Michael Burkhard
Last Name
Burkhard
First name
Michael
Email
michael.burkhard@unisg.ch
Phone
+41 71 224 7574
15 results
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Item type:Publication, The Textbook Learns to Talk: How to Design Chatbot-Mediated Learning to Foster Collaborative High-Order Learning?(Association for the Advancement of Computing in Education (AACE), 2021-11-09); ; ; Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fostering Students' Academic Writing Skills: Feedback Model for an AI-enabled Support Environment.(Association for the Advancement of Computing in Education (AACE), 2021-11-09); ; Due to recent advances in natural language processing (NLP), a new generation of digital learning support systems is emerging, which make it possible to analyse the writing quality of texts offering individual, linguistic feedback to writers through various kinds of automated text evaluation. These intelligent tutoring systems (ITS) have to be integrated into existing teaching practices alongside traditional feedback providers (e.g., tutor, peer students). Therefore, this paper explores how academic writing skills of students could be fostered by providing different types of feedback from a tutor, peer students and an ITS. It proposes a feedback model for academic writing in an AI-enabled learning support environment and illustrates the importance of the different feedback providers in an academic writing use case. Through this, the paper aims to contribute to a better understanding of the changing nature of how students' academic writing skills can be fostered in the age of artificial intelligence.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Usefulness of Open and Proprietary LLMs in Argumentative Writing Support(2024-07-02); ; ; ; In this article, we present the results of an exploratory study conducted with our self-developed tool Artist. The goal of the tool is to give formative feedback to develop students' argumentation skills. We compare the feedback that two different LLMs, an open-sourced one by META and one of OpenAI's fully proprietary ones, give to students' argumentative writing. We find that, overall, students find the feedback provided by both LLMs helpful (7.51 vs. 7.65 on a scale from 1 to 10), and they rate the quality of the feedback as good to very good. We take this as a very encouraging provisional result that invites larger and more extensive studies on the topic.Type:conference paperScopus© Citations 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How to Deal with AI-Powered Writing Tools in Academic Writing: A Stakeholder Analysis(2023)Due to the advances of artificial intelligence (AI) and natural language processing, new AI-powered writing tools have emerged. They can be used by students among other things for text translation, to improve spelling or to generate new texts. In academic writing, AI-powered writing tools are posing challenges but also opportunities for teaching and learning. It is an open question in which way to sensibly deal with these tools. To address the issue, this paper investigates, what interests different stakeholders (students, lecturers, university administration) pursue in relation to AI-powered writing tools. Building on this, tensions between different stakeholders are identified and (teaching) strategies proposed to deal with these tensions. To discuss the findings in light of recent developments around ChatGPT, semi-structured expert interviews were conducted in April 2023 with five academic writing lecturers at the University of St.Gallen. The results suggest that as writing tools become more and more powerful, the need for strategies to ensure their reasonable and transparent use also increases.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Computer Supported Argumentation Learning: Design of a Learning Scenario in Academic Writing by Means of a Conjecture Map(2023); ; ; ; Patcharin PanjabureeIn academic writing, the competency to argue is important. However, first-year students often have difficulties to construct good arguments. Advances in natural language processing (NLP) have made it possible to better analyze the writing quality of texts. New tools have emerged which can give students individual feedback on their texts and the structure of their arguments. While the use of these argumentation learning support tools can help create better texts, using them in an academic context also carries risks. Learning scenarios are needed that promote argumentation competency using argumentation tools while also making students aware of their limitations. To address this issue, this paper investigates how a learning design with an argumentation learning support tool can be developed to increase the argumentation competency of first-year students. The conjecture-mapping technique was used, to visualize our assumptions and illustrate the developed learning design. As part of a fi rst design cycle, the learning design was tested with 80 students in seven academic writing classes at the University of St.Gallen in Switzerland. Preliminary findings suggest that the learning design might be helpful to improve the argumentation competency as well as the data-literacy of students (in relation to argumentation tools). However, further research is necessary to confirm or reject our hypotheses.Type:conference paperJournal:Proceedings of the 15th International Conference on Computer Supported EducationVolume:1Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Student Perceptions of AI-Powered Writing Tools: Towards Individualized Teaching Strategies(2022-11-10)Due to the advances of artificial intelligence (AI) and natural language processing, new kinds of Internet-based writing tools have emerged. Among other things, these AI-powered writing tools can be used by students for text translation, to improve spelling or for rewriting and summarizing texts. On the one hand, they can provide detailed recommendations for the adaptation of text elements within seconds. On the other hand, they also produce inconsistencies and errors, that students might not be aware of. How to deal with these tools in an educational context is a difficult question. Since writing tools are usually used unsupervised and without further instructions, students may need guidance from the teacher in interacting with those tools, to prevent the risk of misapplication. To better understand this underlying issue, the paper at hand uses survey data of 365 freshmen students to describe and analyze student perceptions of AI-powered writing tools. Regarding AI-powered writing tools, different student types were identified by using the k-means clustering method. The results suggest that students have different attitudes towards AI-powered writing tools. Some students may use them in an unreflective, non-sceptical way, which can lead to (un)voluntary plagiarism. Other students may not use writing tools at all, partly because they are sceptic, but also because they may lack meaningful learning strategies in general (course repeaters). Depending on the different student types, individualized teaching strategies might be helpful to promote or urge caution in the use of these tools.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Educational Chatbots for Collaborative Learing: Results of a Design Experiment in a Middle School(2022-11-09); ; ; Educational chatbots promise many benefits for teaching and learning. Although chatbot use cases in this research field are rapidly growing, most studies focus on individual users rather than on collaborative group settings. To address this issue, this paper investigates how chatbot-mediated learning can be designed to foster middle school students in team-based assignments. Using an educational design research approach, quality indicators of educational chatbots were derived from the literature, which served as a guideline for the development of the chatbot Tubo (meaning tutoring bot). Tubo is part of a web-based team learning environment in which students can chat with each other and collaboratively work on their group assignments. As a team member and tutor of each group, Tubo guides the students through the learning journey by different scaffolding elements and helps with content-related questions the students have. As part of a first design cycle, the chatbot application was tested with a school class of a technical vocational school in Switzerland. The received feedback suggests that the approach of team-based learning with chatbots has a lot of potential from the students' and teachers' point of view. However, the role distribution of the individual group members may have to be further specified to address the different needs of autonomous as well as more control-oriented students.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Micro- and Macro-Level Features of NLP-Based Writing Tools in Higher Education(2022-12-02); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Social Robots as Teaching Assistance System in Higher Education: Conceptual Framework for the Development of Use CasesThis paper provides an overview of the current state of research on social robots in higher education and the existing frameworks to categorize and develop social robot applications. Based on the existing work, we present our own framework to develop use cases for social robots in the education sector. Our framework is based on a heuristic and symbiotic design approach that serves as a guideline for developing use cases and views human-robot interaction as two complementary and mutually reinforcing roles. We illustrate our framework by means of a use case that we have conducted in 2019 during the initial lecture of the large-scale course ‘Introduction to academic writing’.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hochschulbildung: KI-basiertes Forschen und Schreiben(Schäffer-Poeschel, 2024); ; ; ; Mit der rasanten Verbreitung von Anwendungen generativer KI wie z.B. ChatGPT, Midjour-ney oder GitHub Co-Pilot in den letzten Monaten beginnen sich Arbeitsroutinen breitflächig zu verändern: Studierende erzeugen Textpassagen mit ChatGPT; Illustratoren erzeugen Vi-sualisierungen mit Midjourney; Software-Entwickler lassen sich Code-Passagen von GitHub Co-Pilot erstellen, überprüfen oder in andere Programmiersprachen übersetzen. Damit rückt die Frage in den Vordergrund, wie denn eine zielführende, produktive, und für Menschen attraktive Zusammenarbeit mit diesen „intelligenten“ Assistenzsystemen erreicht werden kann.Type:book section