Roman Rietsche
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Dr.
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Rietsche
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Roman
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roman.rietsche@unisg.ch
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+41 71 224 33 25
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Quantum computing

2022-08-05 , Rietsche, Roman , Dremel, Christian , Bosch, Samuel , Steinacker, Léa , Meckel, Miriam , Leimeister, Jan Marco

Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems.

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Towards Designing an Adaptive Argumentation Learning Tool

2019-12 , Wambsganss, Thiemo , Rietsche, Roman

Digitalization triggers a shift in the compositions of skills and knowledge needed for students in their future work life. Hence, higher order thinking skills are becoming more important to solve future challenges. One subclass of these skills, which contributes significantly to communication, collaboration and problem-solving, is the skill of how to argue in a structured, reflective and well-formed way. However, educational organizations face difficulties in providing the boundary conditions necessary to develop this skill, due to increasing student numbers paired with financial constraints. In this short paper, we present the first steps of our design science research project on how to design an adaptive IT-tool that helps students develop their argumentation skill through formative feedback in large-scale lectures. Based on scientific learning theory and user interviews, we propose preliminary requirements and design principles for an adaptive argumentation learning tool. Furthermore, we present a first instantiation of those principles.

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A Corpus for Suggestion Mining of German Peer Feedback

2022 , Rietsche, Roman , Ritz, Eva , Janda, Julius , Pfütze, Dominik

Peer feedback in online education becomes increasingly important to meet the demand for feedback in large scale classes, such as e.g. Massive Open Online Courses (MOOCs). However, students are often not experts in how to write helpful feedback to their fellow students. In this paper, we introduce a corpus compiled from university students’ peer feedback to be able to detect suggestions on how to improve the students’ work and therefore being able to capture peer feedback helpfulness. To the best of our knowledge, this corpus is the first student peer feedback corpus in German which additionally was labelled with a new annotation scheme. The corpus consists of more than 600 written feedback (about 7,500 sentences). The utilisation of the corpus is broadly ranged from Dependency Parsing to Sentiment Analysis to Suggestion Mining, etc. We applied the latter to empirically validate the utility of the new corpus. Suggestion Mining is the extraction of sentences that contain suggestions from unstructured text. In this paper, we present a new annotation scheme to label sentences for Suggestion Mining. Two independent annotators labelled the corpus and achieved an inter-annotator agreement of 0.71. With the help of an expert arbitrator a gold standard was created. An automatic classification using BERT achieved an accuracy of 75.3%.

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Does Real-Time Feedback Matter? A Simulation Study to Link Individual and Organizational Performance

2021 , Rietsche, Roman , Aier, Stephan , Michael, Rivera

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Individualisierung in der beruflichen Bildung durch Hybrid Intelligence. Potentiale und Grenzen

2021 , Söllner, Matthias , Janson, Andreas , Rietsche, Roman , Thiel de Gafenco, Marian , Seufert, Sabine , Guggemos, Josef , Ifenthaler, Dirk , Ertl, Hubert , Seifried, Jürgen

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Artificial Socialization? How Artificial Intelligence Applications Can Shape A New Era of Employee Onboarding Practices

2023-01-06 , Ritz, Eva , Fabio, Donisi , Elshan, Edona , Rietsche, Roman

Onboarding has always emphasized personal contact with new employees. Excellent onboarding can extend employee retention and improve loyalty. Even in a physical setting, the onboarding process is demanding for both the newcomer and the onboarding organization. Remote work, in contrast, has made this process even more challenging by forcing a rapid shift from offline to online onboarding practices. Organizations are adopting new technologies like artificial intelligence (AI) to support work processes, such as hiring processes or innovation facilitation, which could shape a new era of work practices. However, it has not been studied how AI applications can or should support onboarding. Therefore, our research conducts a literature review on current onboarding practices and uses expert interviews to evaluate AI's potential and pitfalls for each action. We contribute to the literature by presenting a holistic picture of onboarding practices and assessing potential application areas of AI in the onboarding process.

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Unleashing Process Mining for Education: Designing an IT-Tool for Students to Self-Monitor their Personal Learning Paths

2022-02-23 , Ritz, Eva , Wambsganss, Thiemo , Rietsche, Roman , Schmitt, Anuschka , Oeste-Reiß, Sarah , Leimeister, Jan Marco

The ability of students to self-monitor their learning paths is in demand as never before due to the recent rise of online education formats, which entails less interaction with lecturers. Recent advantages in educational process mining (EPM) offer new opportunities to monitor students’ learning paths by processing log data captured by technology-mediated learning environments. However, current literature falls short on providing user-centered design principles for IT-tools which can monitor learning paths using EPM. Hence, in this paper, we examine how to design a self-monitoring tool that supports students to evaluate their learning paths. Based on theoretical insights of 66 papers and nine user interviews, we propose seven design principles for an IT-tool which facilitates self-monitoring for students based on EPM. Further, we evaluate the design principles with seven potential users. Our results demonstrate a promising approach to help students improve their self-efficacy in their individual learning process using EPM.

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A pedagogical perspective on Big Data: A conceptual model for digital learning support. Technology, Knowledge and Learning.

2019 , Seufert, Sabine , Meier, Christoph , Söllner, Matthias , Rietsche, Roman

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The Specificity and Helpfulness of Peer-to-Peer Feedback in Higher Education

2022-07-15 , Rietsche, Roman , Caines, Andrew , Schramm, Cornelius , Pfütze, Dominik , Buttery, Paula

With the growth of online learning through MOOCs and other educational applications, it has become increasingly difficult for course providers to offer personalized feedback to students. Therefore asking students to provide feedback to each other has become one way to support learning. This peer-to-peer feedback has become increasingly important whether in MOOCs to provide feedback to thousands of students or in large-scale classes at universities. One of the challenges when allowing peer-to-peer feedback is that the feedback should be perceived as helpful, and an import factor determining helpfulness is how specific the feedback is. However, in classes including thousands of students, instructors do not have the resources to check the specificity of every piece of feedback between students. Therefore, we present an automatic classification model to measure sentence specificity in written feedback. The model was trained and tested on student feedback texts written in German where sentences have been labelled as general or specific. We find that we can automatically classify the sentences with an accuracy of 76.7% using a conventional feature-based approach, whereas transfer learning with BERT for German gives a classification accuracy of 81.1%. However, the feature-based approach comes with lower computational costs and preserves human interpretability of the coefficients. In addition we show that specificity of sentences in feedback texts has a weak positive correlation with perceptions of helpfulness. This indicates that specificity is one of the ingredients of good feedback, and invites further investigation.

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Is more always better? Simulating Feedback Exchange in Organizations

2021 , Fuchs, Sacha , Rietsche, Roman , Aier, Stephan , Rivera, Michael

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