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Personal names and identity: a socio-onomastic approach to naming practices in the ancient world
No full textStrategies for engaging students through motivational framing of experiential technology use in the classroom
Get PDFThis thesis dives into an understudied but important question for educators: how can we use
technology in classrooms to truly inspire and engage students? Although gamified tools and tech-based learning are becoming more common to support learning in schools and in the context of focus
– second language learning, little attention has been paid to how these tools can be designed and
framed to meet students’ deeper psychological needs and enhance their intrinsic motivation. Drawing
on Self-Determination Theory (SDT), the research presented in the thesis takes an integrative look at
the motivational power of technology in education, particularly focusing on how it can support
students’ needs for autonomy (sense of choice and volition), competence (sense of efficacy), and
relatedness (feeling connected to others). Through a pilot study and three large-scale experiments with
students aged 9 to 16 years, this thesis examines the importance of combining gamified technology
with motivational framing strategies. These strategies include teamwork, friendly competition, and
offering students choices in how they engage with technology-based learning. The results across
studies suggested that when these strategies are thoughtfully combined with technology, students
show greater interest in lessons and feel more engaged. Furthermore, these strategies helped to satisfy
students’ psychological needs for autonomy, relatedness, and competence, which indirectly linked
motivational framing for technology with interest and engagement. Teachers’ approaches also
influenced the learning environment: When teachers used autonomy-supportive styles, encouraging
curiosity and providing structure without pressure, the benefits of technology increased. By
integrating insights from motivational psychology, technology design, and education, this thesis
develops a triadic model of learning, where students thrive at the intersection of engaging
technologies, supportive teachers, and an inspiring classroom environment. The findings suggest that
technology, used the right way, can transform education—not just by making it fun but by nurturing
deep, sustained motivation
Estimating systemic risk using composite quantile regression
Get PDFValue at Risk (VaR) and Average Value at Risk (AVaR) are among the most widely-used risk measures by market participants to assess the risk of individual financial firms and institutions. Despite their popularity, both measures fail to account for spillover effects between firms. To address this limitation, the CoVaR (Conditional Value at Risk) measure was introduced, which defines the VaR of a financial system conditional on the state of another institution. The traditional approach to estimating CoVaR involves a regression model combined with a quantile method to estimate the model’s parameters. This paper proposes a composite quantile regression method to enhance the accuracy of CoVaR estimation. We apply this methodology to several U.S. companies across various sectors, including finance, consumer goods, energy, industry, and technology. An analysis of the out-of-sample forecast accuracy using two popular backtesting criteria demonstrates that the composite quantile method provides more accurate CoVaR estimates than the standard quantile method. All computation codes are freely available in both R and MATLAB
Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders
Get PDFLarge-scale atmospheric dynamics modulate the occurrence of extreme precipitation events and provide sources of predictability of these events on timescales ranging from days to decades. In the midlatitudes, regional dynamical drivers are frequently represented as discrete, persistent and recurrent circulation regimes. However, available methods identify circulation regimes which are either predictable but not necessarily informative of the relevant local-scale impact studied, or targeted to a local-scale impact but no longer as predictable. In this paper, we introduce a generative machine learning method based on variational autoencoders for identifying probabilistic circulation regimes targeted to spatial patterns of precipitation. The method, CMM-VAE, combines targeted dimensionality reduction and probabilistic clustering in a coherent statistical model and extends a previous architecture published by the authors to allow for categorical target variables. We investigate the trade-off between regime informativeness of local precipitation extremes and predictability of the regimes at subseasonal lead times. In an application to study drivers of extreme precipitation over Morocco, we find that the targeted CMM-VAE regimes are more informative of the impact variable of interest, compared to two well-established linear approaches, while maintaining the predictability of conventional non-targeted circulation regimes in subseasonal hindcasts, hence resolving the trade-off identified in previous studies. Furthermore, the targeted regimes and their predictability are physically interpretable in terms of known subseasonal teleconnections relevant to the region, the Madden-Julian Oscillation and variability of the stratospheric polar vortex. The proposed method therefore allows to identify predictable, interpretable and locally relevant representations of regional dynamical drivers given a target variable of interest. These results highlight the potential of the method for a variety of applications, ranging from subseasonal forecasting to attribution and statistical downscaling
‘Isotype, visuelle Hilfsmittel und die Kolonien’
No full textA concise account of Isotype, visual aids, and the colonies, including discussion of Otto Neurath's arguments in favour of visual aids and their contribution to visual education in colonial territories; the activities of Wolfgang Foges, Adprint, and Buffalo Books; and the work of Marie Neurath and the Isotype Institute in the Western Region of Nigeria in the 1950s
