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.
Wolfbauer Irmtraud, Pammer-Schindler Viktoria, Maitz Katharina, Rosé Carolyn P.
2022
We present a script for conversational reflection guidance embedded in reflective practice. Rebo Junior, a non-adaptive conversational agent, was evaluated in a 12-week field study with apprentices. We analysed apprentices’ interactions with Rebo Junior in terms of reflectivity, and measured the development of their reflection competence via reflective essays at three points in time during the field study. Reflection competence, a key competency for lifelong professional learning, becomes significantly higher by the third essay, after repeated interactions with Rebo Junior (paired-samples t-test t13=3.00, p=.010 from Essay 1 to Essay 3). However, we also observed a significant decrease in reflectivity in the Rebo Junior interactions over time (paired-samples t-test between the first and eighth interaction: t7=2.50, p=.041). We attribute this decline to i) the novelty of Rebo Junior wearing off (novelty effect) and ii) the apprentices learning the script and experiencing subsequent frustration due to the script not fading over time. Overall, this work i) informs future design through the observation of consistent decreases in engagement over 8 interactions with static scaffolding, and ii) contributes a reflection script applicable for reflection on tasks that resemble future expected work tasks, a typical setting in lifelong professional learning, and iii) indicates increased reflection competence after repeated reflection guided by a conversational agent.
Mirzababaei Behzad, Pammer-Schindler Viktoria
2021
This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.
Mirzababaei Behzad, Pammer-Schindler Viktoria
2021
This article discusses the usefulness of Toulmin’s model of arguments as structuring an assessment of different types of wrongness in an argument. We discuss the usability of the model within a conversational agent that aims to support users to develop a good argument. Within the article, we present a study and the development of classifiers that identify the existence of structural components in a good argument, namely a claim, a warrant (underlying understanding), and evidence. Based on a dataset (three sub-datasets with 100, 1,026, 211 responses in each) in which users argue about the intelligence or non-intelligence of entities, we have developed classifiers for these components: The existence and direction (positive/negative) of claims can be detected a weighted average F1 score over all classes (positive/negative/unknown) of 0.91. The existence of a warrant (with warrant/without warrant) can be detected with a weighted F1 score over all classes of 0.88. The existence of evidence (with evidence/without evidence) can be detected with a weighted average F1 score of 0.80. We argue that these scores are high enough to be of use within a conditional dialogue structure based on Bloom’s taxonomy of learning; and show by argument an example conditional dialogue structure that allows us to conduct coherent learning conversations. While in our described experiments, we show how Toulmin’s model of arguments can be used to identify structural problems with argumentation, we also discuss how Toulmin’s model of arguments could be used in conjunction with content-wise assessment of the correctness especially of the evidence component to identify more complex types of wrongness in arguments, where argument components are not well aligned. Owing to having progress in argument mining and conversational agents, the next challenges could be the developing agents that support learning argumentation. These agents could identify more complex type of wrongness in arguments that result from wrong connections between argumentation components.
Pammer-Schindler Viktoria, Prilla Michael
2021
A substantial body of human-computer interaction literature investigates tools that are intended to support reflection, e.g. under the header of quantified self or in computer-mediated learning. These works describe the issues that are reflected on by users in terms of examples, such as reflecting on financial expenditures, lifestyle, professional growth, etc. A coherent concept is missing. In this paper, the reflection object is developed based on activity theory, reflection theory and related design-oriented research. The reflection object is both what is reflected on and what is changed through reflection. It constitutes the link between reflection and other activities in which the reflecting person participates. By combining these two aspects—what is reflected on and what is changed—into a coherent conceptual unit, the concept of the reflection object provides a frame to focus on how to support learning, change and transformation, which is a major challenge when designing technologies for reflection.
Pammer-Schindler Viktoria, Rosé Carolyn
2021
Professional and lifelong learning are a necessity for workers. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. We describe the encountered data-related ethics issues. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. Such data may be private, proprietary, or both. Secondly, manual tracking comes with high learner control over data. Thirdly, learning is not necessarily a shared goal in informal professional learning settings. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. As an outlook of this paper, we connect these challenges to ongoing research directions within AIED – natural language processing, socio-technical design, and scenario-based data collection - that might be leveraged and aimed towards addressing data-related ethics challenges.
Rauter Romana, Lerch Anita, Lederer-Hutsteiner Thomas, Klinger Sabine, Mayr Andrea, Gutounig Robert, Pammer-Schindler Viktoria
2020
Dennerlein Sebastian, Wolf-Brenner Christof, Gutounig Robert, Schweiger Stefan, Pammer-Schindler Viktoria
2020
Künstliche Intelligenz (KI) ist zum Gegenstand gesellschaftlicher Debatten geworden. Die Beratung durch KI unterstützt uns in der Schule, im Alltag beim Einkauf, bei der Urlaubsplanung und beim Medienkonsum, manipuliert uns allerdings auch gezielt bei Entscheidungen oder führt durch Filter-Bubble-Phänomene zur Realitätsverzerrung.Eine der jüngsten Aufregungen hierzulande galt der Nutzung moderner Algorithmik durch das österreichische Arbeitsmarktservice AMS. Der sogenannte "AMS-Algorithmus" soll Beratende bei der Entscheidung über Fördermaßnahmen unterstützen.Wenn KI in einem so erheblichen Ausmaß in menschliches Handeln eingreift, bedarf sie im Hinblick auf ethische Prinzipien einer sorgfältigen Bewertung. Das ist notwendig, um unethische Folgen zu vermeiden. Üblicherweise wird gefordert, KI bzw. Algorithmen sollen fair sein, was bedeutet, sie sollen nicht diskriminieren und transparent sollen sie sein, also Einblick in ihre Funktionsweise ermöglichen
Fruhwirth Michael, Ropposch Christiana, Pammer-Schindler Viktoria
2020
Purpose: This paper synthesizes existing research on tools and methods that support data-driven business model innovation, and maps out relevant directions for future research.Design/methodology/approach: We have carried out a structured literature review and collected and analysed a respectable but not excessively large number of 33 publications, due to the comparatively emergent nature of the field.Findings: Current literature on supporting data-driven business model innovation differs in the types of contribution (taxonomies, patterns, visual tools, methods, IT tool and processes), the types of thinking supported (divergent and convergent) and the elements of the business models that are addressed by the research (value creation, value capturing and value proposition).Research implications: Our review highlights the following as relevant directions for future research. Firstly, most research focusses on supporting divergent thinking, i.e. ideation. However, convergent thinking, i.e. evaluating, prioritizing, and deciding, is also necessary. Secondly, the complete procedure of developing data-driven business models and also the development on chains of tools related to this have been under-investigated. Thirdly, scarcely any IT tools specifically support the development of data-driven business models. These avenues also highlight the necessity to integrate between research on specifics of data in business model innovation, on innovation management, information systems and business analytics.Originality/Value: This paper is the first to synthesize the literature on how to identify and develop data-driven
Barreiros Carla, Pammer-Schindler Viktoria, Veas Eduardo Enrique
2019
We present a visual interface for communicating the internal state of a coffee machine via a tree metaphor. Nature-inspired representations have a positive impact on human well-being. We also hypothesize that representing the coffee machine asa tree stimulates emotional connection to it, which leads to better maintenance performance.The first study assessed the understandability of the tree representation, comparing it with icon-based and chart-based representations. An online survey with 25 participants indicated no significant mean error difference between representations.A two-week field study assessed the maintenance performance of 12 participants, comparing the tree representation with the icon-based representation. Based on 240 interactions with the coffee machine, we concluded that participants understood themachine states significantly better in the tree representation. Their comments and behavior indicated that the tree representation encouraged an emotional engagement with the machine. Moreover, the participants performed significantly more optional maintenance tasks with the tree representation.
Renner Bettina, Wesiak Gudrun, Pammer-Schindler Viktoria, Prilla Michael, Müller Lars, Morosini Dalia, Mora Simone, Faltin Nils, Cress Ulrike
2019
Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria
2018
This paper describes a novel visual metaphor to communicate sensor information of a connected device. The Internet of Things aims to extend every device with sensing and computing capabilities. A byproduct is that even domestic machines become increasingly complex, tedious to understand and maintain. This paper presents a prototype instrumenting a coffee machine with sensors. The machine streams the sensor data, which is picked up by an augmented reality application serving a nature metaphor. The nature metaphor, BioAR, represents the status derived from the coffee machine sensors in the features of a 3D virtual tree. The tree is meant to pass for a living proxy of the machine it represents. The metaphor, shown either with AR or a simple holographic display, reacts to the user manipulation of the machine and its workings. A first user study validates that the representation is correctly understood, and that it inspires affect for the machine. A second user study validates that the metaphor scales to a large number of machines.
Thalmann Stefan, Pammer-Schindler Viktoria
2017
Aktuelle Untersuchungen zeigen einerseits auf, dass der Mensch weiterhin eine zentrale Rolle in der Industrie spielt. Andererseits ist aber auch klar, dass die Zahl der direkt in der Produktion beschäftigten Mitarbeter sinken wird. Die Veränderung wird dahin gehen, dass der Mensch weniger gleichförmige Prozese bearbeitet, stattdessen aber in der Lage sein muss, sich schnell ändernden Arbeitstätigkeiten azupassen und individualisierte Fertigungsprozesse zu steuern. Die Reduktion der Mitarbeiter hat jedoch auch eine Reduktion von Redunanzen zur Folge. Dies führt dazu, dass dem Einzelnen mehr Verantwortung übertragen wird. Als Folge haben Fehlentscheidungen eine görßere Tragweite und bedeuten somit auch ein höheres Risikio. Der Erfolg einer Industrie 4.0 Kampagne wird daher im Wesentlichen von den Anpassungsfähigkeiten der Mitarbeiter abhängen.
Pammer-Schindler Viktoria, Fessl Angela, Weghofer Franz, Thalmann Stefan
2017
Die Digitalisierung der Industrie wird aktuell sehr stark aus technoogischer Sicht betrachtet. Aber auch für den Menschen ergebn sich vielfältige Herausforderungen in dieser veränderten Arbeitsumgebung. Sie betreffen hautsächlich das Lernen von benötigtem Wissen.
Pammer-Schindler Viktoria, Fessl Angela, Wesiak Gudrun, Feyertag Sandra, Rivera-Pelayo Verónica
2017
This paper presents a concept for in-app reflection guidance and its evaluation in four work-related field trials. By synthesizing across four field trials, we can show that computer-based reflection guidance can function in the workplace, in the sense of being accepted as technology, being perceived as useful and leading to reflective learning. This is encouraging for all endeavours aiming to transfer existing knowledge on reflection supportive technology from educational settings to the workplace. However,reflective learning in our studies was mostly visible to limited depth in textual entries made in the applications themselves; and proactive reflection guidance technology like prompts were often found to be disruptive. We offer these two issues as highly relevant questions for future research.
Pammer-Schindler Viktoria, Rivera-Pelayo Verónica, Fessl Angela, Müller Lars
2017
The benefits of self-tracking have been thoroughly investigated in private areas of life, like health or sustainable living, but less attention has been given to the impact and benefits of self-tracking in work-related settings. Through two field studies, we introduced and evaluated a mood self-tracking application in two call centers to investigate the role of mood self-tracking at work, as well as its impact on individuals and teams. Our studies indicate that mood self-tracking is accepted and can improve performance if the application is well integrated into the work processes and matches the management style. The results show that (i) capturing moods and explicitly relating them to work tasks facilitated reflection, (ii) mood self-tracking increased emotional awareness and this improved cohesion within teams, and (iii) proactive reactions by managers to trends and changes in team members’ mood were key for acceptance of reflection and correlated with measured improvements in work performance. These findings help to better understand the role and potential of self-tracking in work settings and further provide insights that guide future researchers and practitioners to design and introduce these tools in a workplace setting.
Fessl Angela, Pammer-Schindler Viktoria, Blunk Oliver, Prilla Michael
2016
Reflective learning has been established as a process that deepenslearning in both educational and work-related settings. We present a literaturereview on various approaches and tools (e.g., prompts, journals, visuals)providing guidance for facilitating reflective learning. Research consideredin this review coincides common understanding of reflective learning, hasapplied and evaluated a tool supporting reflection and presents correspondingresults. Literature was analysed with respect to timing of reflection, reflectionparticipants, type of reflection guidance, and results achieved regardingreflection. From this analysis, we were able to derive insights, guidelinesand recommendations for the design of reflection guidance functionality incomputing systems: (i) ensure that learners understand the purpose of reflectivelearning, (ii) combine reflective learning tools with reflective questions either inform of prompts or with peer-to-peer or group discussions, (iii) for work-relatedsettings consider the time with regard to when and how to motivate to reflect.
Simon Jörg Peter, Schmidt Peter, Pammer-Schindler Viktoria
2016
Synchronisation algorithms are central to collaborative editing software. As collaboration is increasingly mediated by mobile devices, the energy efficiency for such algorithms is interest to a wide community of application developers. In this paper we explore the differential synchronisation (diffsync) algorithm with respect to energy consumption on mobile devices. Discussions within this paper are based on real usage data of PDF annotations via the Mendeley iOS app, which requires realtime synchronisation. We identify three areas for optimising diffsync: a.) Empty cycles in which no changes need to be processed b.) tail energy by adapting cycle intervals and c.) computational complexity. Following these considerations, we propose a push-based diffsync strategy in which synchronisation cycles are triggered when a device connects to the network or when a device is notified of changes.
Fessl Angela, Bratic Marina, Pammer-Schindler Viktoria
2014
A continuous learning solution was sought which allows strokenurses to keep the vast body of theoretical knowledge fresh, stay up-to-datewith new knowledge, and relate theoretical knowledge to practical experience.Based on the theoretical background of learning in the medical domain,reflective and game-based learning, we carried out a user-oriented designprocess that involved a focus group and a design workshop. In this process, aquiz that includes both content-based and reflection questions was identified asa viable means of transportation for theoretical knowledge. In this paper wepresent the result of trialling a quiz with both content-based and metacognitive(reflective) questions in two settings: In one trial the quiz was used by nursesas part of a qualification programme for stroke nurses, in the second trial bynurses outside such a formal continuous learning setting. Both trials weresuccessful in terms of user acceptance, user satisfaction and learning. Beyondthis success report, we discuss barriers to integrating a quiz into work processeswithin an emergency ward such as a stroke unit.
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.
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.
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”.
Pammer-Schindler Viktoria, Lindstaedt Stefanie
Cicchinelli Analia, Pammer-Schindler Viktoria
Purpose – The goal of this study is to understand what drives people (i.e., their motivations, autonomous learning attitudes and learning interests) to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.Design/methodology/approach – A three-phase method was used to i) create volunteer mentor profiles; ii) elicit topics of interest; and iii) establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitudes towards lifelong learning and self-regulated learning strategies. The topics of interests were defined by analyzing answers to reflection questions. Statistical methods were applied to analyze the relationships between the interests and the mentor profiles.Findings –Three mentor groups (G1 “low,” G2 “high” and G3 “medium”) were identified based on pre-survey data via bottom-up clustering. Content analysis was used to define the topics of interest: communication skills; learning AI; mentoring; prototype development; problem solving skills; and working with families. Examining relationships between the mentor profile and the topics of interest showed that group G3 “medium” with strong intrinsic motivation had significantly more interest in working with families. The group with overall highest scores (G2 “high”) expressed substantial interest in learning about AI. However, there was a high variability between members of this group. Originality/value –The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes towards lifelong learning. Such knowledge can help organizations shape volunteering experience, offering more value to volunteers. Furthermore, the reflection questions can be used by: i) volunteers as an instrument of reflection; and ii) organizations for eliciting learning interests of volunteers.