arXiv subjects

Kai Marquardt

Publications and source records attributed to Kai Marquardt.

8 recordsLinked to original sources

arXiv2026

Measuring Computer Science Enthusiasm: A Questionnaire-Based Analysis of Age and Gender Effects on Students' Interest

This study examines how age and gender independently shape adolescents' interest in computer science (CS) education. Building on the Person-Object Theory of Interest (POI), we define enthusiasm as a short-term, activating response that combines positive affect, perceived relevance, and intention to re-engage. Because such enthusiasm can shift CS attitudes and engagement intentions even briefly, it offers a useful measure for short outreach activities. We developed a 28-item pre-post questionnaire to assess whether CS interventions raise enthusiasm, then applied it to more than 400 students (244 female, 187 male, aged 10-18) in CS courses. Contrary to the common assumption that early exposure secures lasting interest, we found a marked decline during early adolescence, especially among girls, along with wide variation in interest trajectories across ages. Exploratory factor analysis and ANOVA show that age predicts interest development more strongly than gender, and reveal specific developmental breakpoints. Although older students began with lower baseline attitudes, they showed the largest gains after the intervention, indicating that well-designed short activities can re-engage interest even later in adolescence. These results point to the need for CS education strategies that adapt to developmental stage rather than assuming a single early window matters most. Our validated questionnaire offers a way to measure immediate affective and motivational responses, giving researchers and practitioners a tool to evaluate whether specific interventions succeed in raising enthusiasm.

cs.SE↗

arXiv2026

Gamification Preferences in Digital Education: The Role of Individual Differences

Although personalization is widely advocated in gamified learning, empirical evidence on how learner characteristics and task context shape motivational preferences remains limited. This study examines how user characteristics and learning activity types relate to preferences for gamification elements in digital education. A large-scale quantitative survey (N = 530), including 34% underage participants, assessed preferences for 13 gamification elements in relation to Age, Gender, HEXAD Player Type, Big Five Personality Traits, Felder-Silverman Learning Styles, and Bloom-based Learning Activity Types. Inferential statistical analyses and exploratory machine learning techniques revealed systematic but generally small-to-moderate effects across parameters. Age emerged as the most consistent predictor of preference, followed by player type and personality traits, whereas gender and learning styles showed comparatively weaker associations. In addition, learning activity type significantly influenced the perceived suitability of gamification elements, indicating that motivational design is task-dependent. The findings suggest that gamification effectiveness cannot be reduced to universally motivating elements. Instead, preferences are shaped by the interaction of learner characteristics and instructional context. These results provide empirical grounding for adaptive and modular gamification strategies in digital learning environments.

cs.CY↗

arXiv2025

Gamification with Purpose: What Learners Prefer to Motivate Their Learning

This study investigates learners' preferences for game design elements (GDEs) in educational contexts to inform the development of purpose-driven gamification strategies. It emphasizes a learner-centered approach that aligns gamification design with pedagogical goals, while mitigating risks such as the erosion of intrinsic motivation. A systematic literature review was conducted to identify ten widely discussed GDEs. Visual prototypes representing each element were developed, and a best-worst scaling (BWS) survey with 125 participants was administered to elicit preference rankings. Qualitative feedback was also collected to uncover motivational drivers. Learners consistently preferred GDEs that support learning processes directly-most notably progress bars, concept maps, immediate feedback, and achievements. Qualitative analysis revealed six recurring motivational themes, including visible progress, content relevance, and constructive feedback. The findings suggest that learners value gamification elements that are meaningfully integrated with educational content and support intrinsic motivation. Purpose-aligned gamification should prioritize tools that visualize learning progress and provide actionable feedback, rather than relying solely on extrinsic incentives.

cs.SE↗

arXiv2025

An Experience Report on a Pedagogically Controlled, Curriculum-Constrained AI Tutor for SE Education

The integration of artificial intelligence (AI) into education continues to evoke both promise and skepticism. While past waves of technological optimism often fell short, recent advances in large language models (LLMs) have revived the vision of scalable, individualized tutoring. This paper presents the design and pilot evaluation of RockStartIT Tutor, an AI-powered assistant developed for a digital programming and computational thinking course within the RockStartIT initiative. Powered by GPT-4 via OpenAI's Assistant API, the tutor employs a novel prompting strategy and a modular, semantically tagged knowledge base to deliver context-aware, personalized, and curriculum-constrained support for secondary school students. We evaluated the system using the Technology Acceptance Model (TAM) with 13 students and teachers. Learners appreciated the low-stakes environment for asking questions and receiving scaffolded guidance. Educators emphasized the system's potential to reduce cognitive load during independent tasks and complement classroom teaching. Key challenges include prototype limitations, a small sample size, and the need for long-term studies with the target age group. Our findings highlight a pragmatic approach to AI integration that requires no model training, using structure and prompts to shape behavior. We position AI tutors not as teacher replacements but as enabling tools that extend feedback access, foster inquiry, and support what schools do best: help students learn.

cs.CY↗

arXiv2024

Human Factors in Model-Driven Engineering: Future Research Goals and Initiatives for MDE

Purpose: Software modelling and Model-Driven Engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values.

cs.SE↗

arXiv2016

Three-dimensional simulations of gravitationally confined detonations compared to observations of SN 1991T

The gravitationally confined detonation (GCD) model has been proposed as a possible explosion mechanism for Type Ia supernovae in the single-degenerate evolution channel. Driven by buoyancy, a deflagration flame rises in a narrow cone towards the surface. For the most part, the flow of the expanding ashes remains radial, but upon reaching the outer, low-pressure layers of the white dwarf, an additional lateral component develops. This makes the deflagration ashes converge again at the opposite side, where the compression heats fuel and a detonation may be launched. To test the GCD explosion model, we perform a 3D simulation for a model with an ignition spot offset near the upper limit of what is still justifiable, 200 km. This simulation meets our deliberately optimistic detonation criteria and we initiate a detonation. The detonation burns through the white dwarf and leads to its complete disruption. We determine nucleosynthetic yields by post-processing 10^6 tracer particles with a 384 nuclide reaction network and we present multi-band light curves and time-dependent optical spectra. We find that our synthetic observables show a prominent viewing-angle sensitivity in UV and blue bands, which is in tension with observed SNe Ia. The strong dependence on viewing-angle is caused by the asymmetric distribution of the deflagration ashes in the outer ejecta layers. Finally, we perform a comparison of our model to SN 1991T. The overall flux-level of the model is slightly too low and the model predicts pre-maximum light spectral features due to Ca, S, and Si that are too strong. Furthermore, the model chemical abundance stratification qualitatively disagrees with recent abundance tomography results in two key areas: our model lacks low velocity stable Fe and instead has copious amounts of high-velocity 56Ni and stable Fe. We therefore do not find good agreement of the model with SN 1991T.

astro-ph.SR↗

arXiv2016

Neutrino and gravitational wave signal of a delayed-detonation model of Type Ia supernovae

The progenitor system(s) and the explosion mechanism(s) of Type Ia supernovae (SNe Ia) are still under debate. Non-electromagnetic observables, in particular gravitational waves and neutrino emission, of thermonuclear supernovae are a complementary window to light curves and spectra for studying these enigmatic objects. A leading model for SNe Ia is the thermonuclear incineration of a near-Chandrasekhar mass carbon-oxygen white dwarf star in a "delayed-detonation". We calculate a three-dimensional hydrodynamic explosion for the N100 delayed-detonation model extensively discussed in the literature, taking the dynamical effects of neutrino emission from all important contributing source terms into account. Although neutrinos carry away $2 \times 10^{49}$ erg of energy, we confirm the common view that neutrino energy losses are dynamically not very important, resulting in only a modest reduction of the final kinetic energy by two per cent. We then calculate the gravitational wave signal from the time evolution of the quadrupole moment. Our model radiates $7 \times 10^{39}$ erg in gravitational waves and the spectrum has a pronounced peak around 0.4 Hz. Depending on viewing angle and polarization, we find that the future space-based gravitational wave missions DECIGO and BBO would be able to detect our source to a distance of 1.3 Mpc. We predict a clear signature of the deflagration-to-detonation transition in the neutrino and the gravitational wave signals. If observed, such a feature would be a strong indicator of the realization of delayed-detonations in near-Chandrasekhar mass white dwarfs.

astro-ph.SR↗

arXiv2012

Macroscopic quantum tunneling of Bose-Einstein condensates with long-range interaction

The ground state of Bose-Einstein condensates with attractive particle interaction is metastable. One of the decay mechanisms of the condensate is a collapse by macroscopic quantum tunneling, which can be described by the bounce trajectory as solution of the time-dependent Gross-Pitaevskii equation in imaginary time. For condensates with an electromagnetically induced gravity-like interaction the bounce trajectory is computed with an extended variational approach using coupled Gaussian functions and simulated numerically exact within the mean-field approach on a space-time lattice. It is shown that the variational computations converge very rapidly to the numerically exact result with increasing number of Gaussians. The tunneling rate of the condensate is obtained from the classical action and additional parameters of the bounce trajectory. The converged variational and numerically exact results drastically improve by several orders of magnitude the decay rates obtained previously with a simple variational approach using a single Gaussian-type orbital for the condensate wave function.

cond-mat.quant-gas↗