Andreas Janson
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Janson
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Andreas
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+41 71 224 2592
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  • Publication
    Improving Students Argumentation Learning with Adaptive Self-Evaluation Nudging
    ( 2022-11-11)
    Wambsganss, Thiemo
    ;
    Janson, Andreas
    ;
    Käser, Tanja
    ;
    Leimeister, Jan Marco
    Recent advantages from computational linguists can be leveraged to nudge students with adaptive self evaluation based on their argumentation skill level. To investigate how individual argumentation self evaluation will help students write more convincing texts, we designed an intelligent argumentation writing support system called ArgumentFeedback based on nudging theory and evaluated it in a series of three qualitative and quaxntitative studies with a total of 83 students. We found that students who received a self-evaluation nudge wrote more convincing texts with a better quality of formal and perceived argumentation compared to the control group. The measured self-efficacy and the technology acceptance provide promising results for embedding adaptive argumentation writing support tools in combination with digital nudging in traditional learning settings to foster self-regulated learning. Our results indicate that the design of nudging-based learning applications for self-regulated learning combined with computational methods for argumentation self-evaluation has a beneficial use to foster better writing skills of students.
    Type: journal article
    Journal: Proceedings of the ACM on Human-Computer Interaction (PACMHCI)
    Volume: 6
    Issue: CSCW2
    DOI: 10.1145/3555633
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108081
    Scopus© Citations 8
  • Publication
    Enhancing argumentative writing with automated feedback and social comparison nudging
    (Elsevier, 2022-12)
    Wambsganss, Thiemo
    ;
    Janson, Andreas
    ;
    Leimeister, Jan Marco
    The advantages offered by natural language processing (NLP) and machine learning enable students to receive automated feedback on their argumentation skills, independent of educator, time, and location. Although there is a growing amount of literature on formative argumentation feedback, empirical evidence on the effects of adaptive feedback mechanisms and novel NLP approaches to enhance argumentative writing remains scarce. To help fill this gap, the aim of the present study is to investigate whether automated feedback and social comparison nudging enable students to internalize and improve logical argumentation writing abilities in an undergraduate business course. We conducted a mixed-methods study to investigate the impact of argumentative writing on 71 students in a field experiment. Students in treatment group 1 completed their assignment while receiving automated feedback, whereas students in treatment group 2 completed the same assignment while receiving automated feedback with a social comparison nudge that indicated how other students performed on the same assignment. Students in the control group received generalized feedback based on rules of syntax. We found that participants who received automated argumentation feedback with a social comparison nudge wrote more convincing texts with higher-quality argumentation compared to the two benchmark groups (p < 0.05). The measured self-efficacy, perceived ease of use, and qualitative data provide valuable insights that help explain this effect. The results suggest that embedding automated feedback in combination with social comparison nudges enables students to increase their argumentative writing skills by triggering psychological processes. Receiving only automated feedback in the form of in-text argumentative highlighting without any further guidance appears not to significantly influence students’ writing abilities when compared to syntactic feedback.
    Type: journal article
    Journal: Computers & Education
    Volume: 191
    Issue: 104644
    URL: https://www.sciencedirect.com/science/article/pii/S0360131522002159
    DOI: 10.1016/j.compedu.2022.104644
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108033
    Scopus© Citations 20
  • Publication
    Designing for Conversational System Trustworthiness: The Impact of Model Transparency on Trust and Task Performance
    ( 2022-06-24)
    Schmitt, Anuschka
    ;
    Wambsganss, Thiemo
    ;
    Janson, Andreas
    Designing for system trustworthiness promises to address challenges of opaqueness and uncertainty introduced through Machine Learning (ML)-based systems by allowing users to understand and interpret systems’ underlying working mechanisms. However, empirical exploration of trustworthiness measures and their effectiveness is scarce and inconclusive. We investigated how varying model confidence (70% versus 90%) and making confidence levels transparent to the user (explanatory statement versus no explanatory statement) may influence perceptions of trust and performance in an information retrieval task assisted by a conversational system. In a field experiment with 104 users, our findings indicate that neither model confidence nor transparency seem to impact trust in the conversational system. However, users’ task performance is positively influenced by both transparency and trust in the system. While this study considers the complex interplay of system trustworthiness, trust, and subsequent behavioral outcomes, our results call into question the relation between system trustworthiness and user trust.
    Type: conference paper
    Journal: European Conference on Information Systems (ECIS)
    URL: https://pubs.wi-kassel.de/wp-content/uploads/2022/05/JML_874.pdf
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108582
  • Publication
    Towards a Trust Reliance Paradox? Exploring the Gap Between Perceived Trust in and Reliance on Algorithmic Advice
    ( 2021)
    Schmitt, Anuschka
    ;
    Wambsganss, Thiemo
    ;
    Söllner, Matthias
    ;
    Janson, Andreas
    Beyond AI-based systems’ potential to augment decision-making, reduce organizational resources, and counter human biases, unintended consequences of such systems have been largely neglected so far. Researchers are undecided on whether erroneous advice acts as an impediment to system use or is blindly relied upon. As part of an experimental study, we turn towards the impact of incorrect system advice and how to design for failure-prone AI. In an experiment with 156 subjects we find that, although incorrect algorithmic advice is trusted less, users adapt their answers to a system’s incorrect recommendations. While transparency on a system’s accuracy levels fosters trust and reliance in the context of incorrect advice, an opposite effect is found for users exposed to correct advice. Our findings point towards a paradoxical gap between stated trust and actual behavior. Furthermore, transparency mechanisms should be deployed with caution as their effectiveness is intertwined with system performance.
    Type: conference paper
    Journal: International Conference on Information Systems (ICIS)
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/111308