Higher Order Thinking by Setting and Debriefing Tasks in Dutch Geography Lessons

Tasks are a powerful instrument for geography teachers, as they let students engage with the subject. To advance the cumulative learning of students, teachers have to make sure that students learn how to deal with complex and abstract knowledge structures. In the Netherlands, teachers face a dilemma when it comes to task setting: the intended curriculum aims for a considerable part at (parts of) higher order thinking, whereas the high-stakes exams have a clear focus on the use of thinking strategies. This paper explores the task setting and debriefing of Dutch geography teachers by analyzing twenty-three videotaped lessons in upper secondary education by using the Geography Task Categorization Framework. The results show that Dutch teachers mostly rely on textbooks when setting tasks. The focus lies on reproduction and the use of thinking strategies. Tasks aiming at (parts of) higher order thinking are barely used. Furthermore, teachers use tasks from previous high-stakes exams already used in an early stage of upper secondary education. In the debriefing of tasks, teachers move from simple and concrete to complex and abstract knowledge and vice versa. However, most of these movements aim at simplifying knowledge structures. In the observed lessons, curriculum aims at the level of (parts of) higher order thinking are not achieved. The evaluative rules as set by the high-stakes exams and the type of tasks offered by textbooks seem to be dominant.

Quality 4.0 – a review of and framework for quality management in the digital era

PurposeIndustry 4.0 has brought about a paradigm shift in value delivery with the introduction of disruptive technologies. This has resulted in efforts by organizations to re-invent their business processes and reskill their workforce while attempting to realize digital transformation. Quality management in the context of Industry 4.0 is still in its nascent stage with researchers trying to identify key and relevant components of quality management with respect to Industry 4.0. The current study attempts to address the knowledge gap through a literature review and subsequently provide a conceptual framework for quality in the digital transformation context.Design/methodology/approachAn integrative literature review was conducted to analyze and abstract knowledge from the literature on Quality 4.0 and a conceptual framework was developed based on the review.FindingsThe review revealed the motivators, building blocks and challenges for Quality 4.0. The conceptual framework discusses the salient points relevant to Quality 4.0 with respect to the people, process and technology dimensions and their sub-dimensions that can be used to build 4.0 capabilities. The proposed framework is represented to depict the conceptualization and the relationships among its components.Originality/valueThis study aims to contribute to the model building efforts of researchers towards Quality 4.0. The points discussed here provide an actionable direction to augment the efforts of practitioners and organizations in quality management in the context of Industry 4.0, especially digital transformation.

Foundations of the Socio-Physical Model of Activities (SOMA) for Autonomous Robotic Agents1

In this paper, we present foundations of the Socio-physical Model of Activities (SOMA). SOMA represents both the physical as well as the social context of everyday activities. Such tasks seem to be trivial for humans, however, they pose severe problems for artificial agents. For starters, a natural language command requesting something will leave many pieces of information necessary for performing the task unspecified. Humans can solve such problems fast as we reduce the search space by recourse to prior knowledge such as a connected collection of plans that describe how certain goals can be achieved at various levels of abstraction. Rather than enumerating fine-grained physical contexts SOMA sets out to include socially constructed knowledge about the functions of actions to achieve a variety of goals or the roles objects can play in a given situation. As the human cognition system is capable of generalizing experiences into abstract knowledge pieces applicable to novel situations, we argue that both physical and social context need be modeled to tackle these challenges in a general manner. The central contribution of this work, therefore, lies in a comprehensive model connecting physical and social entities, that enables flexibility of executions by the robotic agents via symbolic reasoning with the model. This is, by and large, facilitated by the link between the physical and social context in SOMA where relationships are established between occurrences and generalizations of them, which has been demonstrated in several use cases in the domain of everyday activites that validate SOMA.

Chapter 17. Logic Tensor Networks: Theory and Applications

The recent availability of large-scale data combining multiple data modalities has opened various research and commercial opportunities in Artificial Intelligence (AI). Machine Learning (ML) has achieved important results in this area mostly by adopting a sub-symbolic distributed representation. It is generally accepted now that such purely sub-symbolic approaches can be data inefficient and struggle at extrapolation and reasoning. By contrast, symbolic AI is based on rich, high-level representations ideally based on human-readable symbols. Despite being more explainable and having success at reasoning, symbolic AI usually struggles when faced with incomplete knowledge or inaccurate, large data sets and combinatorial knowledge. Neurosymbolic AI attempts to benefit from the strengths of both approaches combining reasoning with complex representation of knowledge and efficient learning from multiple data modalities. Hence, neurosymbolic AI seeks to ground rich knowledge into efficient sub-symbolic representations and to explain sub-symbolic representations and deep learning by offering high-level symbolic descriptions for such learning systems. Logic Tensor Networks (LTN) are a neurosymbolic AI system for querying, learning and reasoning with rich data and abstract knowledge. LTN introduces Real Logic, a fully differentiable first-order language with concrete semantics such that every symbolic expression has an interpretation that is grounded onto real numbers in the domain. In particular, LTN converts Real Logic formulas into computational graphs that enable gradient-based optimization. This chapter presents the LTN framework and illustrates its use on knowledge completion tasks to ground the relational predicates (symbols) into a concrete interpretation (vectors and tensors). It then investigates the use of LTN on semi-supervised learning, learning of embeddings and reasoning. LTN has been applied recently to many important AI tasks, including semantic image interpretation, ontology learning and reasoning, and reinforcement learning, which use LTN for supervised classification, data clustering, semi-supervised learning, embedding learning, reasoning and query answering. The chapter presents some of the main recent applications of LTN before analyzing results in the context of related work and discussing the next steps for neurosymbolic AI and LTN-based AI models.

New large subglacial lake in Princess Elizabeth Land, East Antarctica, detected by airborne geophysical observations

Knowledge of subglacial lakes is important for understanding the stability of the Antarctica Ice Sheet (AIS) and its contribution to the global sea-level change. We designed an intensified airborne campaign to collect geophysical data in Princess Elizabeth Land (PEL), East Antarctica, during the 2015–2019 CHINARE expeditions. We developed an innovative method to build a set of evidence of a newly detected subglacial lake, Lake Zhongshan. Adaptive RES data analysis allowed us to detect the lake surface and extent. We quantified the lake depth and volume via gravity modeling. Another dataset collected at Lake Vostok provided the ground truth. The results revealed that Lake Zhongshan, located at 73°26'53"S, 80°30'39"E and ~3,603 m below surface, has an area of 328 ± 1 km2, making it the only one in PEL and the fifth largest in Antarctica. These findings are important for understanding subglacial hydrodynamics in PEL, as well as the stability of the AIS.

New science of art of Hans Sedlmayr

This article provides a brief systemic analysis of the key concepts of the so-called new science of art developed by the Austrian art historian Hans Sedlmayr. The result of Seldmayr’s pursuits are reflected in creation of his own philosophy of art and culture based on a particular worldview. The cognition of the whole, along with individual and unique, underlies this science. Understanding is the goal of scientific knowledge for Sedlmayr. It suggests not only abstract knowledge, but peculiar existential experience as well. Sedlmayr interprets the understanding of artwork as its contemplation, which in turn, is identical to its actualization or presence. In Seldlmayr’s art of science, epistemologies and ontologies merge into each other. He interprets artworks simultaneously as the event and as the social organism, which overcomes the linearity of time and fragmentation of plurality. This artificial complex system, built on the paradoxical identity of the single and plenty, is both finite and infinite. Sedlmayr’s views encompass classical and nonclassical approach towards cognition of the whole. He relies on the principles of monism, seeking to reduce all concepts to a single basis, single point of singularity that designates the synthesis of all the moments of the whole and can be expressed by a single category. The main category, which resembles the center of the opposites, is the “midpoint” (Mitte). The aforementioned ideas are consistent and logical only in such scientific worldview that identifies ontology and epistemology, which implies the unity of contemplation and phenomenon of the artwork. Therefore, in Sedlmayr's constructions, actualization or revival of the artwork is identical with its comprehension. The systemic approach towards the artwork reflected in the theoretical works of Sedlmayr extends the boundaries of art science and converges with philosophy.

In search of different categories of abstract concepts: a fMRI adaptation study

Concrete conceptual knowledge is supported by a distributed neural network representing different semantic features according to the neuroanatomy of sensory and motor systems. If and how this framework applies to abstract knowledge is currently debated. Here we investigated the specific brain correlates of different abstract categories. After a systematic a priori selection of brain regions involved in semantic cognition, i.e. responsible of, respectively, semantic representations and cognitive control, we used a fMRI-adaptation paradigm with a passive reading task, in order to modulate the neural response to abstract (emotions, cognitions, attitudes, human actions) and concrete (biological entities, artefacts) categories. Different portions of the left anterior temporal lobe responded selectively to abstract and concrete concepts. Emotions and attitudes adapted the left middle temporal gyrus, whereas concrete items adapted the left fusiform gyrus. Our results suggest that, similarly to concrete concepts, some categories of abstract knowledge have specific brain correlates corresponding to the prevalent semantic dimensions involved in their representation.

Determining an optimal transport velocity in the marginal ice zone using operational ice-ocean prediction systems

Knowledge of transport in the marginal ice zone (MIZ) is critical for operations in the Arctic and associated emergency response applications, for example, the transport of pollutants, such as oil, as well as predicting drift associated with search and rescue operations. This paper proposes a general transport equation for the MIZ that can be used for operational purposes in the MIZ. This equation is designed to use a mean velocity of the ice and water velocity, which is weighted by the ice concentration. A key component is the introduction of a leeway coefficient for both the ocean and ice components. These leeway coefficients are determined by minimizing the velocity error between the transport model and observed drifter velocity in the MIZ. These leeway values are found to be 3 % of the wind for the water leeway and 2 % and 30° to the right of the wind for the ice leeway, which are consistent with "rule of thumb" values for surface drifters and sea ice respectively. This general transport model is compared with other transport models and the error is reduced by a factor of 2 compared with traditional transport models for 48 hour lead times. The inclusion of a leeway coefficient in the ice is the key component to reduce trajectory errors in the MIZ.

SMAUG v1.0 – a user-friendly muon simulator for transmission tomography of geological objects in 3D

Knowledge about muon tomography has spread in recent years in the geoscientific community and several collaborations between geologists and physicists have been founded. As the data analysis is still mostly done by particle physicists, we address the need of the geoscientific community to participate in the data analysis, while not having to worry too much about the particle physics equations in the background. The result hereof is SMAUG, a toolbox consisting of several modules that cover the various aspects of data analysis in a muon tomographic experiment. In this study we show how a comprehensive geophysical model can be built from basic physics equations. The emerging uncertainties are dealt with by a probabilistic formulation of the inverse problem, which is finally solved by a Monte Carlo Markov Chain algorithm. Finally, we benchmark the SMAUG results against those of a recent study, which however, have been established with an approach that is not easily accessible to the geoscientific community. We show that they reach identical results with the same level of accuracy and precision.

Faktor-Faktor yang Memengaruhi Pengetahuan Anak pada Lagu Daerah Kalimantan

Knowledge of elementary school-aged children in Kalimantan folk songs is a severe problem because children's knowledge is one of the factors that can affect the achievement of local song preservation. So that information is needed regarding surveys about what factors influence children's knowledge of Kalimantan folk songs. The theory used in this study is the theory of factors that influence learning. This study aims to determine the factors that influence children's knowledge of folk songs, especially Kalimantan. This study uses survey research with quantitative descriptive methods, namely, conducting field surveys. The sample of this study was 158 students, namely grades 5A and 5B of Elementary School 005 Samarinda, totaling 40 students, and classes 5A, 5B, and 5C of Elementary School 007 Samarinda totaling 118 students. The results of data analysis show that there are internal and external factors. The internal factor is that students listen to more popular songs because popular songs are currently packaged with unique and various videos. Even the advertisements that they usually hear contain many lyrics from popular songs today so that children are more interested and interested in listening to popular songs than folk songs. Another internal factor is when students do gymnastics together, the songs played are popular dangdut songs today. External factors indicate that the teacher's lack of knowledge about the folk songs of East Kalimantan. Abstrak: Pengetahuan anak-anak usia Sekolah Dasar (SD) pada lagu daerah Kalimantan merupakan permasalahan yang cukup serius, dikarenakan pengetahuan anak-anak merupakan salah satu faktor yang dapat mempengaruhi tercapainya pelestarian lagu daerah. Sehingga diperlukan informasi mengenai survei tentang apa saja faktor-faktor yang mempengaruhi pengetahuan anak pada lagu daerah Kalimantan. Teori yang digunakan dalam penelitian ini adalah teori faktor yang mempengaruhi belajar. Penelitian ini bertujuan untuk mengetahui faktor yang mempengaruhi pengetahuan anak terhadap lagu daerah khususnya Kalimantan. Penelitian ini menggunakan penelitian survei dengan metode deskriptif kwantitatif yakni melakukan survei lapangan. Sempel penelitian ini adalah 158 siswa, yaitu kelas 5A dan 5B Sekolah Dasar Negeri 005 Samarinda berjumlah 40 siswa, dan kelas 5A, 5B, dan 5C Seklah Dasar Negeri 007 Samarinda berjumlah 118 siswa. Hasil analisis data menunjukan bahwa terdapat faktor internal dan eksternal. Faktor internal adalah siswa lebih banyak mendengarkan lagu populer karena lagu populer saat ini dikemas dengan video yang unik dan bermacam-macam. Bahkan iklan-iklan yang biasa mereka dengar banyak gubahan lirik dari lagu-lagu populer saat ini, sehingga anak-anak lebih tertarik dan berminat untuk mendengarkan lagu populer daripada lagu daerah. Faktor internal lain adalah ketika siswa melakukan kegiatan senam bersama, lagu yang diputar adalah lagu-lagu dangdut populer masa kini. Faktor eksternal menunjukan bahwa kurangnya pengetahuan guru mengenai lagu daerah Kalimantan Timur.