@inproceedings{PicklumModzelewskiKnoopetal.2012, author = {Picklum, Mareike and Modzelewski, Georg and Knoop, Susanne and Lichtenberg, Toke and Dittmann, Philipp and B{\"o}hme, Tammo and Fehn, Volker and John, Christian and Kenkel, Johannes and Krieter, Philipp and Niethen, Patrick and Pampuch, Nicole and Schnelle, Marcel and Schwarte, Yvonne and Stark, Sanja and Steenbergen, Alexander and Stehr, Malte and Wielenberg, Henning and Yildirim, Merve and Y{\"u}z{\"u}nc{\"u}, Can and Pollmann, Frederic and Wenig, Dirk and Malaka, Rainer}, title = {Player Control in a Real-Time Mobile Augmented Reality Game}, series = {Entertainment Computing - ICEC 2012 11th International Conference, ICEC 2012, Bremen, Germany, September 26-29, 2012}, volume = {LNISA,vol. 7522}, booktitle = {Entertainment Computing - ICEC 2012 11th International Conference, ICEC 2012, Bremen, Germany, September 26-29, 2012}, editor = {Herrlich, Marc and Malaka, Rainer and Masuch, Maic}, publisher = {Springer Nature}, address = {Berlin, Heidelberg}, isbn = {9783642335419}, issn = {0302-9743}, doi = {10.1007/978-3-642-33542-6_36}, pages = {393 -- 396}, year = {2012}, language = {en} } @article{ViertelKrieterBreiter2021, author = {Viertel, Michael and Krieter, Philipp and Breiter, Andreas}, title = {‹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}, series = {MedienP{\"a}dagogik: Zeitschrift f{\"u}r Theorie und Praxis der Medienbildung}, volume = {44}, journal = {MedienP{\"a}dagogik: Zeitschrift f{\"u}r Theorie und Praxis der Medienbildung}, publisher = {OAPublishing Collective}, issn = {1424-3636}, doi = {10.21240/mpaed/44/2021.10.26.X}, pages = {1 -- 21}, year = {2021}, abstract = {Durch die Datafizierung im Bildungsbereich er{\"o}ffnen sich neue M{\"o}glichkeiten datengetriebene Analysen von Lernprozessen voranzutreiben. Das Dilemma, einerseits Lernprozesse verbessern zu k{\"o}nnen und andererseits immer st{\"a}rker in die Privatsph{\"a}re der Lernenden einzudringen, l{\"a}sst sich hierbei nicht vollst{\"a}ndig aufl{\"o}sen. In diesem Beitrag m{\"o}chten wir vorstellen, wie im Projekt musicalytics in Musikschulkursen zum Thema Songwriting in hybriden Lehr-/Lernarrangements w{\"a}hrend des gesamten Kursverlaufs Bildschirmaufzeichnungen angefertigt wurden, um die Software- und Internetnutzung durch die Teilnehmenden ausserhalb ihrer regul{\"a}ren 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 f{\"u}r 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 f{\"u}r ihre Kursaktivit{\"a}ten 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 m{\"o}gliche Bedeutung f{\"u}r formale Bildungskontexte wie die Schule kritisch reflektiert.}, language = {de} } @inproceedings{FinkenMarxMeyeretal.2017, author = {Finken, Julia and Marx, Franziska and Meyer, Michaela and Krieter, Philipp and Breiter, Andreas}, title = {Entwicklung und Durchf{\"u}hrung computerbasierter Tests zur Messung von Musikkompetenzen}, series = {Bildungsr{\"a}ume DeLFI 2017 - Die 15. e-Learning Fachtagung Informatik der Gesellschaft f{\"u}r Informatik, 5. bis 8. September 2017, Chemnitz, Deutschland}, volume = {DeLFI 2017 - Bildungsr{\"a}ume GI-Edition. Proceedings, vol. P-273}, booktitle = {Bildungsr{\"a}ume DeLFI 2017 - Die 15. e-Learning Fachtagung Informatik der Gesellschaft f{\"u}r Informatik, 5. bis 8. September 2017, Chemnitz, Deutschland}, editor = {Igel, Christoph and Ullrich, Carsten and Wessner, Martin and Gesellschaft f{\"u}r Informatik, Sonstige}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, isbn = {978-3-88579-667-1}, pages = {63 -- 74}, year = {2017}, abstract = {Das Fach Musik stellt besondere Herausforderungen an die Kompetenzmessung. Das gilt sowohl f{\"u}r die psychometrischen Modelle als auch f{\"u}r die Entwicklung computergest{\"u}tzter Testsysteme. Insbesondere die Verwendung von Bild- und Tonbeispielen als auch die Messung von Rhythmus- und Melodieverst{\"a}ndnis setzen neuartige multimediale Testitems voraus. In Zusammenarbeit von Musikp{\"a}dagoginnen und -p{\"a}dagogen 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 f{\"o}rdern die computerbasierten Aufgabentypen die Motivation von Lernenden und andererseits sind zahlreiche praktische Herausforderungen zur Einbettung in den Unterrichtsalltag zu l{\"o}sen. Insbesondere die R{\"u}ckmeldung der Testergebnisse wurde als Anforderung formuliert. Diese wird in Zukunft im Projektverlauf durch ein elektronisches R{\"u}ckmeldesystem umgesetzt.}, language = {de} } @inproceedings{KrieterZerrerPuschmannetal.2024, author = {Krieter, Philipp and Zerrer, Patrick and Puschmann, Cornelius and Geise, Stephanie}, title = {Following Topics Across All Apps and Media Formats: Mobile Keyword Tracking as a Privacy-Friendly Data Source in Mobile Media Research}, series = {UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, Cagliari Italy, July 1 - 4, 2024}, booktitle = {UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, Cagliari Italy, July 1 - 4, 2024}, editor = {Boratto, Ludovico and Gena, Cristina and Marras, Mirko and Germanakos, Panagiotis and Popescus, Elvira}, publisher = {ACM}, address = {New York}, isbn = {979-8-4007-0466-6}, doi = {10.1145/3631700.3664879}, pages = {126 -- 131}, year = {2024}, language = {en} } @incollection{BreiterKrieterLehmannWermseretal.2023, author = {Breiter, Andreas and Krieter, Philipp and Lehmann-Wermser, Andreas and Viertel, Michael and Weyel, Benjamin}, title = {KI-Unterst{\"u}tzung in der Kulturellen Bildung. Potenziale von Learning Analytics f{\"u}r Musiklernen am Beispiel automatisierter Auswertungen von Bildschirmaufzeichnungen}, series = {K{\"u}nstliche Intelligenz in der Bildung}, booktitle = {K{\"u}nstliche Intelligenz in der Bildung}, publisher = {Springer Nature}, address = {Wiesbaden}, isbn = {9783658400781}, doi = {10.1007/978-3-658-40079-8_18}, pages = {377 -- 392}, year = {2023}, language = {de} } @inproceedings{Krieter2024, author = {Krieter, Philipp}, title = {Recall for Good: Flexible Retrospective Mobile In-App Topic Tracking in a Privacy-Friendly Local-First Approach}, series = {MUM '24: Proceedings of the International Conference on Mobile and Ubiquitous Multimedia, Stockholm Sweden December 1 - 4, 2024}, booktitle = {MUM '24: Proceedings of the International Conference on Mobile and Ubiquitous Multimedia, Stockholm Sweden December 1 - 4, 2024}, editor = {Matviienko, Andrii and Niess, Jasmin and Kosch, Thomas}, publisher = {ACM}, address = {New York}, isbn = {979-8-4007-1283-8}, doi = {10.1145/3701571.3703382}, pages = {451 -- 453}, year = {2024}, language = {en} } @incollection{LehmannWermserBreiterFinkenetal.2019, author = {Lehmann-Wermser, Andreas and Breiter, Andreas and Finken, Julia and Krieter, Philipp and Weyel, Benjamin}, title = {musicalytics: Wis Ismt man Musik in dar digitalen Welt?}, series = {Forschung zur Digitalisierung in der kulturellen Bildung}, booktitle = {Forschung zur Digitalisierung in der kulturellen Bildung}, editor = {J{\"o}rissen, Benjamin and Kr{\"o}ner, Stephan and Unterberg, Lisa}, publisher = {kopaed}, address = {M{\"u}nchen}, isbn = {9783867365222}, pages = {149 -- 160}, year = {2019}, language = {de} } @inproceedings{KrieterViertelBreiter2021, author = {Krieter, Philipp and Viertel, Michael and Breiter, Andreas}, title = {Supporting Students' Privacy: How Does Learner Control over Their Data Affect the Dataset for Learning Analytics?}, series = {DELFI 2021 19. Fachtagung Bildungstechnologien der GI ; Proceedings of DELFI Workshops 2021, 13.09.2021 Dortmund,}, booktitle = {DELFI 2021 19. Fachtagung Bildungstechnologien der GI ; Proceedings of DELFI Workshops 2021, 13.09.2021 Dortmund,}, editor = {Lingnau, Andreas}, publisher = {Hochschule Ruhr Wes}, address = {Bottrop}, isbn = {978-3-946757-03-0}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:1393-opus4-7338}, pages = {146 -- 157}, year = {2021}, abstract = {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.}, language = {en} } @incollection{LehmannWermserBreiterWeigeletal.2023, author = {Lehmann-Wermser, Andreas and Breiter, Andreas and Weigel, Benjamin and Viertel, Michael and Krieter, Philipp}, title = {Musikalisches non-formal situiertes Lernen in digitalen Lernumgebungen}, series = {Digitalisierung in der kulturellen Bildung interdisziplin{\"a}re Perspektiven f{\"u}r ein Feld im Aufbruch}, booktitle = {Digitalisierung in der kulturellen Bildung interdisziplin{\"a}re Perspektiven f{\"u}r ein Feld im Aufbruch}, editor = {J{\"o}rissen, Benjamin and Kr{\"o}ner, Stephan and Birnbaum, Lisa and Kr{\"a}mer, Franz and Schmiedl, Friederike}, publisher = {kopaed}, address = {M{\"u}nchen}, isbn = {978-3-96848-687-1}, pages = {62 -- 70}, year = {2023}, language = {de} } @inproceedings{MayaKrieterWolfetal.2022, author = {Maya, Fatima and Krieter, Philipp and Wolf, Karsten D. and Breiter, Andreas}, title = {Extracting Production Style Features of Educational Videos with Deep Learning}, series = {Proceedings of DELFI Workshops 2022 Karlsruhe, 12. September 2022}, booktitle = {Proceedings of DELFI Workshops 2022 Karlsruhe, 12. September 2022}, editor = {Mandausch, Martin and Henning, Peter A.}, publisher = {Gesellschaft f{\"u}r Informatik e.V.}, address = {Bonn}, doi = {10.18420/delfi2022-ws-23}, pages = {123 -- 132}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{KrieterBreiter2018, author = {Krieter, Philipp and Breiter, Andreas}, title = {Analyzing mobile application usage: generating log files from mobile screen recordings}, series = {Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, Barcelona Spain, September 3 - 6, 2018}, booktitle = {Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, Barcelona Spain, September 3 - 6, 2018}, editor = {Balie, Lynne and Oliver, Nuria}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-5898-9}, doi = {10.1145/3229434.3229450}, pages = {1 -- 10}, year = {2018}, language = {en} } @inproceedings{Krieter2019, author = {Krieter, Philipp}, title = {Can I record your screen? mobile screen recordings as a long-term data source for user studies}, series = {Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia, MUM2019, November 26-29, 2019, Pisa, Italy}, booktitle = {Proceedings of the 18th International Conference on Mobile and Ubiquitous Multimedia, MUM2019, November 26-29, 2019, Pisa, Italy}, editor = {Patern{\`o},, Fabio}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-7624-2}, doi = {10.1145/3365610.3365618}, pages = {1 -- 10}, year = {2019}, language = {en} } @article{Krieter2022, author = {Krieter, Philipp}, title = {Are You Still There? An Exploratory Case Study on Estimating Students' LMS Online Time by Combining Log Files and Screen Recordings}, series = {IEEE Transactions on Learning Technologies}, volume = {15}, journal = {IEEE Transactions on Learning Technologies}, number = {1}, publisher = {IEEE}, issn = {1939-1382}, doi = {10.1109/TLT.2022.3154828}, pages = {55 -- 63}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{KrieterViertelBreiter2020, author = {Krieter, Philipp and Viertel, Michael and Breiter, Andreas}, title = {We Know What You Did Last Semester: Learners' Perspectives on Screen Recordings as a Long-Term Data Source for Learning Analytics}, series = {Addressing global challenges and quality education: 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Germany, September 14-18, 2020}, volume = {Lecture Notes in Computer Science, vol. 12315}, booktitle = {Addressing global challenges and quality education: 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Heidelberg, Germany, September 14-18, 2020}, editor = {Alario-Hoyos, Carlos and Rodr{\´i}guez-Triana, Mar{\´i}a Jes{\´u}s and Scheffel, Maren and Arnedillo-S{\´a}nchez, Inmaculada and Dennerlein, Sebastian Maximilian}, publisher = {Springer Nature}, address = {Cham}, isbn = {9783030577162}, issn = {0302-9743}, doi = {10.1007/978-3-030-57717-9_14}, pages = {187 -- 199}, year = {2020}, language = {en} } @incollection{KrieterBreiter2020, author = {Krieter, Philipp and Breiter, Andreas}, title = {Digitale Spuren von Studierenden in virtuellen Lernumgebungen}, series = {Studierende - Medien - Universit{\"a}t: Einblicke in studentische Medienwelt}, booktitle = {Studierende - Medien - Universit{\"a}t: Einblicke in studentische Medienwelt}, editor = {Hofhues, Sandra and Schiefner-Rohs, Mandy and Aßmann, Sandra and Brahm, Taiga}, publisher = {Waxmann}, address = {M{\"u}nster}, isbn = {9783830940494}, doi = {10.31244/9783830990499}, pages = {131 -- 152}, year = {2020}, abstract = {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}, subject = {moodle}, language = {de} } @phdthesis{Krieter2020, author = {Krieter, Philipp}, title = {Looking Inside - Mobile Screen Recordings as a Privacy Friendly Long-Term Data Source to Analyze User Behavior}, publisher = {Bremen}, address = {Universit{\"a}t Bremen}, doi = {10.26092/elib/103}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:46-elib43189}, year = {2020}, abstract = {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.}, language = {en} } @inproceedings{KrieterBreiter2018, author = {Krieter, Philipp and Breiter, Andreas}, title = {Track every move of your students: log files for Learning Analytics from mobile screen recordings}, series = {DeLFI 2018: die 16. E-Learning Fachtagung Informatik der Gesellschaft f{\"u}r Informatik e.V., 10.-12. September 2018, Frankfurt am Main, Deutschland}, volume = {Lecture Notes in Informatics(LNI)-Proceedings, Vol. P-284}, booktitle = {DeLFI 2018: die 16. E-Learning Fachtagung Informatik der Gesellschaft f{\"u}r Informatik e.V., 10.-12. September 2018, Frankfurt am Main, Deutschland}, editor = {Kr{\"o}mker, Detlef and Schroeder, Ulrik}, publisher = {K{\"o}llen Druck+Verlag}, address = {Bonn}, organization = {Gesellschaft f{\"u}r Informatik, Bonn}, isbn = {9783885796787}, doi = {10.18154/RWTH-2018-229913}, pages = {231 -- 242}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{KrieterdeLeonVottaetal.2025, author = {Krieter, Philipp and de Le{\´o}n, Ernesto and Votta, Fabio and Ro{\c{s}}ca, Alexandra}, title = {Classifying TikToks Locally: Political Content Detection with Phi-4 on Android}, series = {Proceedings of the 24th International Conference on Mobile and Ubiquitous Multimedia}, booktitle = {Proceedings of the 24th International Conference on Mobile and Ubiquitous Multimedia}, publisher = {ACM}, address = {New York}, isbn = {9798400720154}, doi = {10.1145/3771882.3773943}, pages = {436 -- 438}, year = {2025}, subject = {Benutzerverhalten}, language = {en} } @article{ZerrerPressmannPuschmannetal.2026, author = {Zerrer, Patrick and Pressmann, Paul and Puschmann, Cornelius and Krieter, Philipp}, title = {Beyond the Black Box: A Multimodal Approach to Understanding In-App Communication}, series = {Medien \& Kommunikationswissenschaft}, volume = {74}, journal = {Medien \& Kommunikationswissenschaft}, number = {1}, publisher = {Nomos Verlag}, issn = {1615-634X}, doi = {10.5771/1615-634X-2026-1-31}, pages = {31 -- 51}, year = {2026}, abstract = {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.}, language = {en} }