290.
Welk, Josefine; Roschke, Christian; Breck, Dominik; Schlosser, Max; Ritter, Marc
Evaluierung einer gamifizierten Lern-App: Usability, User Experience und Lerneffekt in der Hochschulbildung Proceedings Article
In: 23. Fachtagung Bildungstechnologien (DELFI 2025), 2025.
@inproceedings{welkEvaluierungGamifiziertenLernApp2025,
title = {Evaluierung einer gamifizierten Lern-App: Usability, User Experience und Lerneffekt in der Hochschulbildung},
author = {Josefine Welk and Christian Roschke and Dominik Breck and Max Schlosser and Marc Ritter},
doi = {10.18420/delfi2025_15},
year = {2025},
date = {2025-01-01},
booktitle = {23. Fachtagung Bildungstechnologien (DELFI 2025)},
abstract = {Gamification-Strategien können motivationssteigernd wirken. Die App StudiSQ nutzt diesen Ansatz, indem sie Lehrinhalte als Quiz vermittelt und Gamification-Elemente, wie Ranglisten und Abzeichen, zur Förderung der Motivation einsetzt. Im Rahmen einer Evaluation wurden ca. 60 Studierende hinsichtlich Usability und User Experience befragt; der Lerneffekt wurde mittels zweier Tests evaluiert. Zur Vorbereitung auf den zweiten Test nutzte eine Gruppe StudiSQ, die andere nicht. Die Usability (65,9/100) und User Experience (0,61/-3 bis 3) wurden durchschnittlich bis leicht positiv bewertet. Im Durchschnitt verschlechterten sich die Testergebnisse aller Teilnehmenden; Studierende, die StudiSQ nutzten, schnitten etwas besser ab (-0,36 vs. -0,63 Punkte). Im Rahmen der Evaluation konnte kein statistisch signifikanter Unterschied zwischen den beiden Testgruppen festgestellt werden. Entsprechende Maßnahmen zur Konzeptionierung und Durchführung von Folgeuntersuchungen wurden diskutiert. Um eine Nutzung durch Studierende und Lehrkräfte zu fördern, sind außerdem Fehlerbehebungen sowie die Verbesserung der Usability notwendig. Dennoch würden ca. 90 % der befragten Studierenden die App wiederverwenden.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Gamification-Strategien können motivationssteigernd wirken. Die App StudiSQ nutzt diesen Ansatz, indem sie Lehrinhalte als Quiz vermittelt und Gamification-Elemente, wie Ranglisten und Abzeichen, zur Förderung der Motivation einsetzt. Im Rahmen einer Evaluation wurden ca. 60 Studierende hinsichtlich Usability und User Experience befragt; der Lerneffekt wurde mittels zweier Tests evaluiert. Zur Vorbereitung auf den zweiten Test nutzte eine Gruppe StudiSQ, die andere nicht. Die Usability (65,9/100) und User Experience (0,61/-3 bis 3) wurden durchschnittlich bis leicht positiv bewertet. Im Durchschnitt verschlechterten sich die Testergebnisse aller Teilnehmenden; Studierende, die StudiSQ nutzten, schnitten etwas besser ab (-0,36 vs. -0,63 Punkte). Im Rahmen der Evaluation konnte kein statistisch signifikanter Unterschied zwischen den beiden Testgruppen festgestellt werden. Entsprechende Maßnahmen zur Konzeptionierung und Durchführung von Folgeuntersuchungen wurden diskutiert. Um eine Nutzung durch Studierende und Lehrkräfte zu fördern, sind außerdem Fehlerbehebungen sowie die Verbesserung der Usability notwendig. Dennoch würden ca. 90 % der befragten Studierenden die App wiederverwenden.
289.
Welk, Josefine; Roschke, Christian; Breck, Dominik; Schlosser, Max; Ritter, Marc
Evaluating a Gamified Learning App: Usability, User Experience and Learning in Higher Education Proceedings Article
In: Vol. 2 No. 1 (2025): The Proceedings of the International Conference on Teaching, Learning and Education, 2025.
@inproceedings{welkEvaluatingGamifiedLearning2025,
title = {Evaluating a Gamified Learning App: Usability, User Experience and Learning in Higher Education},
author = {Josefine Welk and Christian Roschke and Dominik Breck and Max Schlosser and Marc Ritter},
doi = {10.33422/ictle.v2i1.1111},
year = {2025},
date = {2025-01-01},
booktitle = {Vol. 2 No. 1 (2025): The Proceedings of the International Conference on Teaching, Learning and Education},
abstract = {<jats:p>A variety of methods can be used to increase student motivation, for example different types of gamification strategies like leaderboards, badges or experience points. Among such methods, the StudiSQ app incorporates various gamification elements. In the app, students can independently repeat content from courses in the form of quizzes. Gamification elements such as badges, ranking lists and duels are used to increase motivation. Approximately 60 students took part in an evaluation of the app with a focus on usability, user experience and learning effect. The latter was measured using two tests, with some of the participants using StudiSQ to prepare for the second test and others using conventional learning methods. In terms of usability (65.9/100) and user experience (0.61/-3 to 3), results were medium to slightly positive. On average, the test results of all participants deteriorated; students who used StudiSQ performed slightly better (-0.36 vs. -0.63 points). No statistically significant difference was found between the two test groups during the evaluation. Appropriate measures for the design and implementation of follow-up studies were discussed. To promote use by students and teachers, it is also necessary to fix bugs and improve usability. Nevertheless, around 90% of the students would use the app again.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
<jats:p>A variety of methods can be used to increase student motivation, for example different types of gamification strategies like leaderboards, badges or experience points. Among such methods, the StudiSQ app incorporates various gamification elements. In the app, students can independently repeat content from courses in the form of quizzes. Gamification elements such as badges, ranking lists and duels are used to increase motivation. Approximately 60 students took part in an evaluation of the app with a focus on usability, user experience and learning effect. The latter was measured using two tests, with some of the participants using StudiSQ to prepare for the second test and others using conventional learning methods. In terms of usability (65.9/100) and user experience (0.61/-3 to 3), results were medium to slightly positive. On average, the test results of all participants deteriorated; students who used StudiSQ performed slightly better (-0.36 vs. -0.63 points). No statistically significant difference was found between the two test groups during the evaluation. Appropriate measures for the design and implementation of follow-up studies were discussed. To promote use by students and teachers, it is also necessary to fix bugs and improve usability. Nevertheless, around 90% of the students would use the app again.</jats:p>
288.
Platte, Benny; Kowerko, Danny; Langner, Holger; Skuras, Jan Anastassis; Ritter, Marc; Roschke, Christian
"Synthetic-Seed-Saturation“ Concept: Overcome Nonresponse-Bias in Retrospective Medical Studies Artikel
In: 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), S. 534-537, 2024.
@article{Platte2024,
title = {"Synthetic-Seed-Saturation“ Concept: Overcome Nonresponse-Bias in Retrospective Medical Studies},
author = {Benny Platte and Danny Kowerko and Holger Langner and Jan Anastassis Skuras and Marc Ritter and Christian Roschke},
doi = {10.1109/ICHI61247.2024.00122},
year = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
journal = {2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)},
pages = {534-537},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
287.
Platte, Benny
Forschungsunterstützungen und -hemmnisse niedergelassener Ärzte im regulatorischen Umfeld Artikel
In: Digitale Forschung in der ärztlichen Praxis, 2024.
@article{nokey,
title = {Forschungsunterstützungen und -hemmnisse niedergelassener Ärzte im regulatorischen Umfeld},
author = {Benny Platte},
year = {2024},
date = {2024-03-01},
journal = {Digitale Forschung in der ärztlichen Praxis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
286.
Kothe, Helena Sophie; Apfelstädt, Madlien; Plekat, Marie-Luise; Gründel, Janine; Müller, Felix Jan; Fischer, Luca; Helmer, Finn Johannes; Heinzig, Manuel; Kühn, Alexander Thomas; Vodel, Matthias; Herrmann-Geppert, Iris; Roschke, Christian; Ritter, Marc
Serious Games as an Educational Strategy in Chemistry Classes: Case Study of a Mobile Application for learning Chemistry in School Proceedings Article
In: 2024.
@inproceedings{kotheSeriousGamesEducational2024,
title = {Serious Games as an Educational Strategy in Chemistry Classes: Case Study of a Mobile Application for learning Chemistry in School},
author = {Helena Sophie Kothe and Madlien Apfelstädt and Marie-Luise Plekat and Janine Gründel and Felix Jan Müller and Luca Fischer and Finn Johannes Helmer and Manuel Heinzig and Alexander Thomas Kühn and Matthias Vodel and Iris Herrmann-Geppert and Christian Roschke and Marc Ritter},
doi = {10.1109/segah61285.2024.10639533},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
285.
Thiele, Jeannine; Thiele, Elisa; Roschke, Christian; Heinzig, Manuel; Ritter, Marc
Towards Semi-Automated Game Analytics: An Exploratory Study on Deep Learning-Based Image Classification of Characters in Auto Battler Games Proceedings Article
In: Lecture Notes in Computer Science, 2024.
@inproceedings{thieleSemiAutomatedGameAnalytics2024,
title = {Towards Semi-Automated Game Analytics: An Exploratory Study on Deep Learning-Based Image Classification of Characters in Auto Battler Games},
author = {Jeannine Thiele and Elisa Thiele and Christian Roschke and Manuel Heinzig and Marc Ritter},
doi = {10.1007/978-3-031-60692-2_20},
year = {2024},
date = {2024-01-01},
booktitle = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
284.
Wittrin, Ruben; Platte, Benny; Roschke, Christian; Ritter, Marc; Eibl, Maximilian; Steiner, Carolin Isabel; Tolkmitt, Volker
In: IEEE Transactions on Learning Technologies, 2024.
@article{wittrinGameEffectComparison2024,
title = {The Game Effect: Comparison of Game and Nongame Learning Environments Using the Example of “Arctic Economy”},
author = {Ruben Wittrin and Benny Platte and Christian Roschke and Marc Ritter and Maximilian Eibl and Carolin Isabel Steiner and Volker Tolkmitt},
doi = {10.1109/tlt.2023.3274747},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Learning Technologies},
abstract = {Virtual environments open up far-reaching possibilities with respect to knowledge impartation. Nevertheless, they have the potential to negatively influence learning behavior. As a possible positive determinant, especially in the digital context, the moment “game” can be listed. Accordingly, previous studies prove an overall positive influence of serious games on learning success and motivation. However, the current state of research only allows for careful and few conclusions in terms of a nuanced differentiation of this influence. Thus, this study differentiates on a deeper level with regard to different parameters of learning success and motivation. The aim of the study is to quantify and evaluate a possible influence of the factor “game” with regard to these parameters. Two versions of the modular software environment Arctic Economy , a game and a nongame version, served as the basis for evaluation. Both versions were compared in a field experiment, with randomized group formation ( N = 97) and repeated measures. The data analysis showed the game group having a tendency to demonstrate better learning performance. In the differentiated analysis, a significant effect can be proven: subjects in the game group were able to remember facts more easily than subjects in the nongame group (group difference retention rate x¯¯¯= 17%). In addition, participants showed an average of 46% higher motivation and were significantly more capable of linking the application's content with reality. The identified “game effect” can therefore be classified as highly significant in the context of this study.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Virtual environments open up far-reaching possibilities with respect to knowledge impartation. Nevertheless, they have the potential to negatively influence learning behavior. As a possible positive determinant, especially in the digital context, the moment “game” can be listed. Accordingly, previous studies prove an overall positive influence of serious games on learning success and motivation. However, the current state of research only allows for careful and few conclusions in terms of a nuanced differentiation of this influence. Thus, this study differentiates on a deeper level with regard to different parameters of learning success and motivation. The aim of the study is to quantify and evaluate a possible influence of the factor “game” with regard to these parameters. Two versions of the modular software environment Arctic Economy , a game and a nongame version, served as the basis for evaluation. Both versions were compared in a field experiment, with randomized group formation ( N = 97) and repeated measures. The data analysis showed the game group having a tendency to demonstrate better learning performance. In the differentiated analysis, a significant effect can be proven: subjects in the game group were able to remember facts more easily than subjects in the nongame group (group difference retention rate x¯¯¯= 17%). In addition, participants showed an average of 46% higher motivation and were significantly more capable of linking the application's content with reality. The identified “game effect” can therefore be classified as highly significant in the context of this study.
283.
Baumgart, Matthias
Exploration maschineller Verfahren zur Entwicklung eines methodischen Frameworks zur Evaluierung wissenschaftlicher Texte im Forschungsmanagement Promotionsarbeit
2024.
@phdthesis{baumgart_exploration_2024,
title = {Exploration maschineller Verfahren zur Entwicklung eines methodischen Frameworks zur Evaluierung wissenschaftlicher Texte im Forschungsmanagement},
author = {Matthias Baumgart},
url = {doi.org/10.51382/978-3-96100-203-0},
year = {2024},
date = {2024-01-01},
abstract = {Die Komplexität des Forschungsmanagements an Universitäten und Hochschulen für Angewandte Wissenschaften hat in den letzten Jahren zugenommen, sowohl auf Seiten der Wissenschaftler als auch auf administrativer Ebene. Insbesondere die Texterstellung und -verarbeitung für Forschungsanträge, Publikationen und andere wissenschaftliche Dokumente erfordern erheblichen Aufwand. Gleichzeitig existieren Methoden und Technologien in den Bereichen Information Retrieval, Maschinelles Lernen und Semantischer Technologien, die für die Analyse und Bewertung dieser Texte geeignet sind. Diese Arbeit zielt darauf ab, Aufwände im Lebenszyklus von öffentlich geförderten Forschungsprojekten zu optimieren. Sie identifiziert aktuelle Entwicklungen und Technologien, um Kriterien für eine Gesamtarchitektur abzuleiten, die wissenschaftliche Texte qualitativ annotiert, trainiert und evaluiert. Das resultierende Framework namens FELIX dient als prototypisches System für die computergestützte Assistenz zur Evaluation wissenschaftlicher Texte. Datenkorpora aus Forschungsanträgen und Publikationen wurden für explorative Experimente verwendet, die u. a. auf Methoden des Maschinellen Lernens basieren. FELIX ermöglicht die Analyse von Texten und Metadaten, die Klassifizierung nach definierten Kriterien und die Vorhersage der Bewilligung von Forschungsanträgen. Die Konzeption und Evaluierung von FELIX führte zu wissenschaftlichen und praktischen Implikationen zur Optimierung des Forschungsmanagements.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Die Komplexität des Forschungsmanagements an Universitäten und Hochschulen für Angewandte Wissenschaften hat in den letzten Jahren zugenommen, sowohl auf Seiten der Wissenschaftler als auch auf administrativer Ebene. Insbesondere die Texterstellung und -verarbeitung für Forschungsanträge, Publikationen und andere wissenschaftliche Dokumente erfordern erheblichen Aufwand. Gleichzeitig existieren Methoden und Technologien in den Bereichen Information Retrieval, Maschinelles Lernen und Semantischer Technologien, die für die Analyse und Bewertung dieser Texte geeignet sind. Diese Arbeit zielt darauf ab, Aufwände im Lebenszyklus von öffentlich geförderten Forschungsprojekten zu optimieren. Sie identifiziert aktuelle Entwicklungen und Technologien, um Kriterien für eine Gesamtarchitektur abzuleiten, die wissenschaftliche Texte qualitativ annotiert, trainiert und evaluiert. Das resultierende Framework namens FELIX dient als prototypisches System für die computergestützte Assistenz zur Evaluation wissenschaftlicher Texte. Datenkorpora aus Forschungsanträgen und Publikationen wurden für explorative Experimente verwendet, die u. a. auf Methoden des Maschinellen Lernens basieren. FELIX ermöglicht die Analyse von Texten und Metadaten, die Klassifizierung nach definierten Kriterien und die Vorhersage der Bewilligung von Forschungsanträgen. Die Konzeption und Evaluierung von FELIX führte zu wissenschaftlichen und praktischen Implikationen zur Optimierung des Forschungsmanagements.
282.
Schlosser, Max; Walther, Michael; Manthey, Robert; Vogel, Richard; Baumgart, Matthias; Roschke, Christian; Ritter, Marc; Vodel, Matthias
Unifying the Data Highway: A Harmonized Telemetry Format for Driver Analytics Proceedings Article
In: 2024.
@inproceedings{schlosserUnifyingDataHighway2024,
title = {Unifying the Data Highway: A Harmonized Telemetry Format for Driver Analytics},
author = {Max Schlosser and Michael Walther and Robert Manthey and Richard Vogel and Matthias Baumgart and Christian Roschke and Marc Ritter and Matthias Vodel},
doi = {10.1109/iceccme62383.2024.10796414},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
281.
Platte, Benny; Kowerko, Danny; Langner, Holger; Skuras, Jan Anastassis; Ritter, Marc; Roschke, Christian
“Synthetic-Seed-Saturation“ Concept: Overcome Nonresponse-Bias in Retrospective Medical Studies Proceedings Article
In: 2024.
@inproceedings{platte_synthetic-seed-saturation_2024,
title = {“Synthetic-Seed-Saturation“ Concept: Overcome Nonresponse-Bias in Retrospective Medical Studies},
author = {Benny Platte and Danny Kowerko and Holger Langner and Jan Anastassis Skuras and Marc Ritter and Christian Roschke},
doi = {10.1109/ichi61247.2024.00122},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
280.
Vogel, Richard; Schlosser, Tobias; Manthey, Robert; Ritter, Marc; Vodel, Matthias; Eibl, Maximilian; Schneider, Kristan
A Meta Algorithm for Interpretable Ensemble Learning: The League of Experts Artikel
In: Machine Learning and Knowledge Extraction, 2024.
@article{vogelMetaAlgorithmInterpretable2024,
title = {A Meta Algorithm for Interpretable Ensemble Learning: The League of Experts},
author = {Richard Vogel and Tobias Schlosser and Robert Manthey and Marc Ritter and Matthias Vodel and Maximilian Eibl and Kristan Schneider},
doi = {10.3390/make6020038},
year = {2024},
date = {2024-01-01},
journal = {Machine Learning and Knowledge Extraction},
abstract = {<jats:p>Background. The importance of explainable artificial intelligence and machine learning (XAI/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method’s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations of black box models. These approaches add additional sources for errors without resolving their shortcomings. Less effort is directed into the design of intrinsically interpretable approaches. Methods. We introduce an intrinsically interpretable methodology motivated by ensemble learning: the League of Experts (LoE) model. We establish the theoretical framework first and then deduce a modular meta algorithm. In our description, we focus primarily on classification problems. However, LoE applies equally to regression problems. Specific to classification problems, we employ classical decision trees as classifier ensembles as a particular instance. This choice facilitates the derivation of human-understandable decision rules for the underlying classification problem, which results in a derived rule learning system denoted as RuleLoE. Results. In addition to 12 KEEL classification datasets, we employ two standard datasets from particularly relevant domains—medicine and finance—to illustrate the LoE algorithm. The performance of LoE with respect to its accuracy and rule coverage is comparable to common state-of-the-art classification methods. Moreover, LoE delivers a clearly understandable set of decision rules with adjustable complexity, describing the classification problem. Conclusions. LoE is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications.</jats:p>},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<jats:p>Background. The importance of explainable artificial intelligence and machine learning (XAI/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method’s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations of black box models. These approaches add additional sources for errors without resolving their shortcomings. Less effort is directed into the design of intrinsically interpretable approaches. Methods. We introduce an intrinsically interpretable methodology motivated by ensemble learning: the League of Experts (LoE) model. We establish the theoretical framework first and then deduce a modular meta algorithm. In our description, we focus primarily on classification problems. However, LoE applies equally to regression problems. Specific to classification problems, we employ classical decision trees as classifier ensembles as a particular instance. This choice facilitates the derivation of human-understandable decision rules for the underlying classification problem, which results in a derived rule learning system denoted as RuleLoE. Results. In addition to 12 KEEL classification datasets, we employ two standard datasets from particularly relevant domains—medicine and finance—to illustrate the LoE algorithm. The performance of LoE with respect to its accuracy and rule coverage is comparable to common state-of-the-art classification methods. Moreover, LoE delivers a clearly understandable set of decision rules with adjustable complexity, describing the classification problem. Conclusions. LoE is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications.</jats:p>
279.
278.
Vogel, Richard; Manthey, Robert; Baumgart, Matthias; Roschke, Christian; Ritter, Marc; Vodel, Matthias
Tamperproof Data Transmission to Offline IoT Devices in a Zero-Trust Environment Proceedings Article
In: 2024.
@inproceedings{vogelTamperproofDataTransmission2024,
title = {Tamperproof Data Transmission to Offline IoT Devices in a Zero-Trust Environment},
author = {Richard Vogel and Robert Manthey and Matthias Baumgart and Christian Roschke and Marc Ritter and Matthias Vodel},
doi = {10.1109/icnc59896.2024.10555945},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
277.
Bätz, Johannes; Weinhold, Emilia; Prause, Martin; Breck, Dominik; Knauer, Alexander; Baumgart, Matthias; Ritter, Marc; Roschke, Christian
DEVELOPMENT OF A WEB-BASED APPLICATION FOR PROCESSING OF STUDENT-BASED PEER REVIEWS OF MULTIMEDIA DATA Proceedings Article
In: UNDERSTANDING AND DEVELOPING DIGITAL TEACHING-LEARNING SCENARIOS AT UNIVERSITIES AS VALUE PROPOSITIONS, 2024.
@inproceedings{batz_development_2024,
title = {DEVELOPMENT OF A WEB-BASED APPLICATION FOR PROCESSING OF STUDENT-BASED PEER REVIEWS OF MULTIMEDIA DATA},
author = {Johannes Bätz and Emilia Weinhold and Martin Prause and Dominik Breck and Alexander Knauer and Matthias Baumgart and Marc Ritter and Christian Roschke},
doi = {10.21125/iceri.2024.1152},
year = {2024},
date = {2024-01-01},
booktitle = {UNDERSTANDING AND DEVELOPING DIGITAL TEACHING-LEARNING SCENARIOS AT UNIVERSITIES AS VALUE PROPOSITIONS},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
276.
Thiele, Jeannine; Thiele, Elisa; Roschke, Christian; Heinzig, Manuel; Ritter, Marc
Towards Semi-Automated Game Analytics: An Exploratory Study on Deep Learning-Based Image Classification of Characters in Auto Battler Games Proceedings Article
In: Lecture Notes in Computer Science, 2024.
@inproceedings{thiele_towards_2024,
title = {Towards Semi-Automated Game Analytics: An Exploratory Study on Deep Learning-Based Image Classification of Characters in Auto Battler Games},
author = {Jeannine Thiele and Elisa Thiele and Christian Roschke and Manuel Heinzig and Marc Ritter},
doi = {10.1007/978-3-031-60692-2_20},
year = {2024},
date = {2024-01-01},
booktitle = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
275.
Manthey, Robert; Vogel, Richard; Vodel, Matthias
Decentralized Offline Smart Lock - Chainlock Proceedings Article
In: 3rd Blockchain and Cryptocurrency Conference, 2024.
@inproceedings{mantheyDecentralizedOfflineSmart2024,
title = {Decentralized Offline Smart Lock - Chainlock},
author = {Robert Manthey and Richard Vogel and Matthias Vodel},
year = {2024},
date = {2024-01-01},
booktitle = {3rd Blockchain and Cryptocurrency Conference},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
274.
Lampe, Alexander; Ritter, Marc; Hentschel, Nicolas; Kaminsky, Theo; Vodel, Matthias; Sieber, Adrian; Marasas, Michelle; Beier-Grunwald, Rico; Roschke, Christian; Heinze, Isabel
Raspberry Pi Controller for Remote Laboratory Hardware Access Proceedings Article
In: The Sixteenth International Conference on Mobile, Hybrid, and On-line Learning - eLmL 2024, 2024.
@inproceedings{lampeRaspberryPiController2024,
title = {Raspberry Pi Controller for Remote Laboratory Hardware Access},
author = {Alexander Lampe and Marc Ritter and Nicolas Hentschel and Theo Kaminsky and Matthias Vodel and Adrian Sieber and Michelle Marasas and Rico Beier-Grunwald and Christian Roschke and Isabel Heinze},
year = {2024},
date = {2024-01-01},
booktitle = {The Sixteenth International Conference on Mobile, Hybrid, and On-line Learning - eLmL 2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
273.
Kothe, Helena Sophie; Apfelstädt, Madlien; Plekat, Marie-Luise; Gründel, Janine; Müller, Felix Jan; Fischer, Luca; Helmer, Finn Johannes; Heinzig, Manuel; Kühn, Alexander Thomas; Vodel, Matthias; Herrmann-Geppert, Iris; Roschke, Christian; Ritter, Marc
Serious Games as an Educational Strategy in Chemistry Classes: Case Study of a Mobile Application for learning Chemistry in School Proceedings Article
In: 2024.
@inproceedings{kothe_serious_2024,
title = {Serious Games as an Educational Strategy in Chemistry Classes: Case Study of a Mobile Application for learning Chemistry in School},
author = {Helena Sophie Kothe and Madlien Apfelstädt and Marie-Luise Plekat and Janine Gründel and Felix Jan Müller and Luca Fischer and Finn Johannes Helmer and Manuel Heinzig and Alexander Thomas Kühn and Matthias Vodel and Iris Herrmann-Geppert and Christian Roschke and Marc Ritter},
doi = {10.1109/segah61285.2024.10639533},
year = {2024},
date = {2024-01-01},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
272.
Manthey, Robert; Vogel, Richard; Vodel, Matthias
Offlinefähige Schließanlagen auf Blockchain-Basis Proceedings Article
In: Konferenzband zum Scientific Trackder Blockchain Autumn School 2024, 2024.
@inproceedings{mantheyOfflinefahigeSchliessanlagenAuf2024,
title = {Offlinefähige Schließanlagen auf Blockchain-Basis},
author = {Robert Manthey and Richard Vogel and Matthias Vodel},
year = {2024},
date = {2024-01-01},
booktitle = {Konferenzband zum Scientific Trackder Blockchain Autumn School 2024},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
271.
Wittrin, Ruben; Platte, Benny; Roschke, Christian; Ritter, Marc; Eibl, Maximilian; Steiner, Carolin Isabel; Tolkmitt, Volker
In: IEEE Transactions on Learning Technologies, 2024.
@article{wittrin_game_2024,
title = {The Game Effect: Comparison of Game and Nongame Learning Environments Using the Example of “Arctic Economy”},
author = {Ruben Wittrin and Benny Platte and Christian Roschke and Marc Ritter and Maximilian Eibl and Carolin Isabel Steiner and Volker Tolkmitt},
doi = {10.1109/tlt.2023.3274747},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Learning Technologies},
abstract = {Virtual environments open up far-reaching possibilities with respect to knowledge impartation. Nevertheless, they have the potential to negatively influence learning behavior. As a possible positive determinant, especially in the digital context, the moment “game” can be listed. Accordingly, previous studies prove an overall positive influence of serious games on learning success and motivation. However, the current state of research only allows for careful and few conclusions in terms of a nuanced differentiation of this influence. Thus, this study differentiates on a deeper level with regard to different parameters of learning success and motivation. The aim of the study is to quantify and evaluate a possible influence of the factor “game” with regard to these parameters. Two versions of the modular software environment Arctic Economy , a game and a nongame version, served as the basis for evaluation. Both versions were compared in a field experiment, with randomized group formation ( N = 97) and repeated measures. The data analysis showed the game group having a tendency to demonstrate better learning performance. In the differentiated analysis, a significant effect can be proven: subjects in the game group were able to remember facts more easily than subjects in the nongame group (group difference retention rate x¯¯¯= 17%). In addition, participants showed an average of 46% higher motivation and were significantly more capable of linking the application's content with reality. The identified “game effect” can therefore be classified as highly significant in the context of this study.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Virtual environments open up far-reaching possibilities with respect to knowledge impartation. Nevertheless, they have the potential to negatively influence learning behavior. As a possible positive determinant, especially in the digital context, the moment “game” can be listed. Accordingly, previous studies prove an overall positive influence of serious games on learning success and motivation. However, the current state of research only allows for careful and few conclusions in terms of a nuanced differentiation of this influence. Thus, this study differentiates on a deeper level with regard to different parameters of learning success and motivation. The aim of the study is to quantify and evaluate a possible influence of the factor “game” with regard to these parameters. Two versions of the modular software environment Arctic Economy , a game and a nongame version, served as the basis for evaluation. Both versions were compared in a field experiment, with randomized group formation ( N = 97) and repeated measures. The data analysis showed the game group having a tendency to demonstrate better learning performance. In the differentiated analysis, a significant effect can be proven: subjects in the game group were able to remember facts more easily than subjects in the nongame group (group difference retention rate x¯¯¯= 17%). In addition, participants showed an average of 46% higher motivation and were significantly more capable of linking the application's content with reality. The identified “game effect” can therefore be classified as highly significant in the context of this study.