After COVID-19, new accreditation standards include the need for developing better learning and teaching environments. This will be supported and connected with digitization, entrepreneurship, social inclusion, and a circular economy. The orientation towards equity and quality in education clearly imposes the need for an individual approach to each student separately. This situation is especially pronounced in higher education institutions in the field of technology, whose primary goal is very often individual training for use of highly specialized software and hardware tools. In such a situation, it is necessary to move away from the classical ex-cathedra methodology and develop student-centered learning environments. Global accreditation systems for teaching, learning, practice, and business communication can be simplified using blockchain. On the basis of blockchain technology (BCTs), this paper proposes a Collaborative Learning and Student Work Evaluation (CLSW) model that includes a multi-frontal teaching method (VFN) and combines scientific peer-review standards. BCTs are used to protect student project and assessment data storage and transmission. Assisting higher education institutions in finding “employable capabilities” of proactive students is the idea of CLSW. Before implementing the CLSW paradigm, a poll of lecturers’ views on BCTs was conducted. The poll results show a desire and willingness to teach with BCTs. The model’s fundamental capabilities and the key participants’ duties were described in a project framework. Additionally, this research and proposed model can improve educational process sustainability in general, as it is an open platform easily accessible by all the interested parties, thus contributing to life-long learning.
Kako bi prevenirao odliv korisnika, za telekomunikacionog operatora bilo bi korisno da sazna koji su to parametri koji najviše utiču na odlazak korisnika. Rad se bavi problemom predikcije budućeg odliva korisnika na osnovu istorijskih podataka u programskom jeziku Python. U cilju rešavanja ovog problema pronađen je odgovarajući, otvoreni skup podataka i izvršena istraživačka analiza podataka, kako bi se utvrdio stepen zavisnosti između svake nezavisne i zavisne varijable. Nezavisne varijable opisuju korisnika i servise koje je koristio, dok zavisna varijabla daje odgovor na pitanje: da li je korisnik do tog trenutka napustio operatora? Zatim su kreirani različiti klasifikacioni modeli mašinskog učenja korišćenjem nekih od algoritama implementiranih u Scikit-Learn biblioteci programskog jezika Python. Tačnost najboljih modela iznosila je preko 95%, što je za 10% više od tačnosti null modela, pa se može zaključiti da se predikacija odliva korisnika može uspešno vršiti korišćenjem mašinskog učenja, u programskom jeziku Python.
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