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Due to the increasing growth in available data in recent years, all areas of research and the managements of institutions and organisations, specifically schools and universities, feel the need to give meaning to this availability of data. This article, after a brief reference to the definition of big data, intends to focus attention and reflection...
Context in source publication
Context 1
... theoretical model provides a hierarchy, called DIKW (Data-Information-Knowledge-Wisdom), consisting of a very large base of raw data, which, going towards the top of the pyramid, is subject to an aggregation-contextualisation process (information) and application testing (knowledge). On top of the pyramid, as shown in Figure 5, is confined wisdom, which assumes a level of knowledge that is beyond the scope of a specific application. These cognitive states are then connected in a hierarchical manner, assuming that between them there can be a smooth transition from the bottom to the top. ...
Citations
... The use of appropriate technology is vital to interpret an extensive set of data that can produce relevant and personalized information for users. When this extensive amount of data comes from Virtual Learning Environments and requires treatment and analysis, it is precisely through learning analysis that this is done [Baldassarre 2016]. Learning Analytics consists of an important application of analytical intelligence (Analytics) in the educational context to guide better decision-making through three types of analysis, namely, descriptive, predictive, and prescriptive. ...
Improving the teaching-learning process depends on the availability of useful information for decision-making. However, considering the sizeable increase in data generated by educational platforms, performing statistical analyses which can support proper pedagogical choices for teachers and personalized guidance for students has become a real challenge. This paper is in this context, where the main goal is understanding the data and learning metrics and identifying important information about the studied sample to support personalized feedback in a timely manner, in addition to subsequent quantitative analysis. Thus, this article presents an exploratory analysis of interaction data on an educational platform while applying preparatory simulations for the entrance exam in high school classes. The methodology adopted consisted of organizing and tabulating the data, generating information from graphs, calculating statistics on the variables of interest, and interpreting them. The results indicated the most relevant metrics to predict the student's situation at the end of the course besides the Traditional Score (TS). In addition, feedback examples were proposed based on the identified scenarios from the outcomes of the statistical analysis and learning metrics that aim to expand the evaluative elements beyond right and wrong.
... Znana i popularna piramida DIKW (ang. data, information, knowledge, wisdom) cały czas jest aktualna [6], i pomimo różnych głosów krytyki [7] nadal stanowi fundament różnych branż i gałęzi gospodarki. Prezentacja relacji między danymi, informacjami, wiedzą a czasem mądrością w układzie hierarchicznym jest częścią języka nauki o informacji od wielu lat (RYS. ...
... RYS. 2. Piramida DIKW; rys.: oprac. własne na podstawieBaldassarre, 2016 ...
Proces inwestycyjno-budowlany w Polsce przechodzi głęboką
cyfryzację. W wielu fazach i na różnych etapach tego procesu
wykorzystuje się BIM. W przestrzeni naukowej i biznesowej często
mówi się o „modelu informacyjnym budynku”. Słyszy się frazy
typu „potoki danych”, „faszerowanie informacją”, „strukturyzowane
dane”, „otwarte standardy wymiany danych” itd. W wielu
przypadkach pojęcia „dane” i „informacje” stosuje się zamiennie,
nie zastanawiając się nad ich znaczeniem w kontekście BIM.
Przez to są mylone i czasami prowadzą do błędów poznawczych
czy problemów w komunikacji. Niezależnie od roli (projektant, inwestor,
producent) znajomość obu pojęć i związanych z nimi standardów
jest niezbędna tam, gdzie w procesach pojawia się BIM.
W artykule dokonano głębokiego przeglądu literatury pod kątem
stosowania i znaczenia obu pojęć. Przedstawiono je w konkretnym
studium przypadku w celu lepszego zrozumienia ich definicji.
W artykule podkreślono też znaczenie norm i standardów, które
wyraźnie wskazują jak pracować z danymi i informacjami w BIM.
... Big data has shifted the process of epistemological development by allowing researchers to go beyond the status quo and discover new knowledge that may have otherwise remained unrecognised using a data-driven paradigm (Song & Zhu, 2016). Approaches like natural language processing, predictive analytics, realtime analytics, and social media analytics, for instance, have the power to potentially revolutionise existing scientific knowledge (Baldassarre, 2016). Yet, although analytical technologies have already prompted some radical innovations within the tourism industry, it appears that the majority of existing studies are exploratory and ad hoc (Xiang, 2018). ...
... Based on the notion of learning-by-data, machine learning enables researchers to effectively uncover trends and patterns from large datasets (Song & Zhu, 2016). Whilst having sound knowledge in programming and machine learning is necessary and promising (Prevos, 2017), what is missing in this regard is a scientific motivation (Baldassarre, 2016). Since big data is intrinsically theory-laden, only a search for potential reasoning behind the observed relationships would advance science-driven understanding (dos Santos, 2016). ...
... Lastly, the overlap between computational skills and domain knowledge without sufficient understanding of mathematics and statistics can be viewed as the most serious problem in data science (Baldassarre, 2016). Although researchers might be well-versed in computing science, it is equally critical to understand the underlying mathematical meanings so as to transform statements into theorems (Emmert-Streib et al., 2016). ...
In parallel with the progression of technology, the tourism industry has been continuously confronted with a large amount of data that needs to be systematically analyzed in order to gain significant insights into the science and business sectors. Data science has emerged as an interdisciplinary field where specific competencies from different sub-disciplines come together. This poses far-reaching challenges for both researchers and practitioners alike. To unlock the pillars of data science research and provide a guideline for relevant stakeholders in tourism, this chapter aims to conceptualize the core competencies needed in the data science process. More specifically, it will start with a discussion regarding the interplay between computer science, mathematics and statistics, and domain knowledge. Next, the procedure of data science will be classified into seven distinct phases: (1) topic formulation and relevance for academia and industry, (2) data access and data collection, (3) data pre-processing, (4) feature engineering, (5) analysis, (6) model evaluation and model tuning, and (7) interpretation of results. This chapter will review each stage in depth and evaluate the corresponding level of knowledge and competencies required for each phase. Finally, current implications and potential future directions of data science in the tourism industry will be discussed.
... Baldassarre defines "Big data as a blanket term for the collection of datasets so large or complex that it is difficult to analyse these using traditional data management techniques, such as the relational database management systems (RDBMS)" (Baldassarre, 2016). When it comes to defining Big Data, like it has been observed for AI in the previous chapter, a majority of scholarly published works in the field of Intelligence also describes Big Data at a very high level. ...
The intelligence community and intelligence analysts are faced with the scarcity of human resources and are confronted with an ever-increasing amount of incoming data to be processed. The image of drinking data through a firehose is often used to describe the challenge induced by the advent of big data. This dissertation explores how Artificial Intelligence, supported by foundational technologies originating from the fields of Data Science and Data Management, can offer solutions that would act as a force-multiplier to tackle the challenges of intelligence analysis following the societal digitisation of the past two decades. It does so with a focus on OSINT and SOCMINT data sources, especially within the direction, collection, processing, exploitation and analysis phases of the intelligence production process.
... According to the DIKW framework, data is the result of observation and consists of objective facts, and numbers. Data taken on its own has no meaning (Baldassarre, 2016). Put in context, data produces information. ...
... Knowledge arises when information is related to the experiences, values, insights and competencies of an expert person. Knowledge is contextually synthesised learning (Baldassarre, 2016). If information and knowledge is assimilated into individual experiences (and may transform individual experiences), then wisdom arises. ...
... It is wisdom that enables decision-making and results in evidence-based changes to practice. Wisdom is understanding and is actionable (Baldassarre, 2016). ...
Learning analytics have taken higher education by the proverbial “storm.” Universities primarily employ learning analytics at the level of metrics to satisfy institutional requirements but are also investing significant effort in technical development. In the domain of teaching, learning analytics are making an appearance but are much less developed than in institutional or technical domains. On the basis of the potential of learning analytics to inform teaching practice and thus improve learning experiences, course instructors are now encouraged to use learning analytics at classroom level. Early forages are giving mixed results, and some confusion reigns among teaching staff in relation to the usability/value of learning analytics. The fundamental premise of the present chapter is that if potential of learning analytics to improve learning experiences is to be realized, then learning analytics must shift further into the practice domain, and this requires the projection of learning theory onto learning analytics.
This chapter discusses the intricacies of cybersecurity agents’ perception. It addresses the complexity of perception and illuminates how perception is shaping and influencing the decision-making process. It then explores the necessary considerations when crafting the world representation and discusses the power and bandwidth constraints of perception and the underlying issues of AICA’s trust in perception. On these foundations, it provides the reader with a guide to developing perception models for AICA, discussing the trade-offs of each objective state approximation. The guide is written in the context of the CYST cybersecurity simulation engine, which aims to closely model cybersecurity interactions and can be used as a basis for developing AICA. Because CYST is freely available, the reader is welcome to try implementing and evaluating the proposed methods for themselves.
Among the recent domains specialized in the arena of tracking, examining, and interpreting educational big data (EBD), educational data science (EDS) emerges as a domain that fosters teaching and learning settings, particularly those that use computers and mobile devices linked to the Internet with the aim of adapting and personalizing educational practice according to learners’ profiles. However, so far there is no a clear concept of what really is EDS. Hence, in order to give an answer to the question, this chapter shapes a landscape of EDS that covers from the background and baseline to the trends. In this pathway, a profile of some EDS-related works is outlined, and a taxonomy of EDS is proposed to organize EDS labor and shed light on the nature of the novel domain. As a result, one of the findings reveals EDS is coined from a dual view, where the first covers related fields and the second pursues the definition of its own identity to gain a distinctive place in the arena. Hence, one conclusion acknowledges the convenience of both concepts, inclusive and exclusive, for practical and technical purposes, respectively.KeywordsEducational data scienceData scienceBig dataTeachingLearning
A smart city plays an increasing role in citizens' daily life. A smart city has six main components, namely smart mobility, smart economy, smart governance, smart living, smart environment, and smart people. For the past several decades, government and organizations throughout the world have initiated many smart city projects. Citizens' transportation, education, investment, and other activities have been shifted from traditional to smart models through emerging technologies and the internet. In this paper, following the pioneering work of the intelligent quotient (IQ) test, we propose a framework to measure a city's intelligence level. We also propose a definition for the intelligence of an information system. To evaluate such intelligence, 382 technology indicators have been developed. We have applied smart city IQ testing on the top 100 largest cities in the United States (according to 2017 population). In addition, the smart city IQ score is designed so that it is convertible to the age of a human being. Various text mining and data mining methods (including classification) have been applied. The results of our analysis indicate that the average smart city IQ score is like that of an eight-year-old child. We also show the breakdown of IQ scores in terms of the six main components. Although the evaluation of the smart city projects in the United States is still ongoing, the results obtained so far as reported in this paper can provide important insights for research in this particular field, as well as intelligent information systems in general.
Икономиката в края на 10-те и началото на 20-те години на XXI-ви век се характеризира с високи темпове на дигитализация на всички сфери на стопанския и обществен живот. Един от пътищата за повишаване на ефективността на отделни бизнес-организации и цели сектори на икономиката е усъвършенстване и развитие на използваните информационни системи за управление на бизнеса на базата на дигитализация на управленската дейност и бизнес операциите. Разработването на нови начини за организация на дейността в условия на дигитализация изисква преосмисляне на съществуващите практики за осъществяване на основните стопански операции и дейностите, които ги подпомагат. Възниква обективна необходимост при стопанските операции и дейности да се използват нови методи и средства, които да заместят традиционните плавно и постепенно. Целта на настоящето изследване е да се направи преглед и теоретична обосновка на възможностите за прилагане на дигитализация в секторите строителство и логистика. Авторите се опитват да разгледат от различни гледни точки основните проблеми при дигитализацията на база изследване на съвременни научни публикации по темата за установяване на обективния ход на развитие на информационните и комуникационни технологии.
