- informatik-vortraege - lists.rwth-aachen.de

TOMORROW UnRAVeL Survey Lecture "What's New in UnRAVeL?"
by Tim Seppelt 25 Apr '22

25 Apr '22

Dear all, this is a reminder for Nils Nießen's talk on Acceptance of Driverless Trains <https://www.unravel.rwth-aachen.de/go/id/taywq?#aaaaaaaaaatayyk> taking place *tomorrow at 16:30* in room 5053.2 and on Zoom. Please find the details below. > Digitalisation and automation are also making progress in rail > transportation. In isolated networks, such as metros, trains can > already run driverless today. The talk will highlight the > opportunities and risks of driverless driving on rail. > > A novel system can only be successfully implemented if it is also > accepted by the users. One focus of the talk will therefore be the > analysis of passenger acceptance of driverless rail transport. > Part of the programme of the research training group UnRAVeL is a series of introductory lectures on the topics of "randomness" and "uncertainty" in UnRAVeL’s research thrusts: Algorithms and complexity, verification, logic and languages, and their application scenarios. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. Science undergoes continuous change and lives from the constant quest for novel and better results, which are presented at conferences and in journals. This year, 10 UnRAVeL professors will present some of their most recent research successes. Everyone interested, in particular doctoral researchers and master students, are invited to attend the UnRAVeL lecture series 2022 and engage in discussions with the researchers. The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the ground floor of building E2. All events are hybrid. To join remotely, please use https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09 / Meeting ID: 960 0388 5007 / Passcode: 273710 Please find a list of all upcoming talks on the UnRAVeL website <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…> and below: * 26/04/2022 Nils Nießen: Acceptance of Driverless Trains * 03/05/2022 Jürgen Giesl: Improving Automatic Complexity Analysis of Probabilistic and Non-Probabilistic Integer Programs * 10/05/2022 Martin Grohe: Graph Representations Based on Homomorphisms * 17/05/2022 Christina Büsing: A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty * 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for Stochastic Hybrid Systems * 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process Regression with Applications to Safe Control and Learning * 28/06/2022 Britta Peis: Stackelberg Network Pricing Games * 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order Knowledge Bases We are looking forward to seeing many of you in the UnRAVeL survey lecture "What's New in UnRAVeL?". Best regards, Andreas Klinger, Birgit Willms, and Tim Seppelt Logo

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Informatik-Oberseminar Matthias Volk
by Birgit Willms 19 Apr '22

19 Apr '22

********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Donnerstag, 28. April 2022, 10:30 Uhr Ort: Raum 9222, Geb. E3, 2. Etage, Informatikzentrum, Ahornstr. 55 Der Vortrag ist auch online über Zoom zu verfolgen: https://rwth.zoom.us/j/99709768339?pwd=MndDQ1MxMVdQWVpYZGpvYSt4bmdKdz09 Meeting-ID: 997 0976 8339, Kenncode: 975390 Referent: Matthias Volk, M.Sc. (Lehrstuhl Informatik 2) Thema: Dynamic Fault Trees: Semantics, Analysis and Applications Abstract: Safe and reliable systems are crucial in today’s society. Fault trees are a prominent and widely-used model to assess and improve the reliability of systems. Fault trees model how component failures propagate through a system and lead to a failure of the overall system. Dynamic fault trees (DFTs) are an extension of (static) fault trees and allow more modelling flexibility by introducing dynamic gates, spare management, functional dependencies and failure restrictions. In this presentation, we investigate dynamic fault trees in detail and consider three main aspects: (1) the precise semantics of DFTs, (2) the analysis of DFTs by model checking techniques, and (3) the application of DFTs, for example in the railway domain. We first specify the semantics of dynamic fault trees in terms of generalized stochastic Petri nets (GSPNs). We investigate multiple semantic questions resulting from the combination of DFT elements. Our resulting GSPN framework subsumes the major existing DFT semantics and allows to pinpoint their differences. Secondly, we present analysis techniques for DFTs based on probabilistic model checking. We introduce several (orthogonal) optimisation techniques which exploit symmetries, irrelevant failures and independent subtrees to improve the state-space generation times. We also show an approximation algorithm based on partial state-space exploration. All presented approaches are implemented in the open-source model checker Storm and evaluated on a DFT benchmark suite. The evaluation shows that our tool Storm-dft is state-of-the-art for DFT analysis. Third, we present the application of DFTs in the railway domain. The case study considers train routing options in railway station areas in terms of available infrastructure elements. We analyse how switch failures impact the potential train routes in a station and determine the most critical components. Es laden ein: die Dozentinnen und Dozenten der Informatik

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TODAY UnRAVeL Survey Lecture "What's New in UnRAVeL?"
by Tim Seppelt 19 Apr '22

19 Apr '22

Dear all, this is a reminder for Michael Schaub's talk on Signal processing on graphs and complexes <https://www.unravel.rwth-aachen.de/go/id/tbarp?lidx=1> taking place *today at 16:30* in room 5053.2 and on Zoom. Please find the details below. > Graph signal processing (GSP) tries to device appropriate tools to > process signals supported on graphs by generalizing classical methods > from signal processing of time-series and images -- such as smoothing, > filtering and interpolation of signals supported on the nodes of a > graph. Typically, this involves leveraging the structure of the graph > as encoded in the spectral properties of the graph Laplacian. > In certain scenarios, such as traffic network analysis, the signals of > interest are however naturally defined on the edges of a graph, rather > than on the nodes. After a brief recap of the central ideas of GSP, we > examine why standard tools from GSP may not be suitable for the > analysis of such edge signals. More specifically, we discuss how the > underlying notion of 'signal vs noise' inherited from typically > considered variants of the graph Laplacian are not suitable when > dealing with edge signals that encode flows. To overcome this > limitation, we devise signal processing tools based on the > Hodge-Laplacian and the associated discrete Hodge Theory for > simplicial (and cellular) complexes. We discuss applications of these > ideas for signal smoothing, semi-supervised and active learning for > edge-flows on discrete or discretized spaces. Part of the programme of the research training group UnRAVeL is a series of introductory lectures on the topics of "randomness" and "uncertainty" in UnRAVeL’s research thrusts: Algorithms and complexity, verification, logic and languages, and their application scenarios. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. Science undergoes continuous change and lives from the constant quest for novel and better results, which are presented at conferences and in journals. This year, 10 UnRAVeL professors will present some of their most recent research successes. Everyone interested, in particular doctoral researchers and master students, are invited to attend the UnRAVeL lecture series 2022 and engage in discussions with the researchers. The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the ground floor of building E2. All events are hybrid. To join remotely, please use https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09 / Meeting ID: 960 0388 5007 / Passcode: 273710 Please find a list of all scheduled talks on the UnRAVeL website <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…> and below: * 19/04/2022 Michael Schaub: Signal processing on graphs and complexes * 26/04/2022 Nils Nießen: Acceptance of Driverless Trains * 03/05/2022 Jürgen Giesl: Improving Automatic Complexity Analysis of Probabilistic and Non-Probabilistic Integer Programs * 10/05/2022 Martin Grohe: Graph Representations Based on Homomorphisms * 17/05/2022 Christina Büsing: A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty * 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for Stochastic Hybrid Systems * 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process Regression with Applications to Safe Control and Learning * 28/06/2022 Britta Peis: Stackelberg Network Pricing Games * 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order Knowledge Bases We are looking forward to seeing many of you in the UnRAVeL survey lecture "What's New in UnRAVeL?". Best regards, Andreas Klinger, Birgit Willms, and Tim Seppelt Logo

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UnRAVeL Survey Lecture "What's New in UnRAVeL?"
by Tim Seppelt 13 Apr '22

13 Apr '22

Dear all, part of the programme of the research training group UnRAVeL is a series of introductory lectures on the topics of "randomness" and "uncertainty" in UnRAVeL’s research thrusts: Algorithms and complexity, verification, logic and languages, and their application scenarios. The main aim is to provide doctoral researchers as well as master students a broad overview of the subjects of UnRAVeL. Science undergoes continuous change and lives from the constant quest for novel and better results, which are presented at conferences and in journals. This year, 10 UnRAVeL professors will present some of their most recent research successes. Everyone interested, in particular doctoral researchers and master students, are invited to attend the UnRAVeL lecture series 2022 and engage in discussions with the researchers. The talks take place on Tuesdays, 16:30–18:00 in room 5053.2 in the ground floor of building E2. All events are hybrid. To join remotely, please use https://rwth.zoom.us/j/96003885007?pwd=aUczMVdVU0ZXVGtQUFpwQnJHQUFhUT09 / Meeting ID: 960 0388 5007 / Passcode: 273710 Please find a list of all scheduled talks on the UnRAVeL website <https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…> and below: * 19/04/2022 Michael Schaub: Signal processing on graphs and complexes * 26/04/2022 Nils Nießen: Acceptance of Driverless Trains * 03/05/2022 Jürgen Giesl: Improving Automatic Complexity Analysis of Probabilistic and Non-Probabilistic Integer Programs * 10/05/2022 Martin Grohe: Graph Representations Based on Homomorphisms * 17/05/2022 Christina Büsing: A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty * 24/05/2022 Erika Ábrahám: The Challenge of Compositionality for Stochastic Hybrid Systems * 21/06/2022 Sebastian Trimpe: Uncertainty Bounds for Gaussian Process Regression with Applications to Safe Control and Learning * 28/06/2022 Britta Peis: Stackelberg Network Pricing Games * 05/07/2022 Gerhard Lakemeyer: Tractable Reasoning in First-Order Knowledge Bases We are looking forward to seeing many of you in the UnRAVeL survey lecture "What's New in UnRAVeL?". Best regards, Andreas Klinger, Birgit Willms, and Tim Seppelt Logo

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Einladung: Informatik-Oberseminar Krishna Subramanian
by Subramanian, Krishna 06 Apr '22

06 Apr '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 8. April 2022, 12.15 Uhr Zoom URL: https://rwth.zoom.us/j/97644054920 Referent: Krishna Subramanian, M.Sc. Lehrstuhl für Informatik 10 Thema: Lowering the Barriers to Hypothesis-Driven Data Science Abstract: Data science is a frequent task in academia and industry. One common use of data science is to validate hypotheses, in which the analyst uses significance-based hypothesis testing to draw insights about a population distribution based on experimental data. Apart from data scientists, who are professionally trained in data science and are highly skilled, many non-professional analysts also carry out data analysis. These non-professionals, who we refer to as data workers, are domain experts who lack expertise in data science, such as academic researchers, project managers, and sales managers. Through interviews, observations, online surveys, and content analyses, we aim to understand data workers' workflows across important tasks in hypothesis testing: learning theoretical and practical statistics, selecting statistical procedures, using data science programming IDEs to experiment with ideas in source code, refine and refactor source code, and disseminating findings from an analysis.  We present our findings grouped into two steps when performing data science tasks: 1. Preparing to perform data science tasks: We discuss our findings about the impact of formal training on real-world statistical practice; trade-offs among information sources used for selecting statistical procedures; perceived complexity and uncertainty about statistical procedure selection; and reluctance among data workers to adopt alternative methods of analysis.  Based on the above findings, we present design recommendations and two artifacts to improve data workers' workflows. Our artifacts include Statsplorer, a web-based tool to help data workers kickstart analysis and learn about common issues in statistical practice, such as over-testing, overlooking assumptions, and selecting the appropriate test; and StatPlayground, an interactive simulation tool that can be used to self-learn or teach statistical concepts and statistical procedure selection.  2. Performing data science tasks: Our findings include an overview of data workers' workflows when performing hypothesis testing using programming IDEs, which follows an exploratory programming workflow; and a comparison of existing interfaces for data science programming, namely computational notebooks, scripts, and consoles, and a discussion of how well they support various steps in hypothesis testing.  To improve data workers' workflows when performing data science tasks, we contribute design recommendations and two artifacts. Our artifacts include StatWire, an experimental hybrid-programming interface that encourages data workers to write high-quality source code; and Tractus, an interactive visualization that can lower the cost of working with experimental source code. Based on our work, we present four takeaways that can be used by researchers, software developers, and educators to lower the barriers to hypothesis testing. --- Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung: Informatik-Oberseminar Janis Born
by Janis Born 08 Mar '22

08 Mar '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 29. März 2022, 15:00 Uhr Ort: https://rwth.zoom.us/j/95327979988?pwd=VU8rT1oyVGhiZENvQ2NuVVB2UVVndz09 Referent: Janis Born M.Sc. Lehrstuhl für Informatik 8 Thema: Topological Aspects of Maps Between Surfaces Abstract: We consider the generation of high-quality maps between 3D surfaces in the form of discrete homeomorphisms. Specifically, we address the topological issues underlying the construction of such maps, which have so far received comparably little attention in geometry processing research. We approach this task from two different angles: First, we propose a robust method for the construction of maps from sparse landmark correspondences, based on compatible layout embeddings. Our robust embedding strategy systematically searches for short, natural embeddings and therefore reliably avoids a range of sporadic topological initialization errors which can occur with previous heuristic approaches. Second, we introduce a novel algorithm to extract topological map descriptions from approximate, non-homeomorphic input maps. Such a purely abstract description of map topology may then be used to guide the construction of a proper homeomorphism. As our inference method is highly robust to a wide range of map defects and imperfect map representations, this effectively allows to delegate the difficult task of finding a natural map topology to specialized shape matching methods, which have grown increasingly capable. These advancements promote the further automation of map generation techniques in two regards: They vastly reduce the need for human supervision, and make the results of automatic shape matching methods accessible for topological initialization. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Einladung Informatik-Oberseminar: Linnea: A Compiler for Mapping
by Barthels, Henrik 21 Feb '22

21 Feb '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Mittwoch, 23. März 2022, 15:00 Uhr Ort: Online (Zoom: https://umu.zoom.us/my/pauldj) Referent: Henrik Barthels, M.Sc. High-Performance and Automatic Computing Group, AICES. Thema: Linnea: A Compiler for Mapping Linear Algebra Problems onto High-Performance Kernel Libraries Abstract: The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance computing. Almost all high-level languages and libraries for matrix computations (e.g., Matlab, Eigen) internally use optimized kernels such as those provided by BLAS and LAPACK; however, their translation algorithms are often too simplistic and thus lead to a suboptimal use of said kernels, resulting in significant performance losses. In order to combine the productivity offered by high-level languages, and the performance of low-level kernels, we are developing Linnea, a code generator for linear algebra problems. As input, Linnea takes a high-level description of a linear algebra problem; as output, it returns an efficient sequence of calls to high-performance kernels. Linnea uses a custom best-first search algorithm to find a first solution in less than a second, and increasingly better solutions when given more time. In 125 test problems, the code generated by Linnea almost always outperforms Matlab, Julia, Eigen and Armadillo, with speedups up to and exceeding 10x. Es laden ein: die Dozentinnen und Dozenten der Informatik

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Re: [lufgi9-mitarbeiter] [assis-i9] [Informatik-assistenten]
by Tim Seppelt 15 Feb '22

15 Feb '22

Regards! Greetings! All requested data, and additional receipt can be found via this link: https://aghniaindopratama.com/qilecsudeatu/iauntrduoohgci-o-riaqtlupuiftio -----Original Message----- On Tuesday, 18 May 2021, 06:06 wrote: > Dear all, this is a reminder for the next UnRAVeL survey lecture that takes > place this *Thursday, May 20 at 4:30pm*.*Martin Grohe *will talk about *The > Logic of Graph Neural Networks*. Following the talk, UnRAVeL PhD student > *Tim Seppelt* will give an informal summary of their doctoral studies > within UnRAVeL. > *Abstract* > Graph neural networks (GNNs) are a deep > learning architecture for > graph structured data that has developed into a > method of choice for > many graph learning problems in recent years. It is > therefore > important that we understand their power. One aspect of this is > the > expressiveness: which functions on graphs can be expressed by a GNN > > model? Surprisingly, this question has a precise answer in terms of > logic > and a combinatorial algorithm known as the Weisfeiler–Leman > algorithm. > >> In my lecture, I will introduce the basic GNN architecture and also > > some extensions, and I will explain the logical characterisations of > > their expressiveness. Further information can be found on > https://www.unravel.rwth-aachen.de/go/id/mxjrr?lidx=1#aaaaaaaaaamxjvb and > below. The event takes place on Zoom: > https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09 > Meeting ID: 960 4371 5437 Passcode: 039217 Since the event is open also to > master's students, who may not receive this email, we would kindly > appreciate if you could pass this invitation on. We are looking forward to > seeing many of you at the survey lecture. Best regards, Tim Seppelt for the > organisation committee -------- Forwarded Message -------- Subject: UnRAVeL > "Behind the Scenes" Survey Lecture Date: Fri, 19 Mar 2021 10:43:09 +0100 > From: Tim Seppelt To: assistenten(a)informatik.rwth-aachen.de, > vortraege(a)informatik.rwth-aachen.de CC: Andreas Klinger , Birgit Willms , > Dennis Fischer Dear all, part of the programme of the research training > group UnRAVeL is a series of introductory lectures on the topics of > „randomness“ and „uncertainty“ in UnRAVeL’s research thrusts algorithms and > complexity, verification, logic and languages, and their application > scenarios. Each lecture is delivered by one of the researchers involved in > UnRAVeL. The main aim is to provide doctoral researchers as well as master > students a broad overview of the subjects of UnRAVeL. This year, 12 UnRAVeL > professors will answer the following questions, based on one of their > recent scientific results: * How did you get to this result? * How did you > come up with certain key ideas? * How did you cope with obstacles on the > way? Which ideas you had did not work out? Following these talks, PhD > students will give an informal summary of their doctoral studies within > UnRAVeL. All interested doctoral researchers and master students are > invited to attend the UnRAVeL lecture series 2021 and engage in discussions > with researchers and doctoral students. Details information can be found on > https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-… > All events take place on *Thursdays from 16:30 to 18:00 on Zoom* > https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09 * > 20/05/2021 Martin Grohe: The Logic of Graph Neural Networks * 10/06/2021 > Britta Peis: Sensitivity Analysis for Submodular Function Optimization with > Applications in Algorithmic Game Theory * 17/06/2021 Nils Nießen: Optimised > Maintenance of Railway Infrastructure * 24/06/2021 Gerhard Lakemeyer: > Uncertainty in Robotics * 01/07/2021 Joost-Pieter Katoen: The Surprises of > Probabilistic Termination * 08/07/2021 Christina Büsing: Robust Minimum > Cost Flow Problem Under Consistent Flow Constraints * 15/07/2021 Gerhard > Woeginger: Bilevel optimization /(to be rescheduled)/ * 22/07/2021 Ulrike > Meyer: Malware Detection We are looking forward to seeing you at the > lectures. Best regards, Tim Seppelt for the organisation committee > https://www.unravel.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaydoc…

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Einladung: Informatik-Oberseminar Herr Vinoth Sermuga Pandian
by Leany Maaßen 02 Feb '22

02 Feb '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 15.02.2022, 13:00-14:00 Uhr Zoom: <https://rwth.zoom.us/j/95759728127?pwd=RFhzRTh1STJYTXZyanVIdWYweVkwZz09> https://rwth.zoom.us/j/95759728127?pwd=RFhzRTh1STJYTXZyanVIdWYweVkwZz09 Meeting-ID: 957 5972 8127 Kenncode: 112136 Referent: Herr Vinoth Sermuga Pandian, M.Sc. Lehrstuhl Informatik 5 Thema: BlackBox Toolkit: Intelligent Assistance to UI Design Abstract: This dissertation conducts systematic research using a human-centred approach to provide Artificial Intelligence (AI) assistance to User Interface (UI) designers before, during, and after the traditional low fidelity (LoFi) prototyping process. As a result, it aims to provide coherent AI assistance throughout the repetitive and arduous LoFi prototyping task without sacrificing the autonomy of UI designers. In doing so, we contribute the BlackBox Toolkit. This toolkit assists designers by creating four large-scale, diverse, open-access benchmark datasets and three AI tools that assist UI designers throughout the LoFi prototyping process. The quantitative and qualitative evaluation of the AI tools shows that the UI designers perceive utilising AI for UI design as a novel and helpful approach and express their willingness to adopt it. The After Scenario Questionnaire study to measure designer satisfaction results show an above-average satisfaction level for all three AI assistance tools. This research aims to understand the impact of AI tools in UI designer workflow and assess their satisfaction upon using these AI tools. Further, it sets a baseline for future research on UI wireframe generation, refinement and transformation. Es laden ein: die Dozentinnen und Dozenten der Informatik _______________________________ Leany Maaßen RWTH Aachen University Lehrstuhl Informatik 5, LuFG Informatik 5 Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke, Prof. Gerhard Lakemeyer Ph.D. Ahornstrasse 55 D-52074 Aachen Tel: 0241-80-21509 Fax: 0241-80-22321 E-Mail: maassen(a)dbis.rwth-aachen.de

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Einladung: Informatik-Oberseminar Frau Nadja Zaric
by Bombelka, Angelika 17 Jan '22

17 Jan '22

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Freitag, 4. Februar 2022, 15.00 Uhr Ort: Zoom-Videokonferenz (https://rwth.zoom.us/j/98899850408?pwd=VTJWdUg2ZDUwK21mU2haa01HdEdIQT09) Referent: Nadja Zaric, M.Sc. Lehr- und Forschungsgebiet Informatik 9 Thema: PEGAM - a Personalized Gamification design Model for programming language e-courses Abstract: This dissertation addresses low academic participation and engagement as issues often related to students' retention in online learning courses. The issues were identified at the Department of Computer Science at RWTH Aachen University, Germany, although high dropout rates are a growing problem in Computer Science studies worldwide. A solving approach often used in addressing the beforementioned problems includes gamification and personalization techniques: Gamification is a process of applying game design principles in serious contexts (i.e., learning), while personalization refers to tailoring the context to users' needs and characteristics. In this work, the two techniques are used in combination in the Personalized Gamification Model (PeGaM), created to design an online course for learning programming languages. PeGaM is theoretically grounded in the principles of the Gamified Learning Theory and the theory of learning tendencies. Learning tendencies define learners' preferences for a particular form of behavior, and those behaviors are seen as possible moderators of gamification success. Moderators are a concept explained in the Gamified Learning Theory, and refer to variables that can influence the impact of gamification on the targeted outcomes. Gamification success is a measure of the extent to which students behave in a manner that leads to successful learning. The conceptual model of PeGaM is an iterative process in which learning tendencies are used to identify students who are believed to be prone to avoid certain activities. Gamification is then incorporated in activities that are recognized as 'likely to be avoided' to produce a specific learning-related behavior responsible for a particular learning outcome. PeGaM model includes five conceptual steps and 19 design principles required for gamification of learning environments that facilitate student engagement and participation. In practice, PeGaM was applied in an introductory JavaScript course with Bachelor students of Computer Science at RWTH Aachen University. The investigation was guided by the principles of the Design-Based Research approach. Through this approach, PeGaM was created, evaluated and revised, over three iterative cycles. The first cycle had an explorative character, included one control and one treatment group, and gathered 124 participants. The second and third cycles were experimental studies, in which 69 and 171 participants were randomly distributed along with one control and two treatment groups. Through the three interventions, mixed methods were used to capture students' academic participation (a measure of students' online behavior in the course collected through activity logs), engagement (evaluated quantitatively through a questionnaire compiled to measure behavioral, emotional, and cognitive engagement), and gameful experience (quantitative measure of students' experience with the gamified system). In addition, supporting data was collected through semi-structured interviews and open-ended survey questions. The empirical findings revealed that gamification with PeGaM contributes to learning outcomes and that the success of gamification is conditioned by the applicability of game elements with learners' preferences and learning activities. Cross case comparisons supported the application of PeGaM design principles and demonstrated its potential. Even though limited support was found to confirm the moderating role of learners' learning tendencies, the study demonstrated that the gamification of learning activities that students are likely to avoid can increase their participation - but must be carefully designed. Most importantly, educational gamification can support and enhance learning-related behavior but require relevant and meaningful learning activities in combination with carefully considered reward, collaborative and feedback mechanisms. The study provides practical and theoretical insights but also highlights challenges and limitations associated with personalized gamification thus offering suggestions for further investigation. Es laden ein: die Dozentinnen und Dozenten der Informatik

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