SARADNJA U DIGITALNOM POSLOVNOM OKRUŽENJU PREDUSLOV ZA POSTIZANJE KONKURENTNOSTI I POSLOVNE IZVRSNOSTI
Abstract
Rad analizira na koji način digitalne tehnologije podržavaju saradnju aktera u ekonomiji i društvu da bi se obezbedila veća ekonomska dinamika razvoja i socijalna inkluzivnost i stabilnost. S druge strane, digitalni prodori kao što su brz napredak u veštačkoj inteligenciji,oblak računarstvu, internetu stvari, analitici podataka, kvantnom skoku računarskih potencijala otvaraju rizike sigurnosti, poverenja, koncentracije moći, socijalnih i digitalnih disproporcija, ekoloških rizika koji se samo kroz saradnju i zajedničku akciju na globalnom nivou mogu rešiti, a njihove negativne posledice svesti na minimum. Poseban akcenat analiza stavlja na digitalne platforme i digitalne poslovne ekosisteme kao nove modele organizovanja ekonomske aktivnosti koji kroz saradnju obezbeđuju kreiranje novih znanja, novih proizvode i nove vrednosti za sve učesnike.
Ključne reči: digitalne tehnologije, saradnja, konkurentnost, razvoj
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Learning style theories have been widely used in adaptive learning systems to enhance learning outcomes. However, the previous studies on adaptive learning systems set a high entry barrier for researchers who lack programming skills, and few of the studies involved authentic everyday learning materials. This author proposes to test the feasibility of eye-tracking technology in identifying learning styles with everyday materials, as well as the identification accuracy. This author selected the Felder-Silverman’s learning style model (FSLSM) as the framework, enlisted the behaviour patterns that can be used to identify the eight learning styles in the FSLSM model, and conducted a quasi-experiment to test whether these behaviour patterns apply to eye movement differences. Then, this author compared the results of eye-tracking identification with participants’ self-report based on Index of Learning Style (ILS) questionnaire for identification accuracy. This quasi-experiment recruited 30 university students, including 19 female and 11 male. Findings showed that eye-tracking technology has the potential to quickly identify learners of different types categorised by the FSLSM theory, with accuracy ranging from 63.50% to 84.67%; however, there are disturbing factors contributing to different levels of identification accuracy, which should be investigated in future research.
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The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.
Unmanned autonomous aerial vehicles have become a real center of interest. In the last few years, their utilization has significantly increased. During the last decade many research papers have been published on the topic of modeling and control strategies of autonomous multirotors.Today, they are used for multiple tasks such as navigation and transportation.Thispaperpresentsthedevelopment of a dynamicmodeling and controlalgorithm-backsteppingcontroller of an autonomoushexa-rotormicrocopter. The autonomous hexa-rotor microcopter is an under-actuated and dynamically unstable nonlinear system. The model that represents the dynamic behavior of the hexa-rotor microcopter is complex. Unmanned autonomous aerial vehicles applications are commonly associated with exploration, inspection or surveillance tasks.
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For a productive and a good life, education is a necessity and it improves individuals' life with value and excellence. Also, education is considered a vital need for motivating self-assurance as well as providing the things are needed to partake in today's World. Throughout the years, education faced a number of challenges. Different methods of teaching and learning are suggested to increase the learning quality. In today's world, computers and portable devices are employed in every phase of daily life and many materials are available online anytime, anywhere. Technologies like Artificial Intelligence had a surprising evolution in many fields especially in educational teaching and learning processes. Higher education institutions have started to adopt the use of technology into their traditional teaching mechanisms for enhancing learning and teaching. In this paper, two datasets have been considered for the prediction and classification of student performance respectively using five machine learning algorithms. Eighteen experiments have been performed and preliminary results suggest that performances of students might be predictable and classification of these performances can be increased by applying pre-processing to the raw data before implementing machine learning algorithms.