TY - CHAP A1 - Picklum, Mareike A1 - Modzelewski, Georg A1 - Knoop, Susanne A1 - Lichtenberg, Toke A1 - Dittmann, Philipp A1 - Böhme, Tammo A1 - Fehn, Volker A1 - John, Christian A1 - Kenkel, Johannes A1 - Krieter, Philipp A1 - Niethen, Patrick A1 - Pampuch, Nicole A1 - Schnelle, Marcel A1 - Schwarte, Yvonne A1 - Stark, Sanja A1 - Steenbergen, Alexander A1 - Stehr, Malte A1 - Wielenberg, Henning A1 - Yildirim, Merve A1 - Yüzüncü, Can A1 - Pollmann, Frederic A1 - Wenig, Dirk A1 - Malaka, Rainer ED - Herrlich, Marc ED - Malaka, Rainer ED - Masuch, Maic T1 - Player Control in a Real-Time Mobile Augmented Reality Game T2 - Entertainment Computing - ICEC 2012 11th International Conference, ICEC 2012, Bremen, Germany, September 26-29, 2012 Y1 - 2012 SN - 9783642335419 U6 - https://doi.org/10.1007/978-3-642-33542-6_36 SN - 0302-9743 VL - LNISA,vol. 7522 SP - 393 EP - 396 PB - Springer Nature CY - Berlin, Heidelberg ER - TY - JOUR A1 - Viertel, Michael A1 - Krieter, Philipp A1 - Breiter, Andreas T1 - ‹Da habe ich es dann einfach ausgeschaltet›: Perspektiven von Lernenden auf Datensammlung mittels Langzeit-Bildschirmaufzeichnungen in non-formalen Bildungskontexten,Learners’ Perspectives on Data Collection Using Long-Term Screen Recordings in Non-Formal Educational Contexts JF - MedienPädagogik: Zeitschrift für Theorie und Praxis der Medienbildung N2 - Durch die Datafizierung im Bildungsbereich eröffnen sich neue Möglichkeiten datengetriebene Analysen von Lernprozessen voranzutreiben. Das Dilemma, einerseits Lernprozesse verbessern zu können und andererseits immer stärker in die Privatsphäre der Lernenden einzudringen, lässt sich hierbei nicht vollständig auflösen. In diesem Beitrag möchten wir vorstellen, wie im Projekt musicalytics in Musikschulkursen zum Thema Songwriting in hybriden Lehr-/Lernarrangements während des gesamten Kursverlaufs Bildschirmaufzeichnungen angefertigt wurden, um die Software- und Internetnutzung durch die Teilnehmenden ausserhalb ihrer regulären Kurszeiten zu untersuchen. Neben einer Darstellung der technischen Umsetzung dieser hoch immersiven Form der Datensammlung werden im Beitrag vor allem die Implikationen einer solchen Datafizierung am Beispiel von Bildschirmaufnahmen für die Teilnehmenden diskutiert. Die Lernenden wurden im Anschluss an die Kurse befragt, wie sie diese weitgehende Form der Datenerhebung wahrgenommen haben und welche Rolle diese Art der Beobachtung für ihre Kursaktivitäten spielte. Die zentralen Ergebnisse der qualitativen Interviewstudie mit 15 Teilnehmenden geben Einblick in die ambivalenten Positionierungen der Befragten hinsichtlich Selbstbestimmung, Entgrenzung, Vermessung und Kontrolle im Kursverlauf. Die Ergebnisse werden vor dem Hintergrund des non-formalen Lernsettings eingeordnet und diskutiert. Zudem wird deren mögliche Bedeutung für formale Bildungskontexte wie die Schule kritisch reflektiert. Y1 - 2021 U6 - https://doi.org/10.21240/mpaed/44/2021.10.26.X SN - 1424-3636 VL - 44 SP - 1 EP - 21 PB - OAPublishing Collective ER - TY - CHAP A1 - Finken, Julia A1 - Marx, Franziska A1 - Meyer, Michaela A1 - Krieter, Philipp A1 - Breiter, Andreas ED - Igel, Christoph ED - Ullrich, Carsten ED - Wessner, Martin ED - Gesellschaft für Informatik, Sonstige T1 - Entwicklung und Durchführung computerbasierter Tests zur Messung von Musikkompetenzen T2 - Bildungsräume DeLFI 2017 - Die 15. e-Learning Fachtagung Informatik der Gesellschaft für Informatik, 5. bis 8. September 2017, Chemnitz, Deutschland N2 - Das Fach Musik stellt besondere Herausforderungen an die Kompetenzmessung. Das gilt sowohl für die psychometrischen Modelle als auch für die Entwicklung computergestützter Testsysteme. Insbesondere die Verwendung von Bild- und Tonbeispielen als auch die Messung von Rhythmus- und Melodieverständnis setzen neuartige multimediale Testitems voraus. In Zusammenarbeit von Musikpädagoginnen und -pädagogen und Informatikerinnen und Informatikern wurde im Projekt „PosyMus – Potenziale von Feedbacksystemen im Musikunterricht“6 ein neuartiges Testsystem entwickelt und in Schulen getestet. Der Beitrag stellt die Testitems sowie die Erhebungen in den Schulen vor. Dabei wurde zweierlei deutlich: Einerseits fördern die computerbasierten Aufgabentypen die Motivation von Lernenden und andererseits sind zahlreiche praktische Herausforderungen zur Einbettung in den Unterrichtsalltag zu lösen. Insbesondere die Rückmeldung der Testergebnisse wurde als Anforderung formuliert. Diese wird in Zukunft im Projektverlauf durch ein elektronisches Rückmeldesystem umgesetzt. Y1 - 2017 SN - 978-3-88579-667-1 VL - DeLFI 2017 - Bildungsräume GI-Edition. Proceedings, vol. P-273 SP - 63 EP - 74 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Krieter, Philipp A1 - Zerrer, Patrick A1 - Puschmann, Cornelius A1 - Geise, Stephanie ED - Boratto, Ludovico ED - Gena, Cristina ED - Marras, Mirko ED - Germanakos, Panagiotis ED - Popescus, Elvira T1 - Following Topics Across All Apps and Media Formats: Mobile Keyword Tracking as a Privacy-Friendly Data Source in Mobile Media Research T2 - UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, Cagliari Italy, July 1 - 4, 2024 Y1 - 2024 SN - 979-8-4007-0466-6 U6 - https://doi.org/10.1145/3631700.3664879 SP - 126 EP - 131 PB - ACM CY - New York ER - TY - CHAP A1 - Breiter, Andreas A1 - Krieter, Philipp A1 - Lehmann-Wermser, Andreas A1 - Viertel, Michael A1 - Weyel, Benjamin T1 - KI-Unterstützung in der Kulturellen Bildung. Potenziale von Learning Analytics für Musiklernen am Beispiel automatisierter Auswertungen von Bildschirmaufzeichnungen T2 - Künstliche Intelligenz in der Bildung Y1 - 2023 SN - 9783658400781 U6 - https://doi.org/10.1007/978-3-658-40079-8_18 SP - 377 EP - 392 PB - Springer Nature CY - Wiesbaden ER - TY - CHAP A1 - Krieter, Philipp ED - Matviienko, Andrii ED - Niess, Jasmin ED - Kosch, Thomas T1 - Recall for Good: Flexible Retrospective Mobile In-App Topic Tracking in a Privacy-Friendly Local-First Approach T2 - MUM '24: Proceedings of the International Conference on Mobile and Ubiquitous Multimedia, Stockholm Sweden December 1 - 4, 2024 Y1 - 2024 SN - 979-8-4007-1283-8 U6 - https://doi.org/10.1145/3701571.3703382 SP - 451 EP - 453 PB - ACM CY - New York ER - TY - CHAP A1 - Lehmann-Wermser, Andreas A1 - Breiter, Andreas A1 - Finken, Julia A1 - Krieter, Philipp A1 - Weyel, Benjamin ED - Jörissen, Benjamin ED - Kröner, Stephan ED - Unterberg, Lisa T1 - musicalytics: Wis Ismt man Musik in dar digitalen Welt? T2 - Forschung zur Digitalisierung in der kulturellen Bildung Y1 - 2019 SN - 9783867365222 SP - 149 EP - 160 PB - kopaed CY - München ER - TY - CHAP A1 - Krieter, Philipp A1 - Viertel, Michael A1 - Breiter, Andreas ED - Lingnau, Andreas T1 - Supporting Students’ Privacy: How Does Learner Control over Their Data Affect the Dataset for Learning Analytics? T2 - DELFI 2021 19. Fachtagung Bildungstechnologien der GI ; Proceedings of DELFI Workshops 2021, 13.09.2021 Dortmund, N2 - An increasing amount of data for learning analytics is available due to the spread of digital learning environments. Collecting data on student behavior and action in these environments raises the question of how personal privacy can be addressed, as students are the most important stakeholder in the process. In our study we propose a simple but powerful approach to enhance student privacy: a switch to turn the data collection on and off. We let the students decide for themselves when they want to stop and go on with the collection of their data. In this paper, we take a closer look at how our students make use of this option and when and how this influences the collected data by exemplarily investigating the data of two of our participants in detail. In our analysis we combine system log files, screen recordings and qualitative interviews. KW - privacy KW - student empowerment KW - screen recordings KW - mobile digital learning environments KW - data collection Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1393-opus4-7338 SN - 978-3-946757-03-0 SP - 146 EP - 157 PB - Hochschule Ruhr Wes CY - Bottrop ER - TY - CHAP A1 - Lehmann-Wermser, Andreas A1 - Breiter, Andreas A1 - Weigel, Benjamin A1 - Viertel, Michael A1 - Krieter, Philipp ED - Jörissen, Benjamin ED - Kröner, Stephan ED - Birnbaum, Lisa ED - Krämer, Franz ED - Schmiedl, Friederike T1 - Musikalisches non-formal situiertes Lernen in digitalen Lernumgebungen T2 - Digitalisierung in der kulturellen Bildung interdisziplinäre Perspektiven für ein Feld im Aufbruch Y1 - 2023 SN - 978-3-96848-687-1 SP - 62 EP - 70 PB - kopaed CY - München ER - TY - CHAP A1 - Maya, Fatima A1 - Krieter, Philipp A1 - Wolf, Karsten D. A1 - Breiter, Andreas ED - Mandausch, Martin ED - Henning, Peter A. T1 - Extracting Production Style Features of Educational Videos with Deep Learning T2 - Proceedings of DELFI Workshops 2022 Karlsruhe, 12. September 2022 N2 - Enforced by the pandemic, the production of videos in educational settings and their availability on learning platforms allow new forms of video-based learning. This has a strong benefit of covering multiple topics with different design styles and facilitating the learning experience. Consequently, research interest in video-based learning has increased remarkably, with many studies focusing on examining the diverse visual properties of videos and their impact on learner engagement and knowledge gain. However, manually analysing educational videos to collect metadata and to classify videos for quality assessment is a time-consuming activity. In this paper, we address the problem of automatic video feature extraction related to video production design. To this end, we introduce a novel use case for object detection models to recognize the human embodiment and the type of teaching media used in the video. The results obtained on a small-scale custom dataset show the potential of deep learning models for visual video analysis. This will allow for future use in developing an automatic video assessment system to reduce the workload for teachers and researchers. KW - video-based learning KW - MOOC KW - video lecture design KW - deep learning KW - video features KW - object detection KW - YOLOv4-algorithm Y1 - 2022 U6 - https://doi.org/10.18420/delfi2022-ws-23 SP - 123 EP - 132 PB - Gesellschaft für Informatik e.V. CY - Bonn ER - TY - CHAP A1 - Krieter, Philipp A1 - Breiter, Andreas ED - Balie, Lynne ED - Oliver, Nuria T1 - Analyzing mobile application usage: generating log files from mobile screen recordings T2 - Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, Barcelona Spain, September 3 - 6, 2018 Y1 - 2018 SN - 978-1-4503-5898-9 U6 - https://doi.org/10.1145/3229434.3229450 SP - 1 EP - 10 PB - ACM CY - New York ER - TY - CHAP A1 - Krieter, Philipp ED - Paternò,, Fabio T1 - Can I record your screen? mobile screen recordings as a long-term data source for user studies T2 - Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia, MUM2019, November 26–29, 2019, Pisa, Italy Y1 - 2019 SN - 978-1-4503-7624-2 U6 - https://doi.org/10.1145/3365610.3365618 SP - 1 EP - 10 PB - ACM CY - New York ER - TY - JOUR A1 - Krieter, Philipp T1 - Are You Still There? An Exploratory Case Study on Estimating Students’ LMS Online Time by Combining Log Files and Screen Recordings JF - IEEE Transactions on Learning Technologies N2 - The time students spend in a learning management system (LMS) is an important measurement in learning analytics (LA). One of the most common data sources is log files from LMS, which do not directly reveal the online time, the duration of which needs to be estimated. As this measurement has a great impact on the results of statistical models in LA, its estimation is crucial. In the literature, there are many strategies for estimating the duration, which do not represent the actual online time of the students. In this article, we combine LMS log files of our students with parallel screen recordings and automatically analyze for how long the LMS is present in the video. We visualize the results and show that common online time estimation strategies do not represent the online time for our students accurately. By using modified online time estimation methods, we find estimations that fit the data of our students better on an individual basis. Y1 - 2022 U6 - https://doi.org/10.1109/TLT.2022.3154828 SN - 1939-1382 VL - 15 IS - 1 SP - 55 EP - 63 PB - IEEE ER - TY - CHAP A1 - Krieter, Philipp A1 - Viertel, Michael A1 - Breiter, Andreas ED - Alario-Hoyos, Carlos ED - Rodríguez-Triana, María Jesús ED - Scheffel, Maren ED - Arnedillo-Sánchez, Inmaculada ED - Dennerlein, Sebastian Maximilian T1 - We Know What You Did Last Semester: Learners’ Perspectives on Screen Recordings as a Long-Term Data Source for Learning Analytics T2 - Addressing global challenges and quality education: 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Germany, September 14–18, 2020 Y1 - 2020 SN - 9783030577162 U6 - https://doi.org/10.1007/978-3-030-57717-9_14 SN - 0302-9743 VL - Lecture Notes in Computer Science, vol. 12315 SP - 187 EP - 199 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Krieter, Philipp A1 - Breiter, Andreas ED - Hofhues, Sandra ED - Schiefner-Rohs, Mandy ED - Aßmann, Sandra ED - Brahm, Taiga T1 - Digitale Spuren von Studierenden in virtuellen Lernumgebungen T2 - Studierende – Medien – Universität: Einblicke in studentische Medienwelt N2 - In diesem Beitrag werden die Ergebnisse und der Prozess der LogfileAnalyse im Projekt You(r) Study beschrieben, die zum Ziel hat, die qualitativen Daten des Projekts mit den quantitativen Logdaten eines Learning Management Systems zu verbinden. Mittels deskriptiver Methoden und Clusterbildung werden die Logdaten in Hinblick auf die Fragestellungen des Projekts betrachtet. Außerdem wird kritisch diskutiert, welche Grenzen eine Logfile-Analyse in Bezug zur Zielsetzung hat KW - moodle KW - Logfile-Analyse KW - Learning Analytics KW - Learning Management System Y1 - 2020 SN - 9783830940494 U6 - https://doi.org/10.31244/9783830990499 SP - 131 EP - 152 PB - Waxmann CY - Münster ER - TY - THES A1 - Krieter, Philipp T1 - Looking Inside - Mobile Screen Recordings as a Privacy Friendly Long-Term Data Source to Analyze User Behavior N2 - Mobile devices are ubiquitous in many societies and shape the way we interact with technology and each other. Research on how we use and perceive technology is essential to understand its impact. This work advances how we can follow user behavior on mobile devices. We combine the strength of two common data sources for tracking on mobile devices, log files, and screen recordings. Log files are suitable for long-term and privacy-friendly analyzation but provide rather general data (e.g. system log files) unless one has access to the source code of the applications or operating systems. Screen recordings are usually used for short-termed analysis (e.g. usability tests) because the analysis is time-consuming, but they provide all activities on the screen in high detail regardless of which application or operating system. This thesis combines both data sources and presents an approach to automatically generate log files from mobile screen recordings. The approach utilizes methods of computer vision and machine learning to automatically process screen recordings and extend their use. Screen recordings reveal virtually everything a user does with a device, making privacy important, especially in user studies. We present a privacy concept and implementation and show how the risk of exposing private data can be reduced, by processing all recordings locally on the mobile devices and anonymizing the resulting log files. In order to apply the developed method in practice, we carry out a study in the context of education and show how log files of screen recordings can complement and extend existing research in learning analytics. This thesis opens up novel perspectives on how we can look at human-computer interaction with mobile devices. We show how to generate long-term log data with high detail and accuracy from mobile screen recordings, in a privacy-friendly way, locally on mobile devices. KW - mobile screen recordings KW - log files KW - learning analytics KW - user behavior KW - privacy Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:46-elib43189 PB - Bremen CY - Universität Bremen ER - TY - CHAP A1 - Krieter, Philipp A1 - Breiter, Andreas ED - Krömker, Detlef ED - Schroeder, Ulrik T1 - Track every move of your students: log files for Learning Analytics from mobile screen recordings T2 - DeLFI 2018: die 16. E-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V., 10.-12. September 2018, Frankfurt am Main, Deutschland N2 - One of the main data sources for Learning Analytics are Learning Management Systems (LMS).These log files are limited though to interactions within the LMS and cannot take into account interactions of students in other applications and software in a digital learning environment. In this paper, we present an approach for generating log files based on mobile screen recordings as a data source for Learning Analytics. Logging mobile application usage is limited to rather general system events unless you have access to the source code of the operating system or applications.To address this we generate log files from mobile screen recordings by applying computer vision and machine learning methods to detect individually defined events. In closing, we discuss how these log files can be used as a data source for Learning Analytics and relevant ethical concerns. KW - Learning Analytics KW - mobile screen recordings KW - Computer Vison KW - data sources KW - Human Computer Interaction KW - log files Y1 - 2018 SN - 9783885796787 U6 - https://doi.org/10.18154/RWTH-2018-229913 VL - Lecture Notes in Informatics(LNI)-Proceedings, Vol. P-284 SP - 231 EP - 242 PB - Köllen Druck+Verlag CY - Bonn ER - TY - CHAP A1 - Krieter, Philipp A1 - de León, Ernesto A1 - Votta, Fabio A1 - Roşca, Alexandra T1 - Classifying TikToks Locally: Political Content Detection with Phi-4 on Android T2 - Proceedings of the 24th International Conference on Mobile and Ubiquitous Multimedia KW - Benutzerverhalten KW - Mobiles Endgerät KW - TikTok KW - Nutzerverfolgung KW - Datenschutz KW - Künstliche Intelligenz KW - Großes Sprachmodell Y1 - 2025 SN - 9798400720154 U6 - https://doi.org/10.1145/3771882.3773943 SP - 436 EP - 438 PB - ACM CY - New York ER - TY - JOUR A1 - Zerrer, Patrick A1 - Pressmann, Paul A1 - Puschmann, Cornelius A1 - Krieter, Philipp T1 - Beyond the Black Box: A Multimodal Approach to Understanding In-App Communication JF - Medien & Kommunikationswissenschaft N2 - Mobile devices are increasingly central as sources of up-to-date information, making the precise recording of information behavior on these devices more relevant for research. Established methods of automated data collection are reaching their limits when capturing in-app communication, such as political content within social media applications. Based on two case studies, we present two approaches that enable the identification of exposure to relevant content and, to some extent, the collection of in-app content. The first case study focuses on identifying relevant exposure across different apps on a mobile device using app tracking and screen recordings. The second case study focuses on linking exposure to seen content and deriving respective content features to obtain an enriched dataset. We discuss the advantages and limitations of both approaches and present conceptual frameworks for processing and analyzing such data. KW - mobile tracking KW - screen recording KW - mobile sensing KW - android logs KW - in-app tracking Y1 - 2026 U6 - https://doi.org/10.5771/1615-634X-2026-1-31 SN - 1615-634X VL - 74 IS - 1 SP - 31 EP - 51 PB - Nomos Verlag ER -