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Thiemo Wambsganss

Wissenschaftlicher Mitarbeiter
location_on
IWI-HSG
52-6026
apartment
Müller-Friedbergstrasse 8
9000 St. Gallen
mail
phone
+41 71 224 3234
home
https://thiemowa.github.io/

Schwerpunkte


  • AI-based Adaptive Learning
  • Argumentation Mining
  • Adaptive Skill Learning
  • Empathy Detection
  • Technology-mediated Learning
  • Conversational Agents in Education
  • Natural Language Processing
  • Machine Learning
  • Deep Learning
  • Forschungsgebiete


  • Human-Computer Interaction
  • Natural Language Processing
  • Educational Technology
  • Information Systems
  • Publikationen


    We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of α = 0.79 for the components and the π = 0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.

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    Argumentation skills are an omnipresent foundation of our daily communication and thinking. However, the learning of argumentation skills is limited due to the lack of individual learning conditions for students. Within this dissertation, I aim to explore the potential of adaptive argumentation skill learning based on Artificial Intelligence (AI) by designing, implementing, and evaluating new technology-enhanced pedagogical concepts to actively support students in developing the ability to argue in a structured, logical, and reflective way. I develop new student-centered pedagogical scenarios with empirically evaluated design principles, linguistic corpora, ML algorithms, and innovative learning tools based on an adaptive writing support system and a pedagogical conversational agent. My results indicate that adaptive learning tools based on ML algorithms and user-centered design patterns help students to develop better argumentation writing skills. Thereby, I contribute to research by bridging the boundaries of argumentation learning and argumentation mining and by examining pedagogical scenarios for adaptive argumentation learning from a user-centered perspective.

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    get_appThiemo Wambsganss, Tobias Küng, Söllner Matthias, Jan Marco Leimeister
    Wissenschaftlicher Artikel
    Techniques from Natural-Language-Processing offer the opportunities to design new dialog-based forms of human-computer interaction as well as to analyze the argumentation quality of texts. This can be leveraged to provide students with adaptive tutoring when doing a persuasive writing exercise. To test if individual tutoring for students' argumentation will help them to write more convincing texts, we developed ArgueTutor, a conversational agent that tutors students with adaptive argumentation feedback in their learning journey. We compared ArgueTutor with 55 students to a traditional writing tool. We found students using ArgueTutor wrote more convincing texts with a better quality of argumentation compared to the ones using the alternative approach. The measured level of enjoyment and ease of use provides promising results to use our tool in traditional learning settings. Our results indicate that dialog-based learning applications combined with NLP text feedback have a beneficial use to foster better writing skills of students.

    Mehr
    get_appThiemo Wambsganss, Florian Weber, Matthias Söllner
    Wissenschaftlicher Artikel
    Empathy is an elementary skill for daily interactions and for professional communication, agile teamwork and successful leadership and thus elementary for educational curricula. However, educational organizations face difficulties in providing the boundary conditions necessary for their students to develop empathy skills due to the lack of individual support in traditional large-scale and growing distance-learning scenarios. Drawing on cognitive dissonance theory, we propose an adaptive empathy learning tool that helps students develop their ability to react to other people’s observed experiences through individual feedback in large-scale or distance learning scenarios. Based on a design science research project, we propose a set of design principles and instantiate and evaluate them with our prototype Eva in an online experiment with 65 students. The findings suggest that an adaptive empathy learning tool that follows our design principles is a promising approach to individually support students in their ability to react to other people’s observed abilities in traditional learning scenarios.

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    get_appSeverin Landolt, Thiemo Wambsganss, Matthias Söllner
    Wissenschaftlicher Artikel
    Despite a large number of available techniques around Deep Learning in Natural Language Processing (NLP), no holistic framework exists which supports researchers and practitioners to organise knowledge when designing, comparing and evaluating NLP applications. This paper addresses this lack of a holistic framework by developing a taxonomy for Deep Learning in Natural Language Processing. Based on a systematic literature review as proposed by Webster and Watson [1] and vom Brocke et al. [2] and the iterative taxonomy development process of Nickerson et al. [3] we derived five novel dimensions and 38 characteristics based on a sample of 205 papers. Our research suggests, that a Deep Learning NLP approach can be distinguished by five dimensions which were partly derived from the CRISP-DM methodology: application understanding, data preparation, modeling, learning technique and evaluation. We, therefore, hope to provide guidance and support for researchers and practitioners when using Deep Learning for NLP to design, compare and evaluate NLP applications.

    Mehr
    Recent advances in Natural Language Processing not only bear the opportunity to design new dialog-based forms of human-computer interaction but also to analyze the argumentation quality of texts. Both can be leveraged to provide students with individual and adaptive tutoring in their personal learning journey to develop argumentation skills. Therefore, we present the results of our design science research project on how to design an adaptive dialog-based tutoring system to help students to learn how to argue. Our results indicate the usefulness of an adaptive dialog-based tutoring system to support students individually, independent of a human instructor, time and place. In addition to providing our embedded software artifact, we document our evaluated design knowledge as a design theory. Thus, we provide the first step toward a nascent design theory for adaptive conversational tutoring systems to individual support metacognition skill education of students in traditional learning scenarios.

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    get_appThiemo Wambsganss, Christina Niklaus, Matthias Cetto, Matthias Söllner, Siegfried Handschuh, Jan Marco Leimeister
    Wissenschaftlicher Artikel
    Recent advances in Natural Language Processing (NLP) bear the opportunity to analyze the argumentation quality of texts. This can be leveraged to provide students with individual and adaptive feedback in their personal learning journey. To test if individual feedback on students' argumentation will help them to write more convincing texts, we developed AL, an adaptive IT tool that provides students with feedback on the argumentation structure of a given text. We compared AL with 54 students to a proven argumentation support tool. We found students using AL wrote more convincing texts with better formal quality of argumentation compared to the ones using the traditional approach. The measured technology acceptance provided promising results to use this tool as a feedback application in different learning settings. The results suggest that learning applications based on NLP may have a beneficial use for developing better writing and reasoning for students in traditional learning settings.

    Mehr
    Recent advances in Natural Language Processing (NLP) bear the opportunity to design new forms of human-computer interaction with conversational interfaces. We hypothesize that these interfaces can interactively engage students to increase response quality of course evaluations in education compared to the common standard of web surveys. Past research indicates that web surveys come with disadvantages, such as poor response quality caused by inattention, survey fatigue or satisficing behavior. To test if conversational interfaces have a positive impact on the level of enjoyment and the response quality, we design an NLP-based conversational agent and deploy it in a field experiment with 127 students in our lecture and compare it with a web survey as a baseline. Our findings indicate that using conversational agents for evaluations are resulting in higher levels of response quality and level of enjoyment, and are therefore, a promising approach to increase the effectiveness of surveys in general.

    Mehr
    Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-labor-intensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a two-fold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge.

    Mehr
    Die Digitalisierung führt zu neuen Anforderungen an Fähigkeiten und Kenntnissen, die Studierende in ihrem zukünftigen Berufsleben benötigen. Metakognitive Lernkompetenzen und Higher Order Thinking Skills werden dabei immer wichtiger, um Herausforderungen der Zukunft zu lösen. Eine Unterklasse dieser Fähigkeiten, die wesentlich zu Kommunikation, Kollaboration und Problemlösung beiträgt, ist die Fähigkeit, strukturiert und reflektierend zu argumentieren. Bildungseinrichtungen haben jedoch Schwierigkeiten, die für die Entwicklung dieser Fähigkeit notwendigen Rahmenbedingungen zu schaffen. In diesem Paper stellen wir unser Design Science Research Projekt vor, in dem wir ein Lerntool entwickeln, dass es ermöglicht, Studierende durch intelligentes Feedback beim Erlernen von Argumentation zu unterstützen. Wir präsentieren dazu acht Designprinzipien, abgeleitet aus aktueller Literatur und aus 21 Nutzerinterviews, sowie einen evaluierten Prototyen als eine Form der Instanziierung dieser Prinzipien. Ziel unseres Forschungsprojektes ist es, ein Lerntool zu entwickeln, das Studierenden hilft, ihre Argumentationsfähigkeit durch individuelles Feedback zu verbessern.

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    Ausbildung


    • 2018 - M.Sc., Industrial Engineering @ Karlsruhe Institute of Technology (KIT)
    • 2017 - Stanford ME310 Design Thinking Class as part of the Sugar network 
    • 2016 - B.Sc., Industrial Engineering @ Karlsruhe Institute of Technology (KIT)
    • 2015 - Bachelor thesis within a scientific exchange at the Software Institute at the Beijing Insitute of Technology (BIT), Beijing, China

    Projekte


    • 2021: SNSF Doc.Mobility Fellowship at Carnegie Mellon University: Improving Adaptive Argumentation Learning through Artificial Intelligence  
    • 2019 - 2020: HSG GFF project fund: Improving the Argumentation Skills of Students through Machine Learning  

    Awards


    • 2021 Nominee of project „ArgueLearn“ for DELINA Learntec Innovation award
    • 2021 Supervisor of the best Master’s thesis in the field of educational technology awarded by the Gesellschaft für Informatik (GermanSociety) 
    • 2021 One-year research fellowship from the Swiss National Science Foundation (SNF, Doc.Mobility) with Carnegie Mellon University
    • 2021 Doctoral Consortium at the ACM Conference on Human Factors in Computing Systems (CHI) 2021
    • 2021 Best Reviewer Award at the International Conference on Wirtschaftsinformatik 2021
    • 2021 Nominee Best Paper Award at the International Conference on Wirtschaftsinformatik 2021
    • 2020 Best Reviewer Award at the International Conference on Information Systems (ICIS) 2020
    • 2020 Best Theory Paper First Runner-Up Award at the International Conference on Information Systems (ICIS) 2020
    • 2020 Honourable Mention Award of the ACM Conference on Human Factors in Computing Systems (CHI) 2020  

    Mitgliedschaften


    • Association for Computing Machinery (ACM)
    • ACM Special Interest Group on Computer-Human Interaction (SIGCHI)
    • Association for Information Systems (AIS)
    • Association for Computational Linguistics (ACL)

    Forschungskooperationen


    • Ken Koedinger, Human-Computer Interaction Institute at Carnegie Mellon University
    • Andrew Caines, Institute for Automated Language Teaching and Assessment, Cambridge University 
    • Matthias Söllner, University of Kassel

    Weitere Informationen


    I strive to conduct interdisciplinary research in the fields of Information Systems, Computer Linguistics, and Human-Computer-Interaction to increase learning success and education across all domains. To do so, I use techniques from Artificial Intelligence such as Natural Language Processing, Transfer and Deep Learning to build AI-powered education tools such as Intelligent-Tutoring-Systems or Conversational Agents. 

    More information through: https://ai-for-education.com/