First-year engineering students' computational reasoning abilities

Computational thinking is an essential skill set for today's students, given the digital age in which we live and work (CT). Without a precise definition, it is generally understood to be a collection of abilities and attitudes required to deal with difficulties in any aspect of life, whether or not a computer is involved. Measurement and evaluation of students' progress in CT abilities are critical, and this can only be done using instruments that have been tested and shown to work before. New students at the Basque Country's University of the Basque Country's Engineering Degrees are tested for critical thinking, algorithmic thinking, problem solving, cooperation and creativity using a previously proven tool.

The importance of understanding web frameworks for statisticians

As the web and computational technology carry on growing and huge data are yielded on the web, these technologies are turn into important for a statisticians' work. It is worthy that statistician always gain knowledge of new aspects of computation. A lack of computational reasoning skills gets it hard for statisticians to work in a team. If statistician do not take up this computations challenge more coherently, statistics will be marginalized and take away related at a time when its data science reputation grow up significantly. In addition, people rely on the information on web, for whatever their reason.Since web growth, several major transforms have evolved, from the most rudimentary concept until a new model of interaction between humans and machines. Simple interactivity denotes that users can enter data to the application on a web page, then click on button, and then appears a new web page with the results of the computations. This application has been known as web application with most are built with the utility of web frameworks which is a package of programming tasks that offering services through the Internet. Therefore, this paper gives short overview the importance of Flask web frameworks to assist the lack of computational skill of statistician over web application in the simplest possible way and how web framework is used to create a web page with application form, run the application to compute statistical calculation which has been deployed in local server, and produce a web page with the solutions

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine

Background: Biomedical translational science is increasingly leveraging computational reasoning on large repositories of structured knowledge (such as the Unified Medical Language System (UMLS), the Semantic Medline Database (SemMedDB), ChEMBL, DrugBank, and the Small Molecule Pathway Database (SMPDB)) and data in order to facilitate discovery of new therapeutic targets and modalities. Since 2016, the NCATS Biomedical Data Translator project has been working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and within the field more broadly, there is an urgent need for an open-source framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be either downloaded in standard serialized form or queried via a public application programming interface (API) that accords with the FAIR data principles. Results: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building—and hosting a web API for querying—a biomedical knowledge graph that uses an Extract-Transform-Load (ETL) approach to integrate 70 knowledge sources (including the aforementioned sources) into a single knowledge graph. The semantic layer and schema for RTX-KG2 follow the standard Biolink metamodel to maximize interoperability within Translator. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered web interface. JavaScript Object Notation (JSON) serializations of RTX-KG2 are available for download of RTX-KG2 in both the pre-canonicalized form and in canonicalized form (in which synonym concepts are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M concept nodes and 39.3M relationship edges with a rich set of 77 relationship types. Conclusion: RTX-KG2 is the first open-source knowledge graph of which we are aware that integrates UMLS, SemMedDB, ChEMBL, DrugBank, SMPDB, and 65 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema at the intersections of these databases. RTX-KG2 is publicly available for querying via its (API) at arax.ncats.io/api/rtxkg2/v1.2/openapi.json. The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2.

The Wicked and the Logical: Facilitating Integrative Learning Among Introductory Computing Students

Higher education has embraced integrative learning as a means of enabling students to tackle so-called “wicked” problems, i.e. problems that are sufficiently complex, contested, and ambiguous that conventional, disciplinary specific approaches are inadequate to address. However, challenges remain in defining integrative learning consistently and effectively, especially because the cognitive processes that make up an integrative learning experience are not understood fully. This mixed-methods study was designed to help understand how students perceive, navigate, and resolve challenges that require them to integrate knowledge of one “wicked” subject (sustainability) with the skills of a practice rooted in mathematical logic (computer programming); how they express their integrative learning through reflective writing; and how we gain a stronger understanding of this process through linguistic analysis. The findings suggest that some students demonstrated the ability to integrate computational reasoning skills into socially relevant contexts more successfully, confidently, and in more well-rounded ways than others, though success required ways of thinking that extended beyond programming. The findings also underscore the potential need for reconceptualizing integrative teaching and learning in fields that have problem-solving traditions rooted in less “wicked” solutions.

The Wicked and the Logical: Facilitating Integrative Learning Among Introductory Computing Students

Higher education has embraced integrative learning as a means of enabling students to tackle so-called “wicked” problems, i.e. problems that are sufficiently complex, contested, and ambiguous that conventional, disciplinary specific approaches are inadequate to address. However, challenges remain in defining integrative learning consistently and effectively, especially because the cognitive processes that make up an integrative learning experience are not understood fully. This mixed-methods study was designed to help understand how students perceive, navigate, and resolve challenges that require them to integrate knowledge of one “wicked” subject (sustainability) with the skills of a practice rooted in mathematical logic (computer programming); how they express their integrative learning through reflective writing; and how we gain a stronger understanding of this process through linguistic analysis. The findings suggest that some students demonstrated the ability to integrate computational reasoning skills into socially relevant contexts more successfully, confidently, and in more well-rounded ways than others, though success required ways of thinking that extended beyond programming. The findings also underscore the potential need for reconceptualizing integrative teaching and learning in fields that have problem-solving traditions rooted in less “wicked” solutions.

The Automated Taxonomic Concept Reasoner

We present a visual and interactive taxonomic Artificial Intelligence (AI) tool, the Automated Taxonomic Concept Reasoner (ATCR), whose graphical web interface is under development and will also become available via an Application Programming Interface (API). The tool employs automated reasoning (Beeson 2014) to align multiple taxonomies visually, in a web browser, using user or expert-provided taxonomic articulations, i.e. "Region Connection Calculus (RCC-5) relationships between taxonomic concepts, provided in a specific logical language (Fig. 1). It does this by representing the problem of taxonomic alignment under these constraints in terms of logical inference, while performing these inferences computationally and leveraging the powerful Microsoft Z3 Satisfiability Modulo Theory (SMT) solver (de Moura and Bjørner 2008). This tool represents further development of utilities for the taxonomic concept approach, which fundamentally addresses the challenge of robust biodiversity data aggregation in light of multiple conflicting sources (and source classifications) from which primary biodiversity data almost invariably originate. The approach has proven superior to aggregation, based just on the syntax and semantics provided by the Darwin Core standard Franz and Sterner 2018). Fig. 1 provides an artificial example of such an alignment. Two taxonomies, A and B, are shown. There are five taxonomic concepts, A.One, A.Two, A.Three, B.One and B.Two. A.Two and A.Three are sub-concepts (children) of A.One, and B.Two is a sub-concept (child) of B.One. These are represented by the direction of the grey arrows. The undirected mustard-coloured lines represent relationships, i.e., the articulations referred to in the previous paragraph. These may be of five kinds: congruent (==), includes (<) and included in (>), overlap (><), and disjointness. These five relationships are known in the AI literature as the Region Connection Calculus-5 (RCC-5) (Randell et al. 1992, Bennett 1994, Bennett 1994), and taken exclusively and in conjunction with each other, have certain desirable properties with respect to the representation of spatial relationships. The provided relationship (i.e. the articulation) may also be an arbitrary disjunction of these five fundamental kinds, thus allowing for representation of some degree of logical uncertainty. Then, and under three assumptions that: "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. ATCR is related to Euler/X (Franz et al. 2015), an existing tool for the same kinds of taxonomic alignment problems, which was used, for example, to obtain an alignment of two influential primate classifications (Franz et al. 2016). It differs from Euler/X in that it employs a different logical encoding that enables more efficient and more informative computational reasoning, and also in that it provides a graphical web interface, which Euler/X does not.

Computational Thinking and Digital Games: Developing Skills With Fun

INTRODUCTION: Several researchers consider the importance of Computational Thinking being presented and developed from the earliest years of basic education and, furthermore, that digital games can be one of the vehicles to introduce it to children in schools. However, before developing new game solutions for this purpose, it is important to recognize how games can actually contribute to develop Computational Thinking, as well as to identify which skills have been worked on. OBJECTIVE: In this sense, this article presents the synthesis of a systematic mapping, whose objective was to identify how digital games can be used to develop Computational Thinking skills. METHOD: The objective was met by a systematic literature mapping executed by two reviewers and an expert. RESULTS: It was possible to identify some games used to stimulate the development of Computational Thinking skills, as well as the mechanics used by these games. CONCLUSION: It has been found that puzzle games are most commonly used to develop skills in Computational Reasoning. It has also been observed that the abilities of Abstraction and Algorithmic Thinking are the main skills developed in these games.

A Logical Model of Homology for Comparative Biology

There is a growing body of research on the evolution of anatomy in a wide variety of organisms. Discoveries in this field could be greatly accelerated by computational methods and resources that enable these findings to be compared across different studies and different organisms and linked with the genes responsible for anatomical modifications. Homology is a key concept in comparative anatomy; two important types are historical homology (the similarity of organisms due to common ancestry) and serial homology (the similarity of repeated structures within an organism). We explored how to most effectively represent historical and serial homology across anatomical structures to facilitate computational reasoning. We assembled a collection of homology assertions from the literature with a set of taxon phenotypes for the skeletal elements of vertebrate fins and limbs from the Phenoscape Knowledgebase. Using seven competency questions, we evaluated the reasoning ramifications of two logical models: the Reciprocal Existential Axioms (REA) homology model and the Ancestral Value Axioms (AVA) homology model. The AVA model returned all user-expected results in addition to the search term and any of its subclasses. The AVA model also returns any superclass of the query term in which a homology relationship has been asserted. The REA model returned the user-expected results for five out of seven queries. We identify some challenges of implementing complete homology queries due to limitations of OWL reasoning. This work lays the foundation for homology reasoning to be incorporated into other ontology-based tools, such as those that enable synthetic supermatrix construction and candidate gene discovery. [Homology; ontology; anatomy; morphology; evolution; knowledgebase; phenoscape.]