Stefanie Lindstaedt

Publications

Publications
Prof. Stefanie Lindstaedt

CEO Know-Center
Director Institute of Interactive Systems & Data Science (ISDS)
at Graz University of Technology (TU Graz)
Pammer-Schindler Viktoria, Lindstaedt Stefanie
2022
Digitale Kompetenzen sind im Bereich des strategischen Managements selbstverständlich, AI Literacy allerdings nicht. In diesem Artikel diskutieren wir, welches grundlegende Verständnis über künstliche Intelligenz (Artificial Intelligence – AI) für Entscheidungsträger:Innen im strategischen Management wichtig ist und welches darüber hinausgehende kontextspezifische und strategische Wissen.Digitale Kompetenzen für einen Großteil von beruflichen Tätigkeitsgruppen sind in aller Munde, zu Recht. Auf der Ebene von Entscheidungsträger:Innen im strategischen Management allerdings greifen diese zu kurz; sie sind größtenteils selbstverständlich im notwendigen Ausmaß: digitales Informationsmanagement, die Fähigkeit zur Kommunikation und Zusammenarbeit im Digitalen wie auch die Fähigkeiten, digitale Technologien zum Wissenserwerb und Lernen und zur Unterstützung bei kreativen Prozessen einzusetzen (Liste dieser typischen digitalen Kompetenzen aus [1]).Anders stellt sich die Sache dar, wenn es um spezialisiertes Wissen über moderne Computertechnologien geht, wie Methoden der automatischen Datenauswertung (Data Analytics) und der künstlichen Intelligenz, Internet of Things, Blockchainverfahren etc. (Auflistung in Anlehnung an Abb. 3 in [2]). Dieses Wissen wird in der Literatur durchaus als in Organisationen notwendiges Wissen behandelt [2]; allerdings üblicherweise mit dem Fokus darauf, dass dieses von Spezialist:Innen abgedeckt werden soll.Zusätzlich, und das ist die erste Hauptthese in diesem Kommentar, argumentieren wir, dass Entscheidungsträger:Innen im strategischen Management Grundlagenwissen in diesen technischen Bereichen brauchen, um in der Lage zu sein, diese Technologien in Bezug auf ihre Auswirkungen auf das eigene Unternehmen bzw. dessen Geschäftsumfeld einschätzen zu können. In diesem Artikel wird genauer das nötige Grundlagenwissen in Bezug auf künstliche Intelligenz (Artificial Intelligence – AI) betrachtet, das wir hier als „AI Literacy“ bezeichnen.

Link

Maitz Katharina, Fessl Angela, Pammer-Schindler Viktoria, Kaiser Rene_DB, Lindstaedt Stefanie
2022
Artificial intelligence (AI) is by now used in many different work settings, including construction industry. As new technologies change business and work processes, one important aspect is to understand how potentially affected workers perceive and understand the existing and upcoming AI in their work environment. In this work, we present the results of an exploratory case study with 20 construction workers in a small Austrian company about their knowledge of and attitudes toward AI. Our results show that construction workers’ understanding of AI as a concept is rather superficial, diffuse, and vague, often linked to physical and tangible entities such as robots, and often based on inappropriate sources of information which can lead to misconceptions about AI and AI anxiety. Learning opportunities for promoting (future) construction workers’ AI literacy should be accessible and understandable for learners at various educational levels and encompass aspects such as i) conveying the basics of digitalization, automation, and AI to enable a clear distinction of these concepts, ii) building on the learners’ actual experience realm, i.e., taking into account their focus on physical, tangible, and visible entities, and iii) reducing AI anxiety by elaborating on the limits of AI.

Link

Müllner Peter , Schmerda Stefan, Theiler Dieter, Lindstaedt Stefanie , Kowald Dominik
2022
Data and algorithm sharing is an imperative part of data- and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data providers, and algorithm providers. Although recommender systems are known to effectively interconnect users and items in e-commerce settings, there is a lack of research on the applicability of recommender systems for data and algorithm sharing. To fill this gap, we identify six recommendation scenarios for supporting data and algorithm sharing, where four of these scenarios substantially differ from the traditional recommendation scenarios in e-commerce applications. We evaluate these recommendation scenarios using a novel dataset based on interaction data of the OpenML data and algorithm sharing platform, which we also provide for the scientific community. Specifically, we investigate three types of recommendation approaches, namely popularity-, collaboration-, and content-based recommendations. We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios. Plus, the recommendation accuracy strongly depends on the specific scenario, e.g., algorithm recommendations for users are a more difficult problem than algorithm recommendations for datasets. Finally, the content-based approach generates the least popularity-biased recommendations that cover the most datasets and algorithms.

Link

Hoffer Johannes Georg, Ofner Andreas Benjamin, Rohrhofer Franz Martin, Lovric Mario, Kern Roman, Lindstaedt Stefanie , Geiger Bernhard
2022
Most engineering domains abound with models derived from first principles that have beenproven to be effective for decades. These models are not only a valuable source of knowledge, but they also form the basis of simulations. The recent trend of digitization has complemented these models with data in all forms and variants, such as process monitoring time series, measured material characteristics, and stored production parameters. Theory-inspired machine learning combines the available models and data, reaping the benefits of established knowledge and the capabilities of modern, data-driven approaches. Compared to purely physics- or purely data-driven models, the models resulting from theory-inspired machine learning are often more accurate and less complex, extrapolate better, or allow faster model training or inference. In this short survey, we introduce and discuss several prominent approaches to theory-inspired machine learning and show how they were applied in the fields of welding, joining, additive manufacturing, and metal forming.

Paper Link

Gashi Milot, Gursch Heimo, Hinterbichler Hannes, Pichler Stefan, Lindstaedt Stefanie , Thalmann Stefan
2022
Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of the industrial application, are required. However, collecting maintenance data is complex and challenging as it requires human involvement and expertise. Due to time constrains, motivating workers to provide comprehensive labeled data is very challenging, and thus maintenance data are mostly incomplete or even completely missing. In addition to these aspects, a lot of condition monitoring data-sets exist, but only very few labeled small maintenance data-sets can be found. Hence, our proposed solution can provide additional labels and offer new research possibilities for these data-sets. To address this challenge, we introduce MEDEP, a novel maintenance event detection framework based on the Pruned Exact Linear Time (PELT) approach, promising a low false-positive (FP) rate and high accuracy results in general. MEDEP could help to automatically detect performed maintenance events from the deviations in the condition monitoring data. A heuristic method is proposed as an extension to the PELT approach consisting of the following two steps: (1) mean threshold for multivariate time series and (2) distribution threshold analysis based on the complexity-invariant metric. We validate and compare MEDEP on the Microsoft Azure Predictive Maintenance data-set and data from a real-world use case in the welding industry. The experimental outcomes of the proposed approach resulted in a superior performance with an FP rate of around 10% on average and high sensitivity and accuracy results.

Paper Link

Lacic Emanuel, Fadljevic Leon, Weissenböck Franz, Lindstaedt Stefanie , Kowald Dominik
2022
Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation. Therefore, we conducted a two-weeks online study that started in October 2020, in which we analyzed the impact of recommendations on two user groups, i.e., anonymous and subscribed users, and three user interface types, i.e., on a desktop, mobile and tablet device. With respect to user interface types, we find that the probability of a recommendation to be seen is the highest for desktop devices, while the probability of interacting with recommendations is the highest for mobile devices. With respect to popularity bias mitigation, we find that personalized, content-based news recommendations can lead to a more balanced distribution of news articles' readership popularity in the case of anonymous users. Apart from that, we find that significant events (e.g., the COVID-19 lockdown announcement in Austria and the Vienna terror attack) influence the general consumption behavior of popular articles for both, anonymous and subscribed users

Paper Link

Lovric Mario, Meister Richard, Steck Thomas, Fadljevic Leon, Gerdenitsch Johann, Schuster Stefan, Schiefermüller Lukas, Lindstaedt Stefanie , Kern Roman
2020
In industrial electro galvanizing lines aged anodes deteriorate zinc coating distribution over the strip width, leading to an increase in electricity and zinc cost. We introduce a data-driven approach in predictive maintenance of anodes to replace the cost- and labor-intensive manual inspection, which is still common for this task. The approach is based on parasitic resistance as an indicator of anode condition which might be aged or mis-installed. The parasitic resistance is indirectly observable via the voltage difference between the measured and baseline (theoretical) voltage for healthy anode. Here we calculate the baseline voltage by means of two approaches: (1) a physical model based on electrical and electrochemical laws, and (2) advanced machine learning techniques including boosting and bagging regression. The data was collected on one exemplary rectifier unit equipped with two anodes being studied for a total period of two years. The dataset consists of one target variable (rectifier voltage) and nine predictive variables used in the models, observing electrical current, electrolyte, and steel strip characteristics. For predictive modelling, we used Random Forest, Partial Least Squares and AdaBoost Regression. The model training was conducted on intervals where the anodes were in good condition and validated on other segments which served as a proof of concept that bad anode conditions can be identified using the parasitic resistance predicted by our models. Our results show a RMSE of 0.24 V for baseline rectifier voltage with a mean ± standard deviation of 11.32 ± 2.53 V for the best model on the validation set. The best-performing model is a hybrid version of a Random Forest which incorporates meta-variables computed from the physical model. We found that a large predicted parasitic resistance coincides well with the results of the manual inspection. The results of this work will be implemented in online monitoring of anode conditions to reduce operational cost at a production site

Link

Obermeier, Melanie Maria, Wicaksono, Wisnu Adi, Taffner, Julian, Bergna, Alessandro, Poehlein, Anja, Cernava, Tomislav, Lindstaedt Stefanie , Lovric Mario, Müller Bogota, Christina Andrea, Berg, Gabriele
2020
The expanding antibiotic resistance crisis calls for a more in depth understanding of the importance of antimicrobial resistance genes (ARGs) in pristine environments. We, therefore, studied the microbiome associated with Sphagnum moss forming the main vegetation in undomesticated, evolutionary old bog ecosystems. In our complementary analysis of culture collections, metagenomic data and a fosmid library from different geographic sites in Europe, we identified a low abundant but highly diverse pool of resistance determinants, which targets an unexpectedly broad range of 29 antibiotics including natural and synthetic compounds. This derives both, from the extraordinarily high abundance of efflux pumps (up to 96%), and the unexpectedly versatile set of ARGs underlying all major resistance mechanisms. Multi-resistance was frequently observed among bacterial isolates, e.g. in Serratia, Rouxiella, Pandoraea, Paraburkholderia and Pseudomonas. In a search for novel ARGs, we identified the new class A β-lactamase Mm3. The native Sphagnum resistome comprising a highly diversified and partially novel set of ARGs contributes to the bog ecosystem´s plasticity. Our results reinforce the ecological link between natural and clinically relevant resistomes and thereby shed light onto this link from the aspect of pristine plants. Moreover, they underline that diverse resistomes are an intrinsic characteristic of plant-associated microbial communities, they naturally harbour many resistances including genes with potential clinical relevance

Link

Gayane Sedrakya, Dennerlein Sebastian, Pammer-Schindler Viktoria, Lindstaedt Stefanie
2020
Our earlier research attempts to close the gap between learning behavior analytics based dashboard feedback and learning theories by grounding the idea of dashboard feedback onto learning science concepts such as feedback, learning goals, (socio-/meta-) cognitive mechanisms underlying learning processes. This work extends the earlier research by proposing mechanisms for making those concepts and relationships measurable. The outcome is a complementary framework that allows identifying feedback needs and timing for their provision in a generic context that can be applied to a certain subject in a given LMS. The research serves as general guidelines for educators in designing educational dashboards, as well as a starting research platform in the direction of systematically matching learning sciences concepts with data and analytics concepts

Link

Dennerlein Sebastian, Tomberg Vladimir, Treasure-Jones, Tamsin, Theiler Dieter, Lindstaedt Stefanie , Ley Tobias
2020
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.

Link

Lindstaedt Stefanie , Geiger Bernhard, Pirker Gerhard
2019
Big Data and data-driven modeling are receiving more and more attention in various research disciplines, where they are often considered as universal remedies. Despite their remarkable records of success, in certain cases a purely data-driven approach has proven to be suboptimal or even insufficient.This extended abstract briefly defines the terms Big Data and data-driven modeling and characterizes scenarios in which a strong focus on data has proven to be promising. Furthermore, it explains what progress can be made by fusing concepts from data science and machine learning with current physics-based concepts to form hybrid models, and how these can be applied successfully in the field of engine pre-simulation and engine control.

Kowald Dominik, Traub Matthias, Theiler Dieter, Gursch Heimo, Lacic Emanuel, Lindstaedt Stefanie , Kern Roman, Lex Elisabeth
2019
Kowald Dominik, Lacic Emanuel, Theiler Dieter, Traub Matthias, Kuffer Lucky, Lindstaedt Stefanie , Lex Elisabeth
2019
Thalmann Stefan, Gursch Heimo, Suschnigg Josef, Gashi Milot, Ennsbrunner Helmut, Fuchs Anna Katharina, Schreck Tobias, Mutlu Belgin, Mangler Jürgen, Huemer Christian, Lindstaedt Stefanie
2019
Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising approach. With two high-tech companies (world market leader in their domains) from Austria, we are approaching this challenge and jointly develop cognitive decision support systems for three real world industrial use cases. Within this position paper, we introduce our understanding of cognitive decision support and we introduce three industrial use cases, focusing on the requirements for cognitive decision support. Finally, we describe our preliminary solution approach for each use case and our next steps.

Paper Link

Cicchinelli Analia, Veas Eduardo Enrique, Pardo Abelardo, Pammer-Schindler Viktoria, Fessl Angela, Barreiros Carla, Lindstaedt Stefanie
2018
This paper aims to identify self-regulation strategies from students' interactions with the learning management system (LMS). We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i) self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii) interactions with the LMS to find traces of self regulation in observable indicators, and iii) self regulation behaviours over the course duration. The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments.

Paper Link

Lindstaedt Stefanie , Czech Paul, Fessl Angela
2017
A Lifecycle Approach to Knowledge Excellence various industries and use cases. Through their cognitive computing-based approach, which combines the strength of man and the machine, they are setting standards within both the local and the international research community. With their expertise in the field of knowledge management they are describing the basic approaches in this chapter.

Ayris Paul, Berthou Jean-Yves, Bruce Rachel, Lindstaedt Stefanie , Monreale Anna, Mons Barend, Murayama Yasuhiro, Södegard Caj, Tochtermann Klaus, Wilkinson Ross
2016
The European Open Science Cloud (EOSC) aims to accelerate and support the current transition to more effective Open Science and Open Innovation in the Digital Single Market. It should enable trusted access to services, systems and the re-use of shared scientific data across disciplinary, social and geographical borders. This report approaches the EOSC as a federated environment for scientific data sharing and re-use, based on existing and emerging elements in the Member States, with light-weight international guidance and governance, and a large degree of freedom regarding practical implementation.

Lindstaedt Stefanie , Ley Tobias, Klamma Ralf, Wild Fridolin
2016
Recognizing the need for addressing the rather fragmented character of research in this field, we have held a workshop on learning analytics for workplace and professional learning at the Learning Analytics and Knowledge (LAK) Conference. The workshop has taken a broad perspective, encompassing approaches from a number of previous traditions, such as adaptive learning, professional online communities, workplace learning and performance analytics. Being co-located with the LAK conference has provided an ideal venue for addressing common challenges and for benefiting from the strong research on learning analytics in other sectors that LAK has established. Learning Analytics for Workplace and Professional Learning is now on the research agenda of several ongoing EU projects, and therefore a number of follow-up activities are planned for strengthening integration in this emerging field.

Kraker Peter, Schlögl Christian, Jack Kris, Lindstaedt Stefanie
2015
Given the enormous amount of scientific knowledge that is produced each and every day, the need for better ways of gaining–and keeping–an overview of research fields is becoming more and more apparent. In a recent paper published in the Journal of Informetrics [1], we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating such overviews. First, we investigated the distribution of subject areas in user libraries of educational technology researchers on Mendeley. The results show that around 69% of the publications in an average user library can be attributed to a single subject area. Then, we used co-readership patterns to map the field of educational technology. The resulting knowledge domain visualization, based on the most read publications in this field on Mendeley, reveals 13 topic areas of educational technology research. The visualization is a recent representation of the field: 80% of the publications included were published within ten years of data collection. The characteristics of the readers, however, introduce certain biases to the visualization. Knowledge domain visualizations based on readership statistics are therefore multifaceted and timely, but it is important that the characteristics of the underlying sample are made transparent.

Paper

Lindstaedt Stefanie , Ley Tobias, Sack Harald
2015

Dennerlein Sebastian, Theiler Dieter, Marton Peter, Lindstaedt Stefanie , Lex Elisabeth, Santos Patricia, Cook John
2015
We present KnowBrain (KB), an open source Dropbox-like knowledge repository with social features for informal workplace learning. KB enables users (i) to share and collaboratively structure knowledge, (ii) to access knowledge via sophisticated content- and metadatabased search and recommendation, and (iii) to discuss artefacts by means of multimedia-enriched Q&A. As such, KB can support, integrate and foster various collaborative learning processes related to daily work-tasks.

Paper Link

Kraker Peter, Schlögl C. , Jack K., Lindstaedt Stefanie
2015
Given the enormous amount of scientific knowledgethat is produced each and every day, the need for better waysof gaining – and keeping – an overview of research fields isbecoming more and more apparent. In a recent paper publishedin the Journal of Informetrics [1], we analyze the adequacy andapplicability of readership statistics recorded in social referencemanagement systems for creating such overviews. First, weinvestigated the distribution of subject areas in user librariesof educational technology researchers on Mendeley. The resultsshow that around 69% of the publications in an average userlibrary can be attributed to a single subject area. Then, we usedco-readership patterns to map the field of educational technology.The resulting knowledge domain visualization, based on the mostread publications in this field on Mendeley, reveals 13 topicareas of educational technology research. The visualization isa recent representation of the field: 80% of the publicationsincluded were published within ten years of data collection. Thecharacteristics of the readers, however, introduce certain biasesto the visualization. Knowledge domain visualizations based onreadership statistics are therefore multifaceted and timely, but itis important that the characteristics of the underlying sample aremade transparent.

Paper Link

Kraker Peter, Lindstaedt Stefanie , Schlögl C., Jack K.
2015
In this paper, we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating knowledge domain visualizations. First, we investigate the distribution of subject areas in user libraries of educational technology researchers on Mendeley. The results show that around 69% of the publications in an average user library can be attributed to a single subject area. Then, we use co-readership patterns to map the field of educational technology. The resulting visualization prototype, based on the most read publications in this field on Mendeley, reveals 13 topic areas of educational technology research. The visualization is a recent representation of the field: 80% of the publications included were published within ten years of data collection. The characteristics of the readers, however, introduce certain biases to the visualization. Knowledge domain visualizations based on readership statistics are therefore multifaceted and timely, but it is important that the characteristics of the underlying sample are made transparent.

Paper Link

Stegmaier Florian, Seifert Christin, Kern Roman, Höfler Patrick, Bayerl Sebastian, Granitzer Michael, Kosch Harald, Lindstaedt Stefanie , Mutlu Belgin, Sabol Vedran, Schlegel Kai
2014
Research depends to a large degree on the availability and quality of primary research data, i.e., data generated through experiments and evaluations. While the Web in general and Linked Data in particular provide a platform and the necessary technologies for sharing, managing and utilizing research data, an ecosystem supporting those tasks is still missing. The vision of the CODE project is the establishment of a sophisticated ecosystem for Linked Data. Here, the extraction of knowledge encapsulated in scientific research paper along with its public release as Linked Data serves as the major use case. Further, Visual Analytics approaches empower end users to analyse, integrate and organize data. During these tasks, specific Big Data issues are present.

Paper Link

Lindstaedt Stefanie , Reiter, T., Cik, M., Haberl, M., Breitwieser, C., Scherer, R., Kröll Mark, Horn Christopher, Müller-Putz, G., Fellendorf, M.
2013
Today, proper traffic incident management (IM) has to deal increasingly with problems such as traffic congestion and environmental sustainability. Therefore, IM intends to clear the road for traffic as quickly as possible after an incident has happened. Electronic data verifiably has great potential for supporting traffic incident management. As a consequence, this paper presents an innovative incident detection method using anonymized mobile communications data. The aim is to outline suitable methods for depicting the traffic situation of a designated test area. In order to be successful, the method needs to be able to calculate the traffic situation in-time and report anomalies back to the motorway operator. The resulting procedures are compared to data from real incidents and are thus validated. Special attention is turned to the question whether incidents can be detected quicker with the aid of mobile phone data than with conventional methods. Also, a focus is laid on the quicker deregistration of the incident, so that the traffic management can react superiorly.

Divitini Monica, Lindstaedt Stefanie , Pammer-Schindler Viktoria, Ley Tobias
2013
With this workshop, we intend to bring together the European communities of technology-enhanced learning, which typically meets at the ECTEL, and of computersupported cooperative work, which typically meets at the ECSCW. While the ECTEL community has traditionally focused on technology support for learning, be it in formal learning environments like schools, universities, etc. or in informal learning environments like workplaces, the ECSCW community has traditionally investigated how computers can and do mediate and influence collaborative work, in settings as diverse as the typical “gainful employment” situations, project work within university courses, volunteer settings in NGOs etc. Despite overlapping areas of concerns, the two communities are also exploiting different theories and methodological approaches. Within this workshop, we discuss issues that are relevant for both communities, and have the potential to contribute to a more lively communication between both communities.

Link

Trattner Christoph, Smadi Mohammad, Theiler Dieter, Dennerlein Sebastian, Kowald Dominik, Rella Matthias, Kraker Peter, Barreto da Rosa Isaías, Tomberg Vladimir, Kröll Mark, Treasure-Jones Tamsin, Kerr Micky, Lindstaedt Stefanie , Ley Tobias
2013
Höfler Patrick, Granitzer Michael, Sabol Vedran, Lindstaedt Stefanie
2013
Linked Data has become an essential part of the Semantic Web. A lot of Linked Data is already available in the Linked Open Data cloud, which keeps growing due to an influx of new data from research and open government activities. However, it is still quite difficult to access this wealth of semantically enriched data directly without having in-depth knowledge about SPARQL and related semantic technologies. In this paper, we present the Linked Data Query Wizard, a prototype that provides a Linked Data interface for non-expert users, focusing on keyword search as an entry point and a tabular interface providing simple functionality for filtering and exploration.

Link

Breitweiser Christian, Terbu Oliver, Holzinger Andreas, Brunner Clemens, Lindstaedt Stefanie , Müller-Putz Gernot
2013
We developed an iOS based application called iScope to monitor biosignals online. iScope is able to receive different signal types via a wireless network connection and is able to present them in the time or the frequency domain. Thus it is possible to inspect recorded data immediately during the recording process and detect potential artifacts early without the need to carry around heavy equipment like laptops or complete PC workstations. The iScope app has been tested during various measurements on the iPhone 3GS as well as on the iPad 1 and is fully functional.

Paper

Kraker Peter, Trattner Christoph, Jack Kris, Lindstaedt Stefanie , Schlgl Christian
2013
At the beginning of a scientific study, it is usually quite hardto get an overview of a research field. We aim to addressthis problem of classic literature search using web data. Inthis extended abstract, we present work-in-progress on aninteractive visualization of research fields based on readershipstatistics from the social reference management systemMendeley. To that end, we use library co-occurrences as ameasure of subject similarity. In a first evaluation, we findthat the visualization covers current research areas withineducational technology but presents a view that is biasedby the characteristics of readers. With our presentation, wehope to elicit feedback from the Websci’13 audience on (1)the usefulness of the prototype, and (2) how to overcomethe aforementioned biases using collaborative constructiontechniques.

Paper

Fellendorf Martin, Brandstätter Michael, Reiter Thomas, Lindstaedt Stefanie , Breitwieser Christian, Haberl Michael, Hebenstreit Cornelia, Scherer Reinhold, Kraschl-Hirschman Karin, Kröll Mark, Ruthner Thomas, Walther Bernhard
2012
Das mobile Verkehrsmanagementsystem MOVEMENTS soll als einfaches und zuverlässiges System entwickelt werden, das durch mobile Anzeigemöglichkeiten mit dezentralen Ansteuerungsmöglichkeiten und zentraler Überwachungsfunktion flächendeckend einsetzbar ist. Bei den Anzeigetafeln ist auf Lesbarkeit und Verständlichkeit von Texten und Piktogrammen zu achten, um für die Verkehrsteilnehmer auch unter schlechten Sichtbedingungen wahrnehmbar zu sein. Die mobile Anzeige soll sowohl für planbare Ereignisse (Veranstaltungen, Baustellen, ...), als auch für ungeplante Ereignisse längerer Dauer (Unfälle mit verkehrsbeeinträchtigender Wirkung, Straßensperren durch Naturereignisse, wie Hangrutschungen, ...) eingesetzt werden. Generell sollen durch den Einsatz von MOVEMENTS die Lenkungs- und Informationsmöglichkeiten der ASFINAG in Netzteilen ohne Verkehrsbeeinflussungsanlagen verbessert werden

Seitlinger Christian, Schöfegger Karin, Lindstaedt Stefanie , Ley Tobias
2012

Ravenscroft Andrew, Lindstaedt Stefanie , Delgado Kloos Carlos, Hernández-Leo Davinia
2012
This book constitutes the refereed proceedings of the 7th European Conference on Technology Enhanced Learning, EC-TEL 2012, held in Saarbrücken, Germany, in September 2012. The 26 revised full papers presented were carefully reviewed and selected from 130 submissions. The book also includes 12 short papers, 16 demonstration papers, 11 poster papers, and 1 invited paper. Specifically, the programme and organizing structure was formed through the themes: mobile learning and context; serious and educational games; collaborative learning; organisational and workplace learning; learning analytics and retrieval; personalised and adaptive learning; learning environments; academic learning and context; and, learning facilitation by semantic means.

Link

Drachsler Hendrik, Verbert Katrien, Manouselis Nikos, Vuorikari Riina, Wolpers Martin, Lindstaedt Stefanie
2012
Technology Enhanced Learning is undergoing a significant shift in paradigm towards more data driven systems that will make educational systems more transparent and predictable. Data science and data-driven tools will change the evaluation of educational practice and didactical interventions for individual learners and educational institutions. We summarise these developments and new challenges in the preface of this Special Issue under the keyword dataTEL that stands for ‘Data-Supported Technology-Enhanced Learning’.

Paper

Kump Barbara, Seifer Christin, Beham Günter, Lindstaedt Stefanie , Ley Tobias
2012
User knowledge levels in adaptive learning systems can be assessed based on user interactions that are interpreted as Knowledge Indicating Events (KIE). Such an approach makes complex inferences that may be hard to understand for users, and that are not necessarily accurate. We present MyExperiences, an open learner model designed for showing the users the inferences about them, as well as the underlying data. MyExperiences is one of the first open learner models based on tree maps. It constitutes an example of how research into open learner models and information visualization can be combined in an innovative way.

Paper

Pammer-Schindler Viktoria, Kump Barbara, Lindstaedt Stefanie
2012
Collaborative tagging platforms allow users to describe resources with freely chosen keywords, so called tags. The meaning of a tag as well as the precise relation between a tag and the tagged resource are left open for interpretation to the user. Although human users mostly have a fair chance at interpreting this relation, machines do not. In this paper we study the characteristics of the problem to identify descriptive tags, i.e. tags that relate to visible objects in a picture. We investigate the feasibility of using a tag-based algorithm, i.e. an algorithm that ignores actual picture content, to tackle the problem. Given the theoretical feasibility of a well-performing tag-based algorithm, which we show via an optimal algorithm, we describe the implementation and evaluation of a WordNet-based algorithm as proof-of-concept. These two investigations lead to the conclusion that even relatively simple and fast tag-based algorithms can yet predict human ratings of which objects a picture shows. Finally, we discuss the inherent difficulty both humans and machines have when deciding whether a tag is descriptive or not. Based on a qualitative analysis, we distinguish between definitional disagreement, difference in knowledge, disambiguation and difference in perception as reasons for disagreement between raters.

Paper Link

Lindstaedt Stefanie , Christl Conny
2011
This chapter presents a domain-independent computational environment which supports work-integrated learning at the professional workplace. The Advanced Process-Oriented Self-Directed Learning Environment (APOSDLE) provides learning support during the execution of work tasks (instead of beforehand), within the work environment of the user (instead of within a separate learning system), and repurposes content which was not originally intended for learning (instead of relying on the expensive manual creation of learning material). Since this definition of work-integrated learning might differ from other definitions employed within this book, a short summary of the theoretical background is provided. Along the example of the company Innovation Service Network (ISN), a network of SME’s, a rich and practical description of the deployment and usage of APOSDLE is given. The chapter provides the reader with firsthand experiences and discusses efforts and lessons learned, backed up with experiences gained in two other application settings, namely EADS in France and a Chamber of Commerce and industry in Germany.

Link

Lindstaedt Stefanie , Kraker Peter, Wild Fridolin, Ullmann Thomas, Duval Erik, Parra Gonzalo
2011
This deliverable reports on first usage experiences and evaluations of the STELLAR Science 2.0 Infrastructure. Usage experiences were available predominantly for the "mature" part of the infrastructure provided by standard Web 2.0 tools adapted to STELLAR needs. Evaluations are provided for newly developed tools. We first provide an overview of the whole STELLAR Science 2.0 Infrastructure and the relationships between the building blocks. While the individual building blocks already benefit researchers, the integration between them is the key for a positive usage experience. The publication meta data ecosystem for example provides researchers with an easy to retrieve set of TEL related data. Tools like the ScienceTable, Muse, the STELLAR latest publication widget, and the STELLAR BuRST search show already several scenarios of how to make use of this infrastructure. Especially a strong focus on anlytical tools based on publication and social media data seem useful. In order to highlight the relevance of the infrastructure to the individual capacitiy building activties within STELLAR, the usage experiences of individual building blocks are then reported with respect to Researcher Capacity (e.g. Deliverable Wikis, More! application), Doctoral Academy Capacity (e.g. DoCoP), Community Level Capacity (e.g TELeurope), and Leadership Capacity (e.g. Meeting of Minds, Podcast Series). Here we draw from 11 scientific papers published. The reader will find an overview of all these papers in the Appendix. Based on the usage experiences and evaluations we have identified a number of ideas which might be worth considering for future developments. For example, the experiences gained with the Deliverable Wikis show how the modification of the standard Wiki history can provide useful analytical insights into the collaboration of living deliverables and can return the focus on authorship (which is intentionally masked in Wikis, because of their strong notion on the product and not on authors). We conclude with main findings and an outlook on the development plan and evaluation plan which are currently being developed and which will influence D6.6. Particularly, we close with the notion of a Personal Research Environment (PRE) which draws from the concept of Personal Learning Environments (PLE).

Link

Lindstaedt Stefanie , Kump Barbara, Rath Andreas S.
2011
Within this chapter we first outline the important role learning plays within knowledge work and its impact on productivity. As a theoretical background we introduce the paradigm of Work-Integrated Learning (WIL) which conceptualizes informal learning at the workplace and takes place tightly intertwined with the execution of work tasks. Based on a variety of in-depth knowledge work studies we identify key requirements for the design of work-integrated learning support. Our focus is on providing learning support during the execution of work tasks (instead of beforehand), within the work environment of the user (instead of within a separate learning system), and by repurposing content for learning which was not originally intended for learning (instead of relying on the expensive manual creation of learning material). In order to satisfy these requirements we developed a number of context-aware knowledge services. These services integrate semantic technologies with statistical approaches which perform well in the face of uncertainty. These hybrid knowledge services include the automatic detection of a user’s work task, the ‘inference’ of the user’s competencies based on her past activities, context-aware recommendation of content and colleagues, learning opportunities, etc. A summary of a 3 month in-depth summative workplace evaluation at three testbed sites concludes the chapter.

Link

Erdmann Michael, Hansch Daniel, Pammer-Schindler Viktoria, Rospocher Marco, Ghidini Chiara, Lindstaedt Stefanie , Serafini Luciano
2011
This chapter describes some extensions to and applications of the Semantic MediaWiki. It complements the discussion of the SMW in Chap. 3. Semantic enterprise wikis combine the strengths of traditional content management systems, databases, semantic knowledge management systems and collaborative Web 2.0 platforms. Section 12.1 presents SMW+, a product for developing semantic enterprise applications. The section describes a number of real-world applications that are realized with SMW+. These include content management, project management and semantic data integration. Section 12.2 presents MoKi, a semantic wiki for modeling enterprise processes and application domains. Example applications of MoKi include modeling tasks and topics for work-integrated learning, collaboratively building an ontology and modeling clinical protocols. The chapter illustrates the wealth of activities which semantic wikis support.

Link

Kump Barbara, Knipfer Kristin, Pammer-Schindler Viktoria, Schmidt Andreas, Maier Ronald, Kunzmann Christine, Cress Ulrike, Lindstaedt Stefanie
2011
The Knowledge Maturing Phase Model has been presented as a model aligning knowledge management and organizational learning. The core argument underlying the present paper is that maturing organizational knowhow requires individual and collaborative reflection at work. We present an explorative interview study that analyzes reflection at the workplace in four organizations in different European countries. Our qualitative findings suggest that reflection is not equally self-evident in different settings. A deeper analysis of the findings leads to the hypothesis that different levels of maturity of processes come along with different expectations towards the workers with regard to compliance and flexibility, and to different ways of how learning at work takes place. Furthermore, reflection in situations where the processes are in early maturing phases seems to lead to consolidation of best practice, while reflection in situations where processes are highly standardized may lead to a modification of these standard processes. Therefore, in order to support the maturing of organizational know-how by providing reflection support, one should take into account the degree of standardisation of the processes in the target group.

Paper

Granitzer Michael, Lindstaedt Stefanie
2011
Moskaliuk, J., Rath, A.S., Devaurs, D., Weber, N., Lindstaedt Stefanie , Kimmerle, J., Cress, U.
2011
Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed.

Link

Kraker Peter, Wagner Claudia, Jeanquartier Fleur, Lindstaedt Stefanie
2011
This paper presents an adaptable system for detecting trends based on the micro-blogging service Twitter, and sets out to explore to what extent such a tool can support researchers. Twitter has high uptake in the scientific community, but there is a need for a means of extracting the most important topics from a Twitter stream. There are too many tweets to read them all, and there is no organized way of keeping up with the backlog. Following the cues of visual analytics, we use visualizations to show both the temporal evolution of topics, and the relations between different topics. The Twitter Trend Detection was evaluated in the domain of Technology Enhanced Learning (TEL). The evaluation results indicate that our prototype supports trend detection but reveals the need for refined preprocessing, and further zooming and filtering facilities.

Paper Link

Scheir Peter, Prettenhofer Peter, Lindstaedt Stefanie , Ghidini Chiara
2010
While it is agreed that semantic enrichment of resources would lead to better search results, at present the low coverage of resources on the web with semantic information presents a major hurdle in realizing the vision of search on the Semantic Web. To address this problem, this chapter investigates how to improve retrieval performance in settings where resources are sparsely annotated with semantic information. Techniques from soft computing are employed to find relevant material that was not originally annotated with the concepts used in a query. The authors present an associative retrieval model for the Semantic Web and evaluate if and to which extent the use of associative retrieval techniques increases retrieval performance. In addition, the authors present recent work on adapting the network structure based on relevance feedback by the user to further improve retrieval effectiveness. The evaluation of new retrieval paradigms - such as retrieval based on technology for the Semantic Web - presents an additional challenge since no off-the-shelf test corpora exist. Hence, this chapter gives a detailed description of the approach taken to evaluate the information retrieval service the authors have built.

Link

Balacheff, Nicolas, Bottino, Rosa, Fischer, Frank, Hofmann, Lena, Joubert, Marie, Kieslinger, Barbara, Lindstaedt Stefanie , Manca, Stefanie, Ney, Muriel, Pozzi, Francesca, Sutherland, Rosamund, Verbert, Katrien, Timmis, Sue, Wild, Fridolin, Scott, Peter, Specht, Marcus
2010
This First TEL Grand Challenge Vision and Strategy Report aims to: • provide a unifying framework for members of STELLAR (including doctoral candidates) to develop their own research agenda • engage the STELLAR community in scientific debate and discussion with the long term aim of developing awareness of and respect for different theoretical and methodological perspectives • build knowledge related to the STELLAR grand challenges through the construction of a wiki that is iteratively co‐edited throughout the life of the STELLAR network • develop understandings of the way in which web 2.0 technologies can be used to construct knowledge within a research community (science 2.0) • develop strategies for ways in which the STELLAR instruments can feed into the ongoing development of the wiki and how the they can be used to address the challenges highlighted in this report.

Link

Pozzi, Francesca, Persico, Donatella, Fischer, Frank, Hofmann, Lena, Lindstaedt Stefanie , Cress, Ulrike, Rath Andreas S., Moskaliuk, Johannes, Weber, Nicolas, Kimmerle, Joachim, Devaurs Didier, Ney, Muriel, Gonçalves, Celso, Balacheff, Nicolas, Schwartz, Claudine, Bosson, Jean-Luc, Dillenbourg, Pierre, Jermann, Patrick, Zufferey, Guillaume, Brown, Elisabeth, Sharples, Mike, Windrum, Caroline, Specht, Marcus, Börner, Dirk, Glahn, Christian, Fiedler, Sebastian, Fisichella, Marco, Herder, Eelco, Marenzi, Ivana, Nejdl, Wolfgang, Kawese, Ricardo, Papadakis, George
2010
In this first STELLAR trend report we survey the more distant future of TEL, as reflected in the roadmaps; we compare the visions with trends in TEL research and TEL practice. This generic overview is complemented by a number of small-scale studies, which focus on a specific technology, approach or pedagogical model.

Link

Wolpers Martin, Kirschner Paul A., Scheffel Maren, Lindstaedt Stefanie , Dimitrova Vania
2010
Lindstaedt Stefanie , Duval E., Ullmann T.D., Wild F., Scott P.
2010
Research2.0 is in essence a Web2.0 approach to how we do research. Research2.0 creates conversations between researchers, enables them to discuss their findings and connects them with others. Thus, Research2.0 can accelerate the diffusion of knowledge.ChallengesAs concluded during the workshop, at least four challenges are vital for future research.The first area is concerned with availability of data. Access to sanitized data and conventions on how to describe publication-related metadata provided from divergent sources are enablers for researchers to develop new views on their publications and their research area. Additional, social media data gain more and more attention. Reaching a widespread agreement about this for the field of technology-enhanced learning would be already a major step, but it is also important to focus on the next steps: what are success-critical added values driving uptake in the research community as a whole?The second area of challenges is seen in Research 2.0 practices. As technology-enhanced learning is a multidisciplinary field, practices developed in one area could be valuable for others. To extract the essence of successful multidisciplinary Research 2.0 practice though, multidimensional and longitudinal empirical work is needed. It is also an open question, if we should support practice by fostering the usage of existing tools or the development of new tools, which follow Research 2.0 principles. What makes a practice sustainable? What are the driving factors?The third challenge deals with impact. What are criteria of impact for research results (and other research artefacts) published on the Web? How can this be related to the publishing world appearing in print? Is a link equal to a citation or a download equal to a subscription? Can we develop a Research 2.0 specific position on impact measurement? This includes questions of authority, quality and re-evaluation of quality, and trust.The tension between openness and privacy spans the fourth challenge. The functionality of mash-ups often relies on the use of third-party services. What happens with the data, if this source is no longer available? What about hidden exchange of data among backend services?

Link

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier
2010
. Supporting learning activities during work has gained momentum fororganizations since work-integrated learning (WIL) has been shown to increaseproductivity of knowledge workers. WIL aims at fostering learning at the workplace,during work, for enhancing task performance. A key challenge for enablingtask-specific, contextualized, personalized learning and work support is to automaticallydetect the user’s task. In this paper we utilize our ontology-based usertask detection approach for studying the factors influencing task detection performance.We describe three laboratory experiments we have performed in twodomains including over 40 users and more than 500 recorded task executions.The insights gained from our evaluation are: (i) the J48 decision tree and Na¨ıveBayes classifiers perform best, (ii) six features can be isolated, which providegood classification accuracy, (iii) knowledge-intensive tasks can be classified aswell as routine tasks and (iv) a classifier trained by experts on standardized taskscan be used to classify users’ personal tasks.

Paper Link

Stern Hermann, Kaiser Rene_DB, Hofmair P., Lindstaedt Stefanie , Scheir Peter, Kraker Peter
2010
One of the success factors of Work Integrated Learning (WIL) is to provide theappropriate content to the users, both suitable for the topics they are currently working on, andtheir experience level in these topics. Our main contributions in this paper are (i) overcomingthe problem of sparse content annotation by using a network based recommendation approachcalled Associative Network, which exploits the user context as input; (ii) using snippets for notonly highlighting relevant parts of documents, but also serving as a basic concept enabling theWIL system to handle text-based and audiovisual content the same way; and (iii) using the WebTool for Ontology Evaluation (WTE) toolkit for finding the best default semantic similaritymeasure of the Associative Network for new domains. The approach presented is employed inthe software platform APOSDLE, which is designed to enable knowledge workers to learn atwork.

Paper

Lindstaedt Stefanie , Kump Barbara, Beham Günter, Pammer-Schindler Viktoria, Ley Tobias, de Hoog R., Dotan A.
2010
We present a work-integrated learning (WIL) concept which aims atempowering employees to learn while performing their work tasks. Withinthree usage scenarios we introduce the APOSDLE environment whichembodies the WIL concept and helps knowledge workers move fluidly alongthe whole spectrum of WIL activities. By doing so, they are experiencingvarying degrees of learning guidance: from building awareness, over exposingknowledge structures and contextualizing cooperation, to triggering reflectionand systematic competence development. Four key APOSDLE components areresponsible for providing this variety of learning guidance. The challenge intheir design lies in offering learning guidance without being domain-specificand without relying on manually created learning content. Our three monthsummative workplace evaluation within three application organizationssuggests that learners prefer awarenss building functionalities and descriptivelearning guidance and reveals that they benefited from it.

Paper Link

Lindstaedt Stefanie , Beham Günter, Stern Hermann, Drachsler H., Bogers T., Vuorikari R., Verbert K., Duval E., Manouselis N., Friedrich M., Wolpers M.
2010
This paper raises the issue of missing data sets for recommender systems in Technology Enhanced Learning that can be used asbenchmarks to compare different recommendation approaches. It discusses how suitable data sets could be created according tosome initial suggestions, and investigates a number of steps that may be followed in order to develop reference data sets that willbe adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing ofdata sets by taking into account legal protection rights. Finally, an initial elaboration of a representation and exchange format forsharable TEL data sets is carried out. The paper concludes with future research needs.

Paper Link

Beham Günter, Kump Barbara, Lindstaedt Stefanie , Ley Tobias
2010
According to studies into learning at work, interpersonal help seeking is the most important strategy of how people acquireknowledge at their workplaces. Finding knowledgeable persons, however, can often be difficult for several reasons. Expertfinding systems can support the process of identifying knowledgeable colleagues thus facilitating communication andcollaboration within an organization. In order to provide the expert finding functionality, an underlying user model is needed thatrepresents the characteristics of each individual user. In our article we discuss requirements for user models for the workintegratedlearning (WIL) situation. Then, we present the APOSDLE People Recommender Service which is based on anunderlying domain model, and on the APOSDLE User Model. We describe the APOSDLE People Recommender Service on thebasis of the Intuitive Domain Model of expert finding systems, and explain how this service can support interpersonal helpseeking at workplaces.

Paper Link

Lindstaedt Stefanie , Kraker Peter, Höfler Patrick, Fessl Angela
2010
In this paper we present an ecosystem for the lightweight exchangeof publication metadata based on the principles of Web 2.0. At the heart of thisecosystem, semantically enriched RSS feeds are used for dissemination. Thesefeeds are complemented by services for creation and aggregation, as well aswidgets for retrieval and visualization of publication metadata. In twoscenarios, we show how these publication feeds can benefit institutions,researchers, and the TEL community. We then present the formats, services,and widgets developed for the bootstrapping of the ecosystem. We concludewith an outline of the integration of publication feeds with the STELLARNetwork of Excellence1 and an outlook on future developments.

Paper Link

Beham Günter, Lindstaedt Stefanie , Ley Tobias, Kump Barbara, Seifert C.
2010
When inferring a user’s knowledge state from naturally occurringinteractions in adaptive learning systems, one has to makes complexassumptions that may be hard to understand for users. We suggestMyExperiences, an open learner model designed for these specificrequirements. MyExperiences is based on some of the key design principles ofinformation visualization to help users understand the complex information inthe learner model. It further allows users to edit their learner models in order toimprove the accuracy of the information represented there.

Link

Beham Günter, Jeanquartier Fleur, Lindstaedt Stefanie
2010
This paper introduces iAPOSDLE, a mobile application enabling the use of work-integrated learning services without being limited by location. iAPOSDLE makes use of the APOSDLE WIL system for self-directed work-integrated learning support, and extends its range of application to mobile learning. Core features of iAPOSDLE are described and possible extensions are discussed.

Paper Link

Ley Tobias, Lindstaedt Stefanie , Schöfegger Karin, Seitlinger Paul, Weber Nicolas, Hu Bo, Riss Uwe, Brun Roman, Hinkelmann Knut, Thönssen Barbara, Maier Ronald, Schmidt Andreas
2009
Schoefegger K., Weber Nicolas, Lindstaedt Stefanie , Ley Tobias
2009
The changes in the dynamics of the economy and thecorresponding mobility and fluctuations of knowledge workers within organizationsmake continuous social learning an essential factor for an organization.Within the underlying organizational processes, KnowledgeMaturing refers to the the corresponding evolutionary process in whichknowledge objects are transformed from informal and highly contextualizedartifacts into explicitly linked and formalized learning objects.In this work, we will introduce a definition of Knowledge (Maturing)Services and will present a collection of sample services that can be dividedinto service functionality classes supporting Knowledge Maturingin content networks. Furthermore, we developed an application of thesesample services, a demonstrator which supports quality assurance withina highly content based organisational context.

Paper Link

Beham Günter, Lindstaedt Stefanie , Kump Barbara, Resanovic D.
2009
Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.
2009
Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.

Paper Link

Pammer-Schindler Viktoria, Serafini L., Lindstaedt Stefanie
2009
Lindstaedt Stefanie , Aehnelt M., de Hoog R.
2009
Lindstaedt Stefanie , de Hoog R., Aehnelt M.
2009
This contribution shortly introduces the collaborative APOSDLE environmentfor integrated knowledge work and learning. It proposes a video presentation and thepresentation of the third APOSDLE prototype.

Link

Schmidt A., Hinkelmann K., Ley Tobias, Lindstaedt Stefanie , Maier R., Riss U.
2009
Effective learning support in organizations requires a flexible and personalizedtoolset that brings together the individual and the organizational perspectiveon learning. Such toolsets need a service-oriented infrastructure of reusable knowledgeand learning services as an enabler. This contribution focuses on conceptualfoundations for such an infrastructure as it is being developed within the MATUREIP and builds on the knowledge maturing process model on the one hand, and theseeding-evolutionary growth-reseeding model on the other hand. These theories areused to derive maturing services, for which initial examples are presented.

Paper Link

Weber Nicolas, Ley Tobias, Lindstaedt Stefanie , Schoefegger K., Bimrose J., Brown A., Barnes S.
2009
Lindstaedt Stefanie , Beham Günter, Ley Tobias, Kump Barbara
2009
Work-integrated learning (WIL) poses unique challenges for usermodel design: on the one hand users’ knowledge levels need to be determinedbased on their work activities – testing is not a viable option; on the other handusers do interact with a multitude of different work applications – there is nocentral learning system. This contribution introduces a user model and correspondingservices (based on SOA) geared to enable unobtrusive adaptabilitywithin WIL environments. Our hybrid user model services interpret usage datain the context of enterprise models (semantic approaches) and utilize heuristics(scruffy approaches) in order to determine knowledge levels, identify subjectmatter experts, etc. We give an overview of different types of user model services(logging, production, inference, control), provide a reference implementationwithin the APOSDLE project, and discuss early evaluation results.

Paper Link

Lindstaedt Stefanie , Rospocher M., Ghidini C., Pammer-Schindler Viktoria, Serafini L.
2009
Enterprise modelling focuses on the construction of a structureddescription of relevant aspects of an enterprise, the so-called enterprisemodel. Within this contribution we describe a wiki-based tool forenterprise modelling, called MoKi (Modelling wiKi). It specifically facilitatescollaboration between actors with different expertise to develop anenterprise model by using structural (formal) descriptions as well as moreinformal and semi-formal descriptions of knowledge. It also supports theintegrated development of interrelated models covering different aspectsof an enterprise.

Link

Lindstaedt Stefanie , Rath Andreas S., Devaurs Didier
2009
‘Understanding context is vital’ [1] and ‘context is key’ [2]signal the key interest in the context detection field. Oneimportant challenge in this area is automatically detectingthe user’s task because once it is known it is possible tosupport her better. In this paper we propose an ontologybaseduser interaction context model (UICO) that enhancesthe performance of task detection on the user’s computerdesktop. Starting from low-level contextual attention metadatacaptured from the user’s desktop, we utilize rule-based,information extraction and machine learning approaches toautomatically populate this user interaction context model.Furthermore we automatically derive relations between themodel’s entities and automatically detect the user’s task.We present evaluation results of a large-scale user study wecarried out in a knowledge-intensive business environment,which support our approach.

Paper Link

Lindstaedt Stefanie , Ghidini C., Kump Barbara, Mahbub N., Pammer-Schindler Viktoria, Rospocher M., Serafini L.
2009
Enterprise modelling focuses on the construction of a structureddescription, the so-called enterprise model, which represents aspectsrelevant to the activity of an enterprise. Although it has becomeclearer recently that enterprise modelling is a collaborative activity, involvinga large number of people, most of the enterprise modelling toolsstill only support very limited degrees of collaboration. Within thiscontribution we describe a tool for enterprise modelling, called MoKi(MOdelling wiKI), which supports agile collaboration between all differentactors involved in the enterprise modelling activities. MoKi is basedon a Semantic Wiki and enables actors with different expertise to developan enterprise model not only using structural (formal) descriptions butalso adopting more informal and semi-formal descriptions of knowledge.

Paper Link

Lindstaedt Stefanie , Hambach S., Müsebeck P., de Hoog R., Kooken J., Musielak M.
2009
Computational support for work-integrated learning will gain more and moreattention. We understand informal self-directed work-integrated learning of knowledgeworkers as a by-product of their knowledge work activities and propose a conceptual as wellas a technical approach for supporting learning from documents and learning in interactionwith fellow knowledge workers. The paper focuses on contextualization and scripting as twomeans to specifically address the latter interaction type.

Paper Link

Lindstaedt Stefanie , Moerzinger R., Sorschag R. , Pammer-Schindler Viktoria, Thallinger G.
2009
Automatic image annotation is an important and challenging task, andbecomes increasingly necessary when managing large image collections. This paperdescribes techniques for automatic image annotation that take advantage of collaborativelyannotated image databases, so called visual folksonomies. Our approachapplies two techniques based on image analysis: First, classification annotates imageswith a controlled vocabulary and second tag propagation along visually similar images.The latter propagates user generated, folksonomic annotations and is thereforecapable of dealing with an unlimited vocabulary. Experiments with a pool of Flickrimages demonstrate the high accuracy and efficiency of the proposed methods in thetask of automatic image annotation. Both techniques were applied in the prototypicaltag recommender “tagr”.

Paper Link

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.

Paper Link

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

Paper Link

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.

Paper Link

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.

Paper Link

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.

Paper Link

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.

Paper Link

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.

Paper Link

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.

Paper

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.

Link

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.

Paper Link

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.

Paper Link

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.

Paper Link

Zimmermann Volker, Fredrich Helge, Grohmann Guido, Hauer Dominik, Sprenger Peter, Leyking Katrina, Martin Gunnar, Loos Peter, Naeve Ambjörn, Karapidis Alexander, Pack Jochen, Lindstaedt Stefanie , Chatti Mohamed Amine, Klamma Ralf, Jarke Matthias, Lefere Paul
2007
Given the importance of an organisation’s human capital to business success, aligning training and competencydevelopment with business needs is a key challenge. Many companies did initiate in the pastknowledge management activities or founded corporate universities as the organization intended to helpcompanies to face this challenge. In this deliverable, we talk about knowledge work and learning managementas a concept to “increase business performance” through a better short- and long-term learningapproach for people at management level. The aim is to provide a guideline for corporate users based onour and others' experiences of implementing solutions for knowledge work and learning. This is connectedto many forms and methods of learning: formal learning processes, informal learning, team learning,collaboration, social networking, community building etc. In many companies, managers think thatknowledge work can be supported solely by offering courses and enabling to access content on demand.In this deliverable this aspect (ACQUIRING knowledge) will not be in focus as it is more the job of atraining department to manage courses and catalogues. Instead we focus on APPLYING knowledge. Theconcept of knowledge work management comes into place, when companies see the ability of their employeesto APPLY their education and knowledge as a strategic instrument to create competitiveness andlook for tools to provide learning and knowledge at workplace on demand and fitting to the individualneeds. And this objective is very actual as the globalization creates pressure on companies and theknowledge and experience of the employees gets the most important differentiator to competitors – leadingto better innovation, faster processes, higher productivity and lower costs. In this deliverable, anoverall approach and guideline for companies will be provided on how to implement knowledge workmanagement and provide learning according to the needs in business and resulting from business processes.

Paper Link

Scheir Peter, Granitzer Michael, Lindstaedt Stefanie
2007
Evaluation of information retrieval systems is a critical aspect of information retrieval research. New retrieval paradigms, as retrieval in the Semantic Web, present an additional challenge for system evaluation as no off-the-shelf test corpora for evaluation exist. This paper describes the approach taken to evaluate an information retrieval system built for the Semantic Desktop and demonstrates how standard measures from information retrieval research are employed for evaluation.

Link

Strohmaier M., Lindstaedt Stefanie
2007
Purpose: The purpose of this contribution is to motivate a new, rapid approachto modeling knowledge work in organizational settings and to introducea software tool that demonstrates the viability of the envisioned concept.Approach: Based on existing modeling structures, the KnowFlowr Toolsetthat aids knowledge analysts in rapidly conducting interviews and in conductingmulti-perspective analysis of organizational knowledge work is introduced.Findings: It is demonstrated how rapid knowledge work visualization can beconducted largely without human modelers by developing an interview structurethat allows for self-service interviews. Two application scenarios illustrate thepressing need for and the potentials of rapid knowledge work visualizations inorganizational settings.Research Implications: The efforts necessary for traditional modeling approachesin the area of knowledge management are often prohibitive. Thiscontribution argues that future research needs to take economical constraintsof organizational settings into account in order to be able to realize the fullpotential of knowledge work management.Practical Implications: This work picks up a problem identified in practiceand proposes the novel concept of rapid knowledge work visualization for makingknowledge work modeling in organizations more feasible.Value: This work develops a vision of rapid knowledge work visualization andintroduces a tool-supported approach that addresses some of the identified challenges.

Paper Link

Kröll Mark, Rath Andreas S., Weber Nicolas, Lindstaedt Stefanie , Granitzer Michael
2007
Knowledge-intensive work plays an increasingly important role in organisations of all types. Knowledge workers contribute their effort to achieve a common purpose; they are part of (business) processes. Workflow Management Systems support them during their daily work, featuring guidance and providing intelligent resource delivery. However, the emergence of richly structured, heterogeneous datasets requires a reassessment of existing mining techniques which do not take possible relations between individual instances into account. Neglecting these relations might lead to inappropriate conclusions about the data. In order to uphold the support quality of knowledge workers, the application of mining methods, that consider structure information rather than content information, is necessary. In the scope of the research project DYONIPOS, user interaction patterns, e.g., relations between users, resources and tasks, are mapped in the form of graphs. We utilize graph kernels to exploit structural information and apply Support Vector Machines to classify task instances to task models

Paper

Rath Andreas S., Kröll Mark, Lindstaedt Stefanie , Granitzer Michael
2007
Knowledge intensive organizations demand a rethinking of business process awareness. Their employees are knowledge workers, who are performing their tasks in a weakly structured way. Stiff organizational processes have to be relaxed, adopted and flexibilized to be able to provide the essential freedom requested by knowledge workers. For effectively and efficiently supporting this type of creative worker the hidden patterns, i.e. how they reach their goals, have to be discovered. This paper focuses on perceiving the knowledge workers work habits in an automatic way for bringing their work patterns to the surface. Capturing low level operating system events, observing user interactions on a fine granular level and doing in deep application inspection, give the opportunity to interrelate the received data. In the scope of the research project DYONIPOS these interrelation abilities are utilized to semantically relate and enrich this captured data to picture the actual task of a knowledge worker. Once the goal of a knowledge worker is clear, intelligent information delivery can be applied

Scheir Peter, Granitzer Michael, Lindstaedt Stefanie , Hofmair P.
2006
In this contribution we present a tool for annotating documents, which are used for workintegratedlearning, with concepts from an ontology. To allow for annotating directly whilecreating or editing an ontology, the tool was realized as a plug-in for the ontology editor Protégé.Annotating documents with semantic metadata is a laborious task, most of the time knowledgerepresentations are created independently from the resources that should be annotated andadditionally in most work environments a high number of documents exist. To increase theefficiency of the person annotating, in our tool the process of assigning concepts to text-documentsis supported by automatic text-classification.

Link

Rath Andreas S., Kröll Mark, Andrews K., Lindstaedt Stefanie , Granitzer Michael
2006
In a knowledge-intensive business environment, knowledgeworkers perform their tasks in highly creative ways. This essential freedomrequired by knowledge workers often conflicts with their organization’sneed for standardization, control, and transparency. Within thiscontext, the research project DYONIPOS aims to mitigate this contradictionby supporting the process engineer with insights into the processexecuter’s working behavior. These insights constitute the basis for balancedprocess modeling. DYONIPOS provides a process engineer supportenvironment with advanced process modeling services, such as processvisualization, standard process validation, and ad-hoc process analysisand optimization services.

Link

Lindstaedt Stefanie , Ulbrich Armin
2006
Granitzer Michael, Lindstaedt Stefanie , Tochtermann K., Kröll Mark, Rath Andreas S.
2006
Knowledge-intensive work plays an increasinglyimportant role in organisations of all types. Thiswork is characterized by a defined input and adefined output but not the way how to transformthe input to an output. Within this context, theresearch project DYONIPOS aims at encouragingthe two crucial roles in a knowledge-intensiveorganization - the process executer and the processengineer. Ad-hoc support will be providedfor the knowledge worker by synergizing the developmentof context sensitive, intelligent, andagile semantic technologies with contextual retrieval.DYONIPOS provides process executerswith guidance through business processes andjust-in-time resource support based on the currentuser context, that are the focus of this paper.

Paper

Ley Tobias, Kump Barbara, Lindstaedt Stefanie , Albert D., Maiden N. A. M., Jones S.
2006
Challenges for learning in knowledge work are being discussed.These include the challenge to better support self-directed learning whileaddressing the organizational goals and constraints at the same time, andproviding guidance for learning. The use of competencies is introduced as away to deal with these challenges. Specifically, the competence performanceapproach offers ways to better leverage organizational context and to supportinformal learning interventions. A case study illustrates the application of thecompetence performance approach for the learning domain of requirementsengineering. We close with conclusions and an outlook on future work.

Link

Lindstaedt Stefanie , Mayer H.
2006
The goal of the APOSDLE (Advanced Process-Oriented SelfDirectedLearning environment) project is to enhance knowledge worker productivityby supporting informal learning activities in the context of knowledgeworkers’ everyday work processes and within their work environments. Thiscontribution seeks to communicate the ideas behind this abstract vision to thereader by using a storyboard, scenarios and mock-ups. The project just startedin March 2006 and is funded within the European Commission’s 6th FrameworkProgram under the IST work program. APOSDLE is an Integrated Projectjointly coordinated by the Know-Center, Austria’s Competence Centre forKnowledge Management, and Joanneum Research. APOSDLE brings together12 partners from 7 European Countries.

Paper Link

Ulbrich Armin, Lindstaedt Stefanie , Scheir Peter, Goertz M.
2006
This contribution introduces the so-called Workplace Learning Contextas essential conceptualisation supporting self-directed learning experiencesdirectly at the workplace. The Workplace Learning Context is to be analysedand exploited for retrieving ‘learning’ material that best-possibly matches witha knowledge worker’s current learning needs. In doing so, several different‘flavours’ of work-integrated learning can be realised including task learning,competency-gap based support and domain-related support. The WorkplaceLearning Context Model, which is also outlined in this contribution, forms thetechnical representation of the Workplace Learning Context.

Paper Link

Ley Tobias, Lindstaedt Stefanie , Albert D.
2005
This paper seeks to suggest ways to support informal, self-directed, work-integrated learning within organizations. We focus on a special type of learning in organizations, namely on competency development, that is a purposeful development of employee capabilities to perform well in a large array of situations. As competency development is inherently a self-directed development activity, we seek to support these activities primarily in an informal learning context. AD-HOC environments which allow employees context specific access to documents in a knowledge repository have been suggested to support learning in the workplace. In this paper, we suggest to use the competence performance framework as a means to enhance the capabilities of AD HOC environments to support competency development. The framework formalizes the tasks employees are working in and the competencies needed to perform the tasks. Relating tasks and competencies results in a competence performance structure, which structures both tasks and competencies in terms of learning prerequisites. We conclude with two scenarios that make use of methods established in informal learning research. The scenarios show how competence performance structures enhance feedback mechanisms in a coaching process between supervisor and employee and provide assistance for self directed learning from a knowledge repository.

Paper Link

Timbrell G., Koller S., Schefe N., Lindstaedt Stefanie
2005
This paper explores a process view of call-centres and the knowledge infrastructuresthat support these processes. As call-centres grow and become more complex in their functionand organisation so do the knowledge infrastructures required to support their size andcomplexity. This study suggests a knowledge-based hierarchy of ‘advice-type’ call-centres anddiscusses associated knowledge management strategies for different sized centres. It introducesa Knowledge Infrastructure Hierarchy model, with which it is possible to analyze and classifycall-centre knowledge infrastructures. The model also demonstrates different types ofinterventions supporting knowledge management in call-centres. Finally the paper discusses thepossibilities of applying traditional maturity model approaches in this context.

Paper Link

Lindstaedt Stefanie , Ley Tobias, Farmer Johannes
2004

Lindstaedt Stefanie , Koller S., Krämer T.
2004

Farmer J., Lindstaedt Stefanie , Droschl G., Luttenberger P.
2004
Carrying out today’s knowledge work without information and communicationtechnology (ICT) is unimaginable. ICT makes it possible to process and exchangeinformation quickly and efficiently. However, accomplishing tasks with ICT isoften tedious: Colleagues have to be asked, how best to proceed. Necessaryresources have to be searched for in the intranet and internet. And one has toget familiar with applying the various systems and tools. This way, solving asimple task can become a time consuming process for inexperienced employeesand also for those who are asked for their expertise.Therefore, at the Know-Center Graz, Austria , the AD-HOC methodology hasbeen developed to support knowledge workers in task-oriented learning andteaching situations. This methodology is used to analyse the work processes, toidentify the needed resources, tools, and systems, and finally to design an ADHOCEnvironment. In this environment, systems and tools are arranged forspecific work processes. Users are then guided at their work tasks and areprovided with the necessary resources instantly.This article presents the AD-HOC methodology. It analyses the obstacles thathamper efficient knowledge work and how AD-HOC overcomes them. Finally, thesupport of users at their specific work tasks by deployed AD-HOC Environmentsis shown in two field studies.

Paper

Timbrell G., Koller S., Lindstaedt Stefanie
2004

Lindstaedt Stefanie , Farmer J.
2004

Lindstaedt Stefanie , Farmer J., Ley Tobias
2004

Lindstaedt Stefanie , Farmer J., Hrastnik J., Rollett H., Strohmaier M.
2003
Personalisierbare Portale als Fenster zu Unternehmensgedachtnissen finden ¨in der Praxis immer haufiger Anwendung. Bei dem Design dieser Portale stellt sich die ¨Frage nach der Strukturierung der Informationen: auf der einen Seite soll der tagliche ¨Arbeitsprozess unterstutzt werden, auf der anderen Seite sollen aber auch Informa- ¨tionen zuganglich gemacht werden, die den Prozess in einen gr ¨ oßeren Kontext set- ¨zen. Unsere Erfahrungen bei der Portalentwicklungen haben gezeigt, dass zwei unterschiedlicheStrategien Anwendung finden: Prozessfokus und Wissensfokus. Die Wahlder individuellen Strategie hangt einerseits von den Zielen ab, die das Portal erf ¨ ullen ¨soll. Andererseits hangen sie aber auch von der Ausgangssituation im anwendenden ¨Unternehmen ab. Dieser Beitrag stellt die beiden Designstrategien vor und identifiziertRahmenbedingungen, die bei der Wahl der Stategie helfen konnen.

Paper

Tochtermann K., Zirm K., Lindstaedt Stefanie
2003

Lindstaedt Stefanie
2002

Lindstaedt Stefanie , Fischer M.
2002

Westbomke J., Kussmaul A., Raiber A., Haase M., Hicks D., Lindstaedt Stefanie
2002
This paper presents research results obtained from the project Personal AdaptableDigital Library Environment (PADDLE). The main focus of the DFG funded research projectis to apply concepts of knowledge management to digital libraries by introducingpersonalization techniques. The idea is to enable the specific needs, experiences, skills andtasks of a knowledge worker using a digital library could be taken into account. Metadata is thekey issue for doing this. Therefore the PADDLE system architecture describes a metadatamanager, which allows the association of metadata with the knowledge objects stored indistributed information resources. Based on this architecture several personalization conceptslike workspaces and profiles are introduced. Finally, a geographic information portal isdescribed that realizes a new way of seeking and accessing geodata related knowledge objectswithin a digital library.

Paper

Lindstaedt Stefanie , Strohmaier M., Rollett Herwig, Hrastnik Janez, Bruhnsen Karin, Droschl Georg, Gerold Markus
2002
One of the first question each knowledge management project facesis: Which concrete activities are referred to under the name of knowledgemanagement and how do they relate to each other? To help answer this questionand to provide guidance when introducing knowledge management we havedeveloped KMap. KMap is an environment which supports a practitioner in theinteractive exploration of a map of knowledge management activities. Theinteraction helps trigger interesting questions crucial to the exploration of thesolution space and makes hidden argumentation lines visible. KMap is not anew theory of knowledge management but a pragmatic “object to think with”and is currently in use in two case studies.

Paper

Lindstaedt Stefanie , Strohmaier M.
2002

Lindstaedt Stefanie
2002

Lindstaedt Stefanie , Scheir Peter, Sarka W.
2002

Lindstaedt Stefanie
2002
Wir betrachten kooperative Lern- und Lehrsituationen im Kontext dertäglichen Arbeitsprozesse aus der Perspektive des Wissensmanagements. In einerCase Study bei DaimlerChrysler wurden Szenarien entwickelt, in denen Wissen inGruppen erarbeitet und weitergegeben wird, um konkrete Arbeitsaufgaben unterZeitdruck erfüllen zu können. Zur Zeit entwickeln wir im Kontext eines WissensmanagementsystemsMethoden und technische Hilfsmittel zur Unterstützung dieseraufgaben-orientierten kooperativen Lern- und Lehrprozesse.

Paper

Lindstaedt Stefanie
2001

Lindstaedt Stefanie
2000

Pammer-Schindler Viktoria, Lindstaedt Stefanie