La Gamificación como estrategia didáctica para la enseñanza/aprendizaje de la programación: un mapeo sistemático de literatura

The incorporation of gamification into Programming courses has been identified as a potential strategy that could maximize student participation and have a positive impact on learning. To our knowledge, there is no study aimed at collecting and analyzing research results on this subject using a systematic method. To fill this gap, a systematic mapping of literature was carried out with the aim of summarizing the studies on the use of gamification as a didactic strategy for teaching/learning of Programming. Initially, through the search in four (4) digital libraries, 186 studies were obtained. Then, after a careful analysis of each of them, we verify that only 78 match our needs. Finally, we have categorized the contributions of these studies to present an overview of the results produced by the research community.La incorporación de la gamificación en cursos de Programación ha sido identificada como una estrategia potencial que podría maximizar la participación de los estudiantes y tener un impacto positivo en el aprendizaje. Hasta donde sabemos, no existe un estudio dirigido a recopilar y analizar los resultados de investigación sobre este tema utilizando un método sistemático. Para llenar este vacío, se realizó un mapeo sistemático de literatura con el objetivo de resumir los estudios sobre el uso de la gamificación como estrategia didáctica para la enseñanza/aprendizaje de la Programación. Inicialmente, mediante la búsqueda en cuatro (4) bibliotecas digitales, se obtuvieron 186 estudios. Luego, después de un cuidadoso análisis de cada uno de ellos, verificamos que sólo 78 coinciden con nuestras necesidades. Por último, hemos categorizado las contribuciones de estos estudios para presentar una visión general de los resultados producidos por la comunidad investigadora

Cyber-Human Systems, Space Technologies, and Threats

CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

Ontological interpretation of network monitoring data

Interpreting measurement and monitoring data from networks in general and the Internet in particular is a challenge. The motivation for this work has been to in- vestigate new ways to bridge the gap between the kind of data which are available and the more developed information which is needed by network stakeholders to support decision making and network management. Specific problems of syntax, semantics, conflicting data and modeling domain-specific knowledge have been identified. The methods developed and tested have used the Resource Descrip- tion Framework (rdf) and the ontology languages of the Semantic Web to bring together data from disparate sources into unified knowledgebases in two discrete case studies, both using real network data. Those knowledgebases have then been demonstrated to be usable and valuable sources of information about the networks concerned. Some success has been achieved in overcoming each of the identified problems using these techniques, proving the thesis that taking an ontological ap- proach to the processing of network monitoring data can be a very useful technique for overcoming problems of interpretation and for making information available to those who need it

A Process Model for the Integrated Reasoning about Quantitative IT Infrastructure Attributes

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently. This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness. Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.IT-Infrastrukturen können mit Attributen, wie Leistung und Energieeffizienz, quantitativ beschrieben werden. Nutzungsbedarfsänderungen und ökonomische Bestrebungen erfordern Kurz- und Langfristentscheidungen zur Anpassung einer IT-Infrastruktur und insbesondere ihre Attribute an dieses dynamische Umfeld. Potentielle Attribut-Zielkonflikte sowie die zentrale Rolle von IT-Infrastrukturen erfordern eine Entscheidungsfindung mittels Reasoning, einem Prozess, der Rückschlüsse (rein) aus Fakten und Prämissen zieht. Die Fokussierung auf spezifische Teile einer IT-Infrastruktur sowie die Beschränkung auf (sehr) wenige Attribute disqualifizieren bestehende Reasoning-Ansätze für dieses Vorhaben, da sie weder das komplexe Zusammenspiel von IT-Infrastruktur-Komponenten, noch Abhängigkeiten zwischen und innerhalb einzelner Attribute ausreichend berücksichtigen können. Diese Arbeit präsentiert ein Prozessmodell für das integrierte Reasoning über quantitative IT-Infrastruktur-Attribute. Die grundlegende Idee des Prozessmodells ist die Herleitung einer individuellen Reasoning-Funktion, einer mathematischen Abbildung von Einfluss- und Modifikationsparametern auf einen Attributvektor. Die Herleitung basiert auf der Integration bestehender (Attribut-)Modelle, um von deren Spezialisierung, Reife und Verbreitung profitieren zu können. Die erzielte Reasoning-Funktion verarbeitet ein individuelles Tupel aus IT-Infrastruktur-Komponenten, Attributen und externen Einflussfaktoren, um eine breite Anwendbarkeit zu gewährleisten. Das Prozessmodell formalisiert ein Reasoning-Vorhaben in drei Phasen. Zunächst werden die Reasoning-Ziele und -Parameter in einer Reasoning-Suite gesammelt und in einem Reasoning-Funktions-Gerüst formalisiert. Anschließend wird das Gerüst entsprechend den Vorgaben der Reasoning-Suite iterativ verfeinert. Abschließend wird die hergeleitete Reasoning-Funktion verwendet, um mittels “What-if”–Analysen, Optimierungsverfahren oder deskriptiver Statistik das Reasoning durchzuführen. Das Prozessmodell enthält fünf Template-Klassen, die den Prozess formalisieren, um Reproduzierbarkeit zu gewährleisten und Fehleranfälligkeit zu reduzieren. Das Prozessmodell wird auf drei Arten validiert. Ein kontrolliertes Experiment zeigt die Durchführbarkeit des Prozessmodells anhand des Reasonings zur Leistung und Energieeffizienz eines Raspberry Pi Clusters. Eine Anforderungsanalyse an einem Superrechner und an der europaweiten Ausführung von Hydro-Meteorologie-Modellen erläutert gemeinsam mit der Betrachtung verwandter Arbeiten den Innovationsgrad des Prozessmodells. Potentielle Erweiterungen nutzen die vorbereiteten Automatisierungsansätze, integrieren menschliche Faktoren, und generieren Modifikationsempfehlungen basierend auf Reasoning-Ergebnissen

Computational Tools and Facilities for the Next-Generation Analysis and Design Environment

This document contains presentations from the joint UVA/NASA Workshop on Computational Tools and Facilities for the Next-Generation Analysis and Design Environment held at the Virginia Consortium of Engineering and Science Universities in Hampton, Virginia on September 17-18, 1996. The presentations focused on the computational tools and facilities for analysis and design of engineering systems, including, real-time simulations, immersive systems, collaborative engineering environment, Web-based tools and interactive media for technical training. Workshop attendees represented NASA, commercial software developers, the aerospace industry, government labs, and academia. The workshop objectives were to assess the level of maturity of a number of computational tools and facilities and their potential for application to the next-generation integrated design environment

Eastern Washington State College general catalog, 1975-1976

The Eastern Washington State College catalog for the 1975-1976 academic year.https://dc.ewu.edu/catalogs/1077/thumbnail.jp

Eastern Washington State College general catalog, 1975-1976

The Eastern Washington State College catalog for the 1975-1976 academic year.https://dc.ewu.edu/catalogs/1077/thumbnail.jp

Air Force Institute of Technology Research Report 2019

This Research Report presents the FY19 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

Air Force Institute of Technology Research Report 2006

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics