Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Engineering Adaptive Interfaces – Enhancement of Comprehension and Decision-Making

The role of information systems is growing steadily and permeating more and more all levels of our society. Meanwhile, information systems have to support different user groups in various decision situations simultaneously. Hence, the existing design approach to creat- ing a unified user interface is reaching its limits. This work examines adaptive information system design by investigating user-adaptive information visualization and situation-aware nudging. An exploratory eye-tracking study investigates participants’ perception and comprehension of different financial visualizations and shows that none of them can be preferred across the board. Moreover, it reveals expertise knowledge as the research direction for visualization recommendations. Afterward, two empirical studies are conducted to relate different visualizations to participants’ domain-specific knowledge. The first study, conducted with a broad sample of the population, shows that financial and graphical literacy increases participants’ financial decision-making competency with certain visualizations. The second study, conducted with a more specific sample and an additional visualization, underlines a large part of the first study’s results. Additionally, it identifies statistical literacy as an increasing factor in financial decision-making. Both studies are demonstrating that different visualizations cause different cognitive loads despite the same amount of information. After all, the results are used to derive visualization recommendations based on domain-specific knowledge and cognitive load. This work also investigates the situation-aware effectiveness of nudging with the example of decision inertia. In a preliminary study, an experimental task is systematically transferred to different situational contexts by observing situational user characteristics. The identified contexts are examined in a subsequent large-scale empirical study with different nudges to reduce decision inertia. The results show gender-specific differences in decision inertia across the context. Hence, information system design has to adapt to gender and situational user characteristics to support users in their decision-making. Moreover, the study delivers empirical evidence for the contextual effectiveness of nudg- ing. Future nudging research has to incorporate situational user characteristics to provide effective nudges in different situational contexts. Especially, further fundamental research is needed to understand the situational effectiveness of nudging. The study identifies in- dividual situational preferences as one promising research stream

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Designing electronic collaborative learning environments

Electronic collaborative learning environments for learning and working are in vogue. Designers design them according to their own constructivist interpretations of what collaborative learning is and what it should achieve. Educators employ them with different educational approaches and in diverse situations to achieve different ends. Students use them, sometimes very enthusiastically, but often in a perfunctory way. Finally, researchers study them and—as is usually the case when apples and oranges are compared—find no conclusive evidence as to whether or not they work, where they do or do not work, when they do or do not work and, most importantly, why, they do or do not work. This contribution presents an affordance framework for such collaborative learning environments; an interaction design procedure for designing, developing, and implementing them; and an educational affordance approach to the use of tasks in those environments. It also presents the results of three projects dealing with these three issues

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey

IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness

In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, particularly in critical applications where the decisions an agent makes can have significant consequences. Yet, existing RL-based systems are essentially competency-unaware in that they lack the necessary interpretation mechanisms to allow human operators to have an insightful, holistic view of their competency. Towards more explainable Deep RL (xDRL), we propose a new framework based on analyses of interestingness. Our tool provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms, natively supporting the popular RLLib toolkit. We showcase the use of our framework by applying the proposed pipeline in a set of scenarios of varying complexity. We empirically assess the capability of the approach in identifying agent behavior patterns and competency-controlling conditions, and the task elements mostly responsible for an agent's competence, based on global and local analyses of interestingness. Overall, we show that our framework can provide agent designers with insights about RL agent competence, both their capabilities and limitations, enabling more informed decisions about interventions, additional training, and other interactions in collaborative human-machine settings.Comment: To be published in the Proceedings of the 1st World Conference on eXplainable Artificial Intelligence (xAI 2023). arXiv admin note: substantial text overlap with arXiv:2211.0637

Truth Discovery in Crowdsourced Detection of Spatial Events

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