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Institute
- Institut für Informatik und Computational Science (1109) (remove)
Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.
The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays.
In this paper, we present a study comparing the depth to diameter (d/D) ratio of small simple craters (200-1000 m) of an area between -88.5 degrees to -90 degrees latitude at the lunar south pole containing Permanent Shadowed Regions (PSRs) versus craters without PSRs. As PSRs can reach temperatures of 110 K and are capable of harboring volatiles, especially water ice, we analyzed the relationship of depth versus diameter ratios and its possible implications for harboring water ice. Variations in the d/D ratios can also be caused by other processes such as degradation, isostatic adjustment, or differences in surface properties. The conducted d/D ratio analysis suggests that a differentiation between craters containing PSRs versus craters without PSRs occurs. Thus, a possible direct relation between d/D ratio, PSRs, and water ice harboring might exist. Our results suggest that differences in the target's surface properties may explain the obtained results. The resulting d/D ratios of craters with PSRs can help to select target areas for future In-Situ Resource Utilization (ISRU) missions.
Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.
Answer set planning
(2022)
Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.
Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Methods Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient >= 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient >= 0.75). Results The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses >= 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features.
Programs are often subjected to significant optimizing and parallelizing transformations based on extensive dependence analysis. Formal validation of such transformations needs modelling paradigms which can capture both control and data dependences in the program vividly. Being value-based with an inherent scope of capturing parallelism, the untimed coloured Petri net (CPN) models, reported in the literature, fit the bill well; accordingly, they are likely to be more convenient as the intermediate representations (IRs) of both the source and the transformed codes for translation validation than strictly sequential variable-based IRs like sequential control flow graphs (CFGs). In this work, an efficient path-based equivalence checking method for CPN models of programs on integers is presented. Extensive experimentation has been carried out on several sequential and parallel examples. Complexity and correctness issues have been treated rigorously for the method.
The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.
We present a method employing Answer Set Programming in combination with Approximate Model Counting for fast and accurate calculation of error propagation probabilities in digital circuits. By an efficient problem encoding, we achieve an input data format similar to a Verilog netlist so that extensive preprocessing is avoided. By a tight interconnection of our application with the underlying solver, we avoid iterating over fault sites and reduce calls to the solver. Several circuits were analyzed with varying numbers of considered cycles and different degrees of approximation. Our experiments show, that the runtime can be reduced by approximation by a factor of 91, whereas the error compared to the exact result is below 1%.
Instruments for measuring the absorbed dose and dose rate under radiation exposure, known as radiation dosimeters, are indispensable in space missions. They are composed of radiation sensors that generate current or voltage response when exposed to ionizing radiation, and processing electronics for computing the absorbed dose and dose rate. Among a wide range of existing radiation sensors, the Radiation Sensitive Field Effect Transistors (RADFETs) have unique advantages for absorbed dose measurement, and a proven record of successful exploitation in space missions. It has been shown that the RADFETs may be also used for the dose rate monitoring. In that regard, we propose a unique design concept that supports the simultaneous operation of a single RADFET as absorbed dose and dose rate monitor. This enables to reduce the cost of implementation, since the need for other types of radiation sensors can be minimized or eliminated. For processing the RADFET's response we propose a readout system composed of analog signal conditioner (ASC) and a self-adaptive multiprocessing system-on-chip (MPSoC). The soft error rate of MPSoC is monitored in real time with embedded sensors, allowing the autonomous switching between three operating modes (high-performance, de-stress and fault-tolerant), according to the application requirements and radiation conditions.
The radiation-sensitive field-effect transistors (RADFETs) with an oxide thickness of 400 nm are irradiated with gate voltages of 2, 4 and 6 V, and without gate voltage.
A detailed analysis of the mechanisms responsible for the creation of traps during irradiation is performed.
The creation of the traps in the oxide, near and at the silicon/silicon-dioxide (Si/SiO2) interface during irradiation is modelled very well. This modelling can also be used for other MOS transistors containing SiO2.
The behaviour of radiation traps during postirradiation annealing is analysed, and the corresponding functions for their modelling are obtained. The switching traps (STs) do not have significant influence on threshold voltage shift, and two radiation-induced trap types fit the fixed traps (FTs) very well. The fading does not depend on the positive gate voltage applied during irradiation, but it is twice lower in case there is no gate voltage.
A new dosimetric parameter, called the Golden Ratio (GR), is proposed, which represents the ratio between the threshold voltage shift after irradiation and fading after spontaneous annealing. This parameter can be useful for comparing MOS dosimeters.
Das Promotionsvorhaben verfolgt das Ziel, die Zuverlässigkeit der Datenspeicherung und die Speicherdichte von neu entwickelten Speichern (Emerging Memories) mit Multi-Level-Speicherzellen zu verbessern bzw. zu erhöhen. Hierfür werden Codes zur Erkennung von unidirektionalen Fehlern analysiert, modifiziert und neu entwickelt, um sie innerhalb der neuen Speicher anwenden zu können. Der Fokus liegt dabei auf sog. Berger-Codes und m-aus-n-Codes. Da Multi-Level-Speicherzellen nicht mehr binär, sondern mit mehreren Leveln arbeiten, können bisher verwendete Codes nicht mehr verwendet werden, bzw. müssen entsprechend angepasst werden. Auf Basis der Berger-Codes und m-aus-n-Codes werden in dieser Arbeit neue Codes abgeleitet, welche in der Lage sind, Daten auch in mehrwertigen Systemen zu schützen.
Mit der Covid-19-Pandemie hat die Digitalisierung an Hochschulen weitere Bedeutung erlangt. Insbesondere dem Einsatz digitaler Medien in Lehre und Studium galt großes Augenmerk. Das legt die Hoffnung nahe, dass die Digitalisierung durch das Virus einen Schub erfahren und die Hochschulen dauerhaft verändert hat. Der Beitrag geht am Beispiel der Universität Potsdam der Frage nach, welcher Natur diese Veränderungen waren – ausgehend sowohl von den unternommenen Maßnahmen als auch von den erzielten Resultaten – und inwiefern sie von Dauer sind. Dabei werden förderliche und hemmende Faktoren identifiziert, die in Empfehlungen für weitere Digitalisierungsvorhaben übersetzt werden.
In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver’s interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth.
In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that
allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth.
Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.
Zum Einfluss von Adaptivität auf die Wahrnehmung von Komplexität in der Mensch-Technik-Interaktion
(2021)
Wir leben in einer Gesellschaft, die von einem stetigen Wunsch nach Innovation und Fortschritt geprägt ist. Folgen dieses Wunsches sind die immer weiter fortschreitende Digitalisierung und informatische Vernetzung aller Lebensbereiche, die so zu immer komplexeren sozio-technischen Systemen führen. Ziele dieser Systeme sind u. a. die Unterstützung von Menschen, die Verbesserung ihrer Lebenssituation oder Lebensqualität oder die Erweiterung menschlicher Möglichkeiten. Doch haben neue komplexe technische Systeme nicht nur positive soziale und gesellschaftliche Effekte. Oft gibt es unerwünschte Nebeneffekte, die erst im Gebrauch sichtbar werden, und sowohl Konstrukteur*innen als auch Nutzer*innen komplexer vernetzter Technologien fühlen sich oft orientierungslos. Die Folgen können von sinkender Akzeptanz bis hin zum kompletten Verlust des Vertrauens in vernetze Softwaresysteme reichen. Da komplexe Anwendungen, und damit auch immer komplexere Mensch-Technik-Interaktionen, immer mehr an Relevanz gewinnen, ist es umso wichtiger, wieder Orientierung zu finden. Dazu müssen wir zuerst diejenigen Elemente identifizieren, die in der Interaktion mit vernetzten sozio-technischen Systemen zu Komplexität beitragen und somit Orientierungsbedarf hervorrufen.
Mit dieser Arbeit soll ein Beitrag geleistet werden, um ein strukturiertes Reflektieren über die Komplexität vernetzter sozio-technischer Systeme im gesamten Konstruktionsprozess zu ermöglichen. Dazu wird zuerst eine Definition von Komplexität und komplexen Systemen erarbeitet, die über das informatische Verständnis von Komplexität (also der Kompliziertheit von Problemen, Algorithmen oder Daten) hinausgeht. Im Vordergrund soll vielmehr die sozio-technische Interaktion mit und in komplexen vernetzten Systemen stehen. Basierend auf dieser Definition wird dann ein Analysewerkzeug entwickelt, welches es ermöglicht, die Komplexität in der Interaktion mit sozio-technischen Systemen sichtbar und beschreibbar zu machen.
Ein Bereich, in dem vernetzte sozio-technische Systeme zunehmenden Einzug finden, ist jener digitaler Bildungstechnologien. Besonders adaptiven Bildungstechnologien wurde in den letzten Jahrzehnten ein großes Potential zugeschrieben. Zwei adaptive Lehr- bzw. Trainingssysteme sollen deshalb exemplarisch mit dem in dieser Arbeit entwickelten Analysewerkzeug untersucht werden. Hierbei wird ein besonderes Augenmerkt auf den Einfluss von Adaptivität auf die Komplexität von Mensch-Technik-Interaktionssituationen gelegt. In empirischen Untersuchungen werden die Erfahrungen von Konstrukteur*innen und Nutzer*innen jener adaptiver Systeme untersucht, um so die entscheidenden Kriterien für Komplexität ermitteln zu können. Auf diese Weise können zum einen wiederkehrende Orientierungsfragen bei der Entwicklung adaptiver Bildungstechnologien aufgedeckt werden. Zum anderen werden als komplex wahrgenommene Interaktionssituationen identifiziert. An diesen Situationen kann gezeigt werden, wo aufgrund der Komplexität des Systems die etablierten Alltagsroutinen von Nutzenden nicht mehr ausreichen, um die Folgen der Interaktion mit dem System vollständig erfassen zu können. Dieses Wissen kann sowohl Konstrukteur*innen als auch Nutzer*innen helfen, in Zukunft besser mit der inhärenten Komplexität moderner Bildungstechnologien umzugehen.
Forschendes Lernen und die digitale Transformation sind zwei der wichtigsten Einflüsse auf die Entwicklung der Hochschuldidaktik im deutschprachigen Raum. Während das forschende Lernen als normative Theorie das sollen beschreibt, geben die digitalen Werkzeuge, alte wie neue, das können in vielen Bereichen vor.
In der vorliegenden Arbeit wird ein Prozessmodell aufgestellt, was den Versuch unternimmt, das forschende Lernen hinsichtlich interaktiver, gruppenbasierter Prozesse zu systematisieren. Basierend auf dem entwickelten Modell wurde ein Softwareprototyp implementiert, der den gesamten Forschungsprozess begleiten kann. Dabei werden Gruppenformation, Feedback- und Reflexionsprozesse und das Peer Assessment mit Bildungstechnologien unterstützt. Die Entwicklungen wurden in einem qualitativen Experiment eingesetzt, um Systemwissen über die Möglichkeiten und Grenzen der digitalen Unterstützung von forschendem Lernen zu gewinnen.
We study the concept of reversibility in connection with parallel communicating systems of finite automata (PCFA in short). We define the notion of reversibility in the case of PCFA (also covering the non-deterministic case) and discuss the relationship of the reversibility of the systems and the reversibility of its components. We show that a system can be reversible with non-reversible components, and the other way around, the reversibility of the components does not necessarily imply the reversibility of the system as a whole. We also investigate the computational power of deterministic centralized reversible PCFA. We show that these very simple types of PCFA (returning or non-returning) can recognize regular languages which cannot be accepted by reversible (deterministic) finite automata, and that they can even accept languages that are not context-free. We also separate the deterministic and non-deterministic variants in the case of systems with non-returning communication. We show that there are languages accepted by non-deterministic centralized PCFA, which cannot be recognized by any deterministic variant of the same type.
Digitalisierung ermöglicht es uns, mit Partnern (z.B. Unternehmen, Institutionen) in einer IT-unterstützten Umgebung zu interagieren und Tätigkeiten auszuführen, die vormals manuell erledigt wurden. Ein Ziel der Digitalisierung ist dabei, Dienstleistungen unterschiedlicher fachlicher Domänen zu Prozessen zu kombinieren und vielen Nutzergruppen bedarfsgerecht zugänglich zu machen. Hierzu stellen Anbieter technische Dienste bereit, die in unterschiedliche Anwendungen integriert werden können.
Die Digitalisierung stellt die Anwendungsentwicklung vor neue Herausforderungen. Ein Aspekt ist die bedarfsgerechte Anbindung von Nutzern an Dienste. Zur Interaktion menschlicher Nutzer mit den Diensten werden Benutzungsschnittstellen benötigt, die auf deren Bedürfnisse zugeschnitten sind. Hierzu werden Varianten für spezifische Nutzergruppen (fachliche Varianten) und variierende Umgebungen (technische Varianten) benötigt. Zunehmend müssen diese mit Diensten anderer Anbieter kombiniert werden können, um domänenübergreifend Prozesse zu Anwendungen mit einem erhöhten Mehrwert für den Endnutzer zu verknüpfen (z.B. eine Flugbuchung mit einer optionalen Reiseversicherung).
Die Vielfältigkeit der Varianten lässt die Erstellung von Benutzungsschnittstellen komplex und die Ergebnisse sehr individuell erscheinen. Daher werden die Varianten in der Praxis vorwiegend manuell erstellt. Dies führt zur parallelen Entwicklung einer Vielzahl sehr ähnlicher Anwendungen, die nur geringes Potential zur Wiederverwendung besitzen. Die Folge sind hohe Aufwände bei Erstellung und Wartung. Dadurch wird häufig auf die Unterstützung kleiner Nutzerkreise mit speziellen Anforderungen verzichtet (z.B. Menschen mit physischen Einschränkungen), sodass diese weiterhin von der Digitalisierung ausgeschlossen bleiben.
Die Arbeit stellt eine konsistente Lösung für diese neuen Herausforderungen mit den Mitteln der modellgetriebenen Entwicklung vor. Sie präsentiert einen Ansatz zur Modellierung von Benutzungsschnittstellen, Varianten und Kompositionen und deren automatischer Generierung für digitale Dienste in einem verteilten Umfeld. Die Arbeit schafft eine Lösung zur Wiederverwendung und gemeinschaftlichen Nutzung von Benutzungsschnittstellen über Anbietergrenzen hinweg. Sie führt zu einer Infrastruktur, in der eine Vielzahl von Anbietern ihre Expertise in gemeinschaftliche Anwendungen einbringen können.
Die Beiträge bestehen im Einzelnen in Konzepten und Metamodellen zur Modellierung von Benutzungsschnittstellen, Varianten und Kompositionen sowie einem Verfahren zu deren vollständig automatisierten Transformation in funktionale Benutzungsschnittstellen. Zur Umsetzung der gemeinschaftlichen Nutzbarkeit werden diese ergänzt um eine universelle Repräsentation der Modelle, einer Methodik zur Anbindung unterschiedlicher Dienst-Anbieter sowie einer Architektur zur verteilten Nutzung der Artefakte und Verfahren in einer dienstorientierten Umgebung.
Der Ansatz bietet die Chance, unterschiedlichste Menschen bedarfsgerecht an der Digitalisierung teilhaben zu lassen. Damit setzt die Arbeit Impulse für zukünftige Methoden zur Anwendungserstellung in einem zunehmend vielfältigen Umfeld.
We introduce a new measure of descriptional complexity on finite automata, called the number of active states. Roughly speaking, the number of active states of an automaton A on input w counts the number of different states visited during the most economic computation of the automaton A for the word w. This concept generalizes to finite automata and regular languages in a straightforward way. We show that the number of active states of both finite automata and regular languages is computable, even with respect to nondeterministic finite automata. We further compare the number of active states to related measures for regular languages. In particular, we show incomparability to the radius of regular languages and that the difference between the number of active states and the total number of states needed in finite automata for a regular language can be of exponential order.
In vielen Studiengängen kommt es durch die oft heterogenen Vorkenntnisse in der Studieneingangsphase zu mangelnder Motivation durch Über- oder Unterforderung. Dieses Problem tritt auch in der musiktheoretischen Grundausbildung an Hochschulen auf. Durch Einsatz von Elementen, die aus dem Unterhaltungskontext geläufig sind, kann eine Steigerung der Motivation erreicht werden. Die Nutzung solcher Elemente wird als Gamification bezeichnet.
Das Ziel der vorliegenden Arbeit ist es, am Fallbeispiel der musiktheoretischen Grundausbildung zu analysieren, ob Lerngelegenheiten durch einen gamifizierten interaktiven Prototyp einer Lernumgebung unterstützt werden können. Dazu wird die folgende Forschungsfrage gestellt: Inwieweit wirkt Gamification auf die Motivation bei den Lernenden zur Beschäftigung mit dem Thema (musikalische) Funktionsanalyse?
Um die Forschungsfragen zu beantworten, wurde zunächst ein systematisches, theoriegeleitetes Vorgehensmodell zur Gamification von Lernumgebungen entwickelt und angewandt. Der so entstandene Prototyp wurde anschließend um alle Game-Design-Elemente reduziert und im Rahmen einer experimentellen Studie mit zwei unabhängigen Versuchsgruppen mit der gamifizierten Variante verglichen.
Die Untersuchung zeigte, dass die Gamification einer Lernanwendung nach dem entwickelten Vorgehensmodell grundsätzlich das Potenzial besitzt, manche Aspekte des Nutzungserlebnisses (UX) positiv zu beeinflussen. Insbesondere hatte die Gamification positive Effekte auf die Joy of Use und die Immersivität. Allerdings blieb das Ausmaß der beobachteten Effekte deutlich hinter den Erwartungen zurück, die auf Basis verschiedener Motivationstheorien getroffen wurden.
Daher erscheint Gamification besonders in außeruniversitären Kontexten vielversprechend, in denen der Fokus auf einer Erhöhung der Joy of Use oder einer Steigerung der Immersivität liegt. Allerdings lassen sich durch die Untersuchung neue Erkenntnisse zur emotionalen Wirkung von Gamification und zu einem systematischen Vorgehen bei der Gamification von Lernanwendungen herausstellen.
Weiterführende Forschung könnte an diese Erkenntnisse anknüpfen, indem sie die emotionale Wirkung von Gamification und deren Einfluss auf die Motivation näher untersucht. Darüber hinaus sollte sie Gamification auch aus einer entscheidungstheoretischen Perspektive betrachten und Analysemethoden entwickeln, mit denen entschieden werden kann, ob der Einsatz von Gamification zur Motivationssteigerung in einem spezifischen Anwendungsfall zielführend ist. Unter Verwendung des entwickelten Vorgehensmodells kann es sinnvoll sein, näher zu untersuchen, welche Faktoren insgesamt für das Gelingen einer Gamification-Maßnahme in Bildungskontexten entscheidend sind. Die Erkenntnisse einer solchen Untersuchung könnten entscheidend zur Verbesserung und Validierung des Vorgehensmodells beitragen.