Translated abstracts

Author: Cbblr

_bibliography/papers.bib

@@ -49,7 +49,11 @@ @unpublished{genath2021wsc2
   year      = {2021},
   additional_info = {Winter Simulation Conference, Phoenix, AZ, USA, Vortrag und Poster},
   abstract = {
-    In contrast to classical simulation studies, the method of knowledge discovery in simulation data uses a simulation model as a data generator (data farming). Subsequently using data mining methods, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered. So far, however, there is a lack of support and automatization tools for non-experts or novices in knowledge discovery in simulation data, which leads to a more difficult use in industrial applications and prevents a broader utilization. In this work, we propose a concept which provides an approach for automating and supporting knowledge discovery in simulation data.
+    In contrast to classical simulation studies, the method of knowledge discovery in simulation data uses a simulation model as a data generator (data farming). 
+    Subsequently using data mining methods, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered.
+    So far, however, there is a lack of support and automatization tools for non-experts or novices in knowledge discovery in simulation data, 
+    which leads to a more difficult use in industrial applications and prevents a broader utilization. 
+    In this work, we propose a concept which provides an approach for automating and supporting knowledge discovery in simulation data.
   }
 }
 
@@ -62,15 +66,15 @@ @article{genath2022zfwf
   year    = {2022},
   bibtex_show={true},
   abstract = {
-    Simulation als Methode der Digitalen Fabrik ist seit langem etabliert
-    zur Unterstützung der Planung von Produktions- und Logistiksystemen.
-    In Ergänzung zu bisher vorherrschenden Simulationsstudien
-    wird bei der hier vorgestellten Methode der Wissensentdeckung in
-    Simulationsdaten ein Simulationsmodell als Datengenerator verwendet.
-    Dadurch können mittels Data-Mining- und Visual-Analytics-Methoden
-    versteckte und potenziell nützliche Ursache-Wirkungs-Beziehungen
-    in den generierten Daten aufgedeckt werden. Bislang fehlte es
-    jedoch an integrierten Softwarelösungen für die Praxis.
+    Simulation as a method of digital factory has long been established
+    to support the planning of production and logistics systems.
+    In addition to the simulation studies that have prevailed to date,
+    the method of knowledge discovery in simulation data presented here
+    uses a simulation model as a data generator.
+    This allows data mining and visual analytics methods to be used
+    to uncover hidden and potentially useful cause-and-effect relationships
+    in the generated data. Until now, however, there has been a lack of
+    integrated software solutions for practical use.
   }
 }
 
@@ -176,7 +180,16 @@ @unpublished{kreuzberger2024turn
   year      = {2024},
   additional_info = {TURN Conference, Berlin, Vortrag und Poster},
   abstract = {
-    Im Projekt examING – Digitalisierung des kompetenzorientierten Prüfens für ingenieurwissenschaftliche Bachelorstudiengänge wird untersucht, wie Feedback zu Programmieraufgaben durch generative KI verbessert werden kann. Ziel ist es, Rückmeldungen individueller, differenzierter, konstruktiver und sprachlich variabler zu gestalten. Der entwickelte Ansatz motiviert Lernende durch praxisnahe Aufgaben, umfangreiche Übungsmöglichkeiten und integrierte Programmierwerkzeuge. Zur Umsetzung wurde ChatGPT über eine API in das webbasierte Autograder-System CodeOcean eingebunden. Mithilfe strukturierter Prompts wird sachorientiertes Feedback generiert, das gezielt auf die eingereichten Lösungen eingeht. Erste Ergebnisse zeigen, dass sich durch die KI-Integration qualitativ hochwertiges, anpassbares Feedback entlang definierter Dimensionen erzeugen lässt. Die nächsten Schritte umfassen die nutzerfreundliche Darstellung des Feedbacks, eine Evaluation der Akzeptanz sowie die Erweiterung auf weitere Anwendungsfälle wie Kommentaranfragen, Hinweiserstellungen und Aufgabenklärung. Das Projekt wird von der Stiftung Innovation in der Hochschullehre im Rahmen des Bund-Länder-Programms „Hochschule durch Digitalisierung stärken“ gefördert.
+    The examING project – digitization of competency-based testing for bachelor's degree programs in engineering – 
+    is investigating how feedback on programming tasks can be improved through generative AI. 
+    The aim is to make feedback more individualized, differentiated, constructive, and linguistically variable. 
+    The approach developed motivates learners through practical tasks, extensive practice opportunities, 
+    and integrated programming tools. To implement this, ChatGPT was integrated into the web-based autograder system CodeOcean via an API. 
+    Structured prompts are used to generate factual feedback that specifically addresses the submitted solutions. 
+    Initial results show that AI integration can be used to generate high-quality, customizable feedback along defined dimensions. 
+    The next steps include the user-friendly presentation of feedback, an evaluation of acceptance, 
+    and expansion to other use cases such as comment requests, note creation, and task clarification. 
+    The project is funded by the Foundation for Innovation in Higher Education as part of the federal-state program “Strengthening Universities through Digitization.”
   }
 }
 
@@ -189,6 +202,16 @@ @unpublished{genath2025digitell
   year      = {2025},
   additional_info = {DigiTeLL – Digital Teaching and Learning Lab, Frankfurt, Vortrag und Poster},
   abstract = {
-    As part of the redesign of a course on operational digitization, the desire was expressed to also digitize teaching itself to a greater extent. The aim is to give students a basic understanding of programming so that they can better understand digital possibilities. The autograder CodeOcean was used as a suitable tool—a web-based open-source platform with a development environment, collaboration functions, and LMS integration. In the “examING” project, funded by the Foundation for Innovation in Higher Education, new digital teaching and examination formats for Python training were developed and tested with CodeOcean. So far, around 270 students have participated in the courses, and around 120 have taken digital exams. Feedback and observations accompanied the implementation. Automated assessment by CodeOcean facilitates individual learning and promotes targeted skills development. The article reflects on the experiences, identifies challenges, and outlines further developments, in particular the planned integration of generative AI such as ChatGPT to further improve feedback for students.
+    As part of the redesign of a course on operational digitization, the desire was expressed to also digitize teaching itself to a greater extent. 
+    The aim is to give students a basic understanding of programming so that they can better understand digital possibilities. 
+    The autograder CodeOcean was used as a suitable tool—a web-based open-source platform with a development environment, 
+    collaboration functions, and LMS integration. 
+    In the “examING” project, funded by the Foundation for Innovation in Higher Education, 
+    new digital teaching and examination formats for Python training were developed and tested with CodeOcean. 
+    So far, around 270 students have participated in the courses, and around 120 have taken digital exams. 
+    Feedback and observations accompanied the implementation. 
+    Automated assessment by CodeOcean facilitates individual learning and promotes targeted skills development. 
+    The article reflects on the experiences, identifies challenges, and outlines further developments, 
+    in particular the planned integration of generative AI such as ChatGPT to further improve feedback for students.
   }
 }