherent in social online environments for recommending tags byanalysing different aspects of the same data (text, visual contentand user context). We also give an assessment of the quality ofthus recommended tags.
Ley Tobias, Ulbrich Armin, Lindstaedt Stefanie , Scheir Peter, Kump Barbara, Albert Dietrich
2008
Purpose – The purpose of this paper is to suggest a way to support work-integrated learning forknowledge work, which poses a great challenge for current research and practice.Design/methodology/approach – The authors first suggest a workplace learning context model, whichhas been derived by analyzing knowledge work and the knowledge sources used by knowledgeworkers. The authors then focus on the part of the context that specifies competencies by applying thecompetence performance approach, a formal framework developed in cognitive psychology. From theformal framework, a methodology is then derived of how to model competence and performance in theworkplace. The methodology is tested in a case study for the learning domain of requirementsengineering.Findings – The Workplace Learning Context Model specifies an integrative view on knowledge workers’work environment by connecting learning, work and knowledge spaces. The competence performanceapproach suggests that human competencies be formalized with a strong connection to workplaceperformance (i.e. the tasks performed by the knowledge worker). As a result, competency diagnosisand competency gap analysis can be embedded into the normal working tasks and learninginterventions can be offered accordingly. The results of the case study indicate that experts weregenerally in moderate to high agreement when assigning competencies to tasks.Research limitations/implications – The model needs to be evaluated with regard to the learningoutcomes in order to test whether the learning interventions offered benefit the user. Also, the validityand efficiency of competency diagnosis need to be compared to other standard practices incompetency management.Practical implications – Use of competence performance structures within organizational settings hasthe potential to more closely relate the diagnosis of competency needs to actual work tasks, and toembed it into work processes.Originality/value – The paper connects the latest research in cognitive psychology and in thebehavioural sciences with a formal approach that makes it appropriate for integration intotechnology-enhanced learning environments.Keywords Competences, Learning, Workplace learning, Knowledge managementPaper type Research paper
Granitzer Michael, Granitzer Gisela, Lindstaedt Stefanie , Rath Andreas S., Groiss W.
2008
It is a well known fact that a wealth of knowledge lies in thehead of employees making them one of the most or even the most valuableasset of organisations. But often this knowledge is not documented andorganised in knowledge systems as required by the organisation, butinformally shared. Of course this is against the organisation’s aim forkeeping knowledge reusable as well as easily and permanently availableindependent of individual knowledge workers.In this contribution we suggest a solution which captures the collectiveknowledge to the benefit of the organisation and the knowledge worker.By automatically identifying activity patterns and aggregating them totasks as well as by assigning resources to these tasks, our proposed solutionfulfils the organisation’s need for documentation and structuring ofknowledge work. On the other hand it fulfils the the knowledge worker’sneed for relevant, currently needed knowledge, by automatically miningthe entire corporate knowledge base and providing relevant, contextdependent information based on his/her current task.
Jones S., Lynch P., Maiden N., Lindstaedt Stefanie
2008
In this paper, we describe a creativity workshop thatwas used in a large research project, called APOSDLE,to generate creative ideas and requirements for a workintegratedlearning system. We present an analysis ofempirical data collected during and after the workshop.On the basis of this analysis, we conclude that the workshopwas an efficient way of generating ideas for futuresystem development. These ideas, on average, were usedat least as much as requirements from other sources inwriting use cases, and 18 months after the workshop wereseen to have a similar degree of influence on the projectto other requirements. We make some observations aboutthe use of more and less creative ideas, and about thetechniques used to generate them. We end with suggestionsfor further work.
Ulbrich Armin, Höfler Patrick, Lindstaedt Stefanie
2008
Ziel dieses Kapitels ist es, gemeinsame Verwendungsszenariendes Semantic Web und des Social Web zu identifizieren und zu benennen.Dabei wird ein Teilaspekt des Themengebiets im Detail betrachtet: die Nutzungvon Services, die Beobachtungen des Verhaltens von Anwendern analysieren, umdaraus maschinell interpretierbare Informationen zu erhalten und diese als Modellezu organisieren. Es werden zunächst einige Eigenschaften und Unterscheidungsmerkmalevon Anwenderverhalten und organisierten Modellen dargestellt.Anschließend wird der mögliche wechselseitige Nutzen von Anwenderverhaltenund Modellen diskutiert. Den Abschluss bildet eine Betrachtung einiger exemplarischerSoftware-Services, die heute schon verwendet werden, um Anwenderverhaltenin Modelle überzuführen.
Lindstaedt Stefanie , , , Lokaiczyk R., Kump Barbara, Beham Günter, Pammer-Schindler Viktoria
2008
In order to support work-integrated learning scenarios task- andcompetency-aware knowledge services are needed. In this paper we introducethree key knowledge services of the APOSDLE system and illustrate how theyinteract. The context determination daemon observes user interactions andinfers the current work task of the user. The user profile service uses theidentified work tasks to determine the competences of the user. And finally, theassociative retrieval service utilizes both the current work task and the inferredcompetences to identify relevant (learning) content. All of these knowledgeservices improve through user feedback.
Christl C., Ghidini C. , Guss J., Lindstaedt Stefanie , Pammer-Schindler Viktoria, Scheir Peter, Serafini L.
2008
Modern businesses operate in a rapidly changing environment.Continuous learning is an essential ingredient in order to stay competitivein such environments. The APOSDLE system utilizes semanticweb technologies to create a generic system for supporting knowledgeworkers in different domains to learnwork. Since APOSDLE relies onthree interconnected semantic models to achieve this goal, the questionon how to efficiently create high-quality semantic models has become oneof the major research challenges. On the basis of two concrete examplesnamelydeployment of such a learning system at EADS, a large corporation,and deployment at ISN, a network of SMEs-we report in detail theissues a company has to face, when it wants to deploy a modern learningenvironment relying on semantic web technology.
Zinnen A., Hambach S., Faatz A., Lindstaedt Stefanie , Beham Günter, Godehardt E., Goertz M., Lokaiczyk R.
2008
Rath Andreas S., Weber Nicolas, Kröll Mark, Granitzer Michael, Dietzel O., Lindstaedt Stefanie
2008
Improving the productivity of knowledge workers is anopen research challenge. Our approach is based onproviding a large variety of knowledge services which takethe current work task and information need (work context)of the knowledge worker into account. In the following wepresent the DYONIPOS application which strives toautomatically identify a user’s work task and thencontextualizes different types of knowledge servicesaccordingly. These knowledge services then provideinformation (documents, people, locations) both from theuser’s personal as well as from the organizationalenvironment. The utility and functionality is illustratedalong a real world application scenario at the Ministry ofFinance in Austria.
Lindstaedt Stefanie , Ley Tobias, Scheir Peter, Ulbrich Armin
2008
This contribution introduces the concept of work-integrated learning which distinguishes itself from traditional e-Learning in that it provides learning support (i) during work task execution and tightly contextualized to the work context,(ii) within the work environment, and (iii) utilizes knowledge artefacts available within the organizational memory for learning. We argue that in order to achieve this highly flexible learning support we need to turn to" scruffy" methods (such as associative retrieval, genetic algorithms, Bayesian and other probabilistic methods) which can provide good results in the presence of uncertainty and the absence of fine-granular models. Hybrid approaches to user context determination, user profile management, and learning material identification are discussed and first results are reported.
Granitzer Michael, Kröll Mark, Seifer Christin, Rath Andreas S., Weber Nicolas, Dietzel O., Lindstaedt Stefanie
2008
’Context is key’ conveys the importance of capturing thedigital environment of a knowledge worker. Knowing theuser’s context offers various possibilities for support, likefor example enhancing information delivery or providingwork guidance. Hence, user interactions have to be aggregatedand mapped to predefined task categories. Withoutmachine learning tools, such an assignment has to be donemanually. The identification of suitable machine learningalgorithms is necessary in order to ensure accurate andtimely classification of the user’s context without inducingadditional workload.This paper provides a methodology for recording user interactionsand an analysis of supervised classification models,feature types and feature selection for automatically detectingthe current task and context of a user. Our analysisis based on a real world data set and shows the applicabilityof machine learning techniques.
Aehnelt M., Ebert M., Beham Günter, Lindstaedt Stefanie , Paschen A.
2008
Knowledge work in companies is increasingly carried out by teams of knowledge workers. They interact within and between teams with the common goal to acquire, apply, create and share knowledge. In this paper we propose a socio-technical model to support intra-organizational collaboration which specifically takes into account the social and collaborative nature of knowledge work. Our aim is to support in particular the efficiency of collaborative knowledge work processes through an automated recommendation of collaboration partners and collaboration media. We report on the theoretical as well as practical aspects of such a socio-technical model.
Ley Tobias, Kump Barbara, Ulbrich Armin, Scheir Peter, Lindstaedt Stefanie
2008
The paper suggests a way to support work-integrated learning for knowledge workwhich poses a great challenge for current research and practice. We first present a WorkplaceLearning Context Model which has been derived by analyzing knowledge work and the knowledgesources used by knowledge workers. The model specifies an integrative view on knowledgeworkers’ work environment by connecting learning, work and knowledge spaces. We then focuson the part of the context which specifies learning goals and their interrelations to task and domainmodels. Our purpose is to support learning needs analysis which is based on a comparison of tasksperformed in the past to those tasks to be tackled in the future. A first implementation in theAPOSDLE project is presented including the models generated for five real world applications andthe software prototype. We close with an outlook on future work.