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EVALUATING AN AI-BASED ADAPTIVE LEARNING SYSTEM: GOALS, METHODS AND INITIAL RESULTS
No full textThe aim of this paper is to describe the evaluation process and findings of an AI-based Adaptive Learning System for the Computer Science discipline at two different German universities and discuss an array of methods in regard to assessing such a system. The primary objectives have been twofold: firstly, to examine the reception of selected learning elements, which were conceptually outlined based on relevant literature, among the student body; and secondly, to investigate the efficacy of individualized adaptive learning paths. These paths were generated by employing a variety of algorithms to analyze students learning style tendencies, with a particular emphasis on adaptive navigational techniques. The used algorithms encompassed a modified version of a literature based adaptive mechanism, an Ant-Colony-Algorithm and a Genetic Algorithm, alongside a lecturer-recommended learning path for a non-adaptive comparison. While the system suggested suitable learning paths based on student data, it never forced the individuals to give up their self-directed learning. The evaluation criteria revolved around the evolution of student motivation, interest levels, and knowledge acquisition during the time they spent working in the system. The evaluation sought to facilitate comparative analyses and assess algorithmic fitness for proficient learning path generation. The methods included both quantitative and qualitative approaches to gather data, seeking to strike a balance between being student-friendly and scientifically informative. They ranged from Likert Scale self-assessments to screen and video observations with retrospective interviews. Since the purpose of adaptive learning systems is intertwined with personalized learning it seems imperative to already take the preferences and opinions of students into account while the system is still in development. This complexity underscores the challenge of evaluating such systems, as significant constraints on student choice - though simplifying evaluation - directly oppose the ethos of individualized, self-directed learning. Initial findings suggest that the underlying theoretical considerations on sequencing and structuring of learning elements are confirmed, coupled with providing adequate flexibility to meet diverse learning needs. Cross-site evaluation of the literature-based learning elements indicated a high comprehensibility and positive student ratings. While significant positive trends were observed regarding knowledge acquisition, they cannot be definitively attributed to a specific method of learning path generation. Motivation and interest analyses show no significant differences among learning path types, albeit heavily limited by sample size. Similarly, emotion measurements, though limited, hint at positive impacts from HASKI system use. Despite limitations, early indications suggest student acceptance and potential effectiveness of learning paths, highlighting the need for larger sample sizes for validation and expansion. Ensuring alignment with student needs and user-friendly design are crucial considerations
Effort-cost decision-making associated with negative symptoms in schizophrenia and bipolar disorder
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Birefringence in Injection-Molded Cyclic Olefin Copolymer Substrates and Its Impact on Integrated Photonic Structures
No full textThis contribution quantifies the birefringence within injection-molded cyclic olefin copolymer plates and discusses its impact on the mechanical properties of the plates. It also focuses on the impact of birefringence on integrated waveguides and Bragg gratings and provides fabrication guidelines for such structures. The anisotropy in all three dimensions of the workpiece is examined by means of polarimetry and a prism coupler. It is found that the birefringence is inhomogenously distributed within the workpieces, whereas the maximum birefringence not only varies locally, but also depends on the observation direction. Overall, a maximum birefringence of 10 × 10−4 is found at the plate’s surface near the injection gate. The anisotropy then reduces exponentially towards the center of the workpiece and saturates at 1.8 × 10−4, in a depth of 0.4 mm. Thus, the birefringence strongly affects near-surface photonic structures. It is found that, depending on their orientation and the local birefringence of the substrate, waveguides and Bragg gratings fabricated with comparable parameters behave completely differently in terms of polarization-dependent optical attenuation, cross-sectional intensity distribution and Bragg reflection signal. For example, the support of the TM mode can vary between total loss and an optical attenuation of 0.9 dB × cm−1. In consequence, this study underlines the importance of quantifying the birefringent state of an injection-molded cyclic olefin copolymer workpiece if it is supposed to serve as a substrate for integrated photonic structures. The study furthermore demonstrates that birefringence effects can be omitted by burying the photonic structures deeper into the volume of the thermoplastic
Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events
No full textThis paper investigates the ability of autonomous driving systems to predict outcomes by
considering human factors like gender, age, and driving experience, particularly in the context of
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to
make plausible deductions, handle conflicting data, and adjust their responses in real-time during
safety-critical situations. A foundational dataset, which encompasses various driving scenarios
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep
learning model incorporates personalized cognitive agents for each driver, considering their distinct
preferences, characteristics, and requirements. This personalized approach aims to enhance the
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall
performance within autonomous driving systems. To describe the causal relationship between external
factors like weather conditions and human factors, and safety-critical driver behaviors, various
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,
correlation analysis is employed to reveal relationships between different factors, helping to
identify the strength and direction of their impact on safety-critical driver behavior.
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical
events; cognitive vehicles
1. Introduction
Despite the increasing prevalence of vehicle automation, the persistently high number
of car crashes remains a concern. Safety-critical events in human-driven scenarios have
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating
human driving behavior is imperative to establish traffic baselines for mixed
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)
behavior [1,2], influence human driving behavior [3].
Car-following behavior, illustrating how a following vehicle responds to the lead
vehicle in the same lane, is a crucial aspect. Existing car-following models often make
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving
experience, gender, character, emotions, and sociological, psychological, and physiological
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of
car-following behavior, limiting model accuracy and applicability. In the development of
more realistic car-following models for mixed traffic, acknowledging the diversity among
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction
times, decision-making processes, and driving styles, the modeling of real-world
driving complexities can be improved. Simplifying drivers into a few categories overlooks
the richness and variety of their characteristics, prompting the need for a more comprehensive
approach to capture nuances within different driver profiles. To address these
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Ein Besucherexperiment zur astronomischen Röntgenoptik für das Deutsche Röntgen-Museum
No full textIm Rahmen eines gemeinsamen Entwicklungsprojekts entsteht ein neues Besucherexperiment zur astronomischen Röntgenoptik, das im Museumslabor RöLab im Deutschen Röntgen-Museum aufgebaut werden soll. Das Ziel des interaktiven Exponats ist es, Begeisterung für die faszinierende Welt der Röntgenastronomie zu wecken und das Berufsbild eines Astronomen zu vermitteln
Work-in-Progress: Input Support for the Formulation of Learning Outcomes in STEM
No full textThe heightened emphasis on student-centered learning in STEM degree programs highlights the importance of precise formulation of learning outcomes. The “AdLer Input Support for the Formulation of Learning Outcomes” offers a valuable solution to meet this challenge
Analysis of Driving Behavior in Adverse Weather Conditions
No full textThis paper discusses the impact of Connected Cooperative and Automated Mobility (CCAM) on safety-critical events. The replacement of human drivers by autonomous vehicles (AVs) is promising improved traffic efficiency and reduction of car- crashes to zero using a baseline network traffic. Predicting driving behavior during car-following has been crucial for enhancing road safety while developing advanced driver assistance systems with adaptive cruise control. Human factors significantly influence the driving behavior of a vehicle. Thus, understanding the causal relations between human factors and driving behavior is essential for accurate prediction of vehicle behavior. This is important when autonomous vehicles are expected to behave (cooperatively, according to traffic rules and good praxis) in a human predictable manner, while driving in mixed traffic, involving autonomous, automated, and human driven vehicles. In this paper, we propose a methodology that combines convolutional neural networks (CNNs) with human factors analysis to predict driving behavior during car-following under adverse weather conditions (AWCs)
Strahlungsmessungen beim Erstflug des Stratosphärenballon-Experiments ASTRABAX
No full textMit dem Aschaffenburger Stratosphären-Ballon-Experiment („ASTRABAX“) werden Experimente zu Material- und Biowissenschaften unter extremer Strahlungsexposition in großer Höhe durchgeführt. Der erste von drei geplanten Ballonflügen startete Mitte Oktober 2024 im norddeutschen Bad Pyrmont und erreichte eine Flughöhe von fast 35 km. Der Schwerpunkt der
physikalischen Experimente lag auf der Vermessung des UV-C-Spektralbereichs mit einem Miniatur-UV-VIS-Spektrometer und der Strahlungsdosimetrie mit einem Geigerzähler. In einem zweiten Satz von Experimenten wurde die Wirkung der Bestrahlung auf Polydopaminproben untersucht, welche derzeit für Beschichtungen von astronomischen Röntgenspiegel entwickelt werden. Die Ballongondel enthielt auch Proben biologischer Zellen, die hier gleichzeitig einer niedrig dosierten Strahlung von hochenergetischen Partikeln, Gammastrahlen und UV-Strahlung ausgesetzt wurden. Experimente unter solchen natürlichen stratosphärischen Bedingungen sind für atmosphärische Höhenflüge, bemannte Raumfahrtmissionen, vergleichbare Expositionen bei anderen Objekten des Sonnensystems und die strahlenbiologische Forschung von Bedeutung.Poste
Monolithic Wideband Air-Coupled Ultrasonic Transducer Based on Additively Manufactured Ferroelectrets
No full textAir-coupled ultrasonic transducers are widely used in non-destructive testing, acoustical sonar systems, and biomedical imaging. These applications require transducers that operate effectively across a broad acoustic frequency spectrum, offer adaptable geometric designs, and increasingly incorporate eco-friendly materials. In this work, we present a monolithic, 3D-printed air-coupled ultrasonic transducer based on ferroelectrets (FEs) and fabricated from biocompatible polylactic acid (PLA). We evaluated the transducer’s acoustical performance by measuring the surface velocity of its active area using laser Doppler vibrometry and assessed its robustness during continuous operation over a 19-day period. Additionally, we measured the sound pressure level (SPL) and wideband characteristics in an anechoic chamber across excitation frequencies from 1kHz to 100kHz. At a resonance frequency of 33kHz, our transducer achieved an SPL of 94.3dB and surface velocities up to 37mm/s. The measured bandwidth of 65.2kHz at the -6dB threshold corresponds to a fractional bandwidth of 189%. The observed exponential decay of the surface velocity, stabilizing at 15% of its initial amplitude, aligns with the isothermal surface potential decay typically observed in FE films made from PLA. These results demonstrate the effectiveness of the transducer, which features an adaptable backplate for tuning acoustic properties. The low-cost transducer, manufactured from biocompatible PLA, is particularly suited for imaging and biomedical applications furthering green electronics