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Record Nr. |
UNINA9910678586203321 |
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Titolo |
Gamification : Analysis, Design, Development and Ludification / / edited by Ioannis Deliyannis, Vaia Tzori, Athanasios Koutsouridis |
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Pubbl/distr/stampa |
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[Place of publication not identified] : , : IntechOpen, , 2023 |
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ISBN |
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Descrizione fisica |
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1 online resource (176 pages) |
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Disciplina |
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Soggetti |
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Gamification |
Video games in education |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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11552 This book examines how gamification is designed and implemented today, particularly within our transmedia world. This process requires balancing the narrative elements and the gamification mechanics used within the scenario that leads to the target objectives. Therefore, it is important to examine how exemplar case studies have been developed to learn from those implementations and advance this research and applications in this area. We approach the interdisciplinary design of gamified systems both as a deterministic and a creative process. From the deterministic perspective, one must design the system functionality as well as the rules and the mechanics that govern the functionality. However, the creative process is often an important factor for the success of an implementation. This includes all aspects of a gamified system, including its aesthetics, storyline and narration, and in more technologically advanced systems that are not limited to one medium. An important development in the field is the ability to use both gamification and ludification techniques within an implementation, a process that can create systems that are enjoyable and can dress up the scenario in our attempt to reach a state of "Flow"(In Csikszentmihalyi's words, flow is "a state in which people are so involved in an activity that nothing else seems to matter; the experience is so enjoyable that people will continue to do it even at |
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great cost, for the sheer sake of doing it"). Contemporary gamified systems utilize a wide variety of technologies that allow the extension from cross-media to transmedia scenarios. The availability of so many technologies and media platforms that one may use to design a gamified process is phenomenal, extending our capacity to create new gamified realizations that were not possible in the past as they can combine the complete reality-virtuality continuum. |
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2. |
Record Nr. |
UNINA9910678248403321 |
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Autore |
Sarang P. G (Poornachandra G.) |
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Titolo |
Thinking Data Science : A Data Science Practitioner’s Guide / / by Poornachandra Sarang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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9783031023637 |
9783031023620 |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (366 pages) : illustrations |
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Collana |
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The Springer Series in Applied Machine Learning, , 2520-1301 |
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Disciplina |
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Soggetti |
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Machine learning |
Artificial intelligence - Data processing |
Artificial intelligence |
Machine Learning |
Data Science |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter. 1. Data Science Process -- Chapter. 2. Dimensionality Reduction - Creating Manageable Training Datasets -- Chapter. 3. Classical Algorithms - Over-view -- Chapter. 4. Regression Analysis -- Chapter. 5. Decision Tree -- Chapter. 6. Ensemble - Bagging and Boosting -- Chapter. 7. K-Nearest Neighbors -- Chapter. 8. Naive Bayes -- Chapter. 9. Support Vector Machines: A supervised learning |
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algorithm for Classification and Regression -- Chapter. 10. Clustering Overview -- Chapter. 11. Centroid-based Clustering -- Chapter. 12. Connectivity-based Clustering -- Chapter. 13. Gaussian Mixture Model -- Chapter. 14. Density-based -- Chapter. 15 -- BIRCH -- Chapter. 16. CLARANS -- Chapter. 17. Affinity Propagation Clustering -- Chapter. 18. STING -- Chapter. 19. CLIQUE -- Chapter. 20. Artificial Neural Networks -- Chapter. 21. ANN-based Applications -- Chapter. 22. Automated Tools -- Chapter. 23. DataScientist’s Ultimate Workflow. |
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Sommario/riassunto |
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This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”. The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. |
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