Learning in a Smart City Environment

Nikolov, Roumen; Shoikova, Elena; Krumova, Milena; Kovatcheva, Eugenia; Dimitrov, Velin; A.Shikalanov,

doi:10.17265/1548-7709/2016.07.003

Cited by 29 publications

(14 citation statements)

References 11 publications

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“…Teleconferences with trainees at remote sites using VideoConferencing technology [37]; • An innovative model of Virtual Business Projects (VBP) based on teamwork [38]; • Remote online lessons based on technologies of synchronous and asynchronous remote communication [39]; • Model of intelligent learning "Monitoring the environmental parameters in a Smart City", which, among other things, provides the creation of remote laboratories for students and so forth [40]; • Creation of breakthrough technologies using virtual reality (VR) environment, games and educational programs, leading to a creative result [41,42].…”

mentioning

confidence: 99%

Innovations in Education—The Development of a New Pedagogical Technology of a Combinational Type, Focused on the Development of Personality of Students

Zhurakovskaya¹,

Sichinava²,

Simakova³

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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The paper considers the problem of adolescent and young students’ personalities’ development in the conditions of face-to-face and distance school learning. The scientific novelty of the study is the proposed classification of pedagogical technologies according to the degree of their novelty: modernized technology, combinational technology, progressive technology, pedagogical technology of the combinational type, the essence of which is shown in its title. A specific model of pedagogical technology of the combinational type “Teaching in cooperation, in a team using flipped classroom” was developed. The developed technology was implemented during the classes of the humanities and natural-mathematical cycles. Nine hundred and eight adolescent and young students (divided into experimental and control groups) and 32 teachers participated in the experimental work. To analyze the differences between the experimental and control groups in terms of the student’s personality manifestation, several criteria and indicators were considered. For mathematical and statistical processing of the obtained results, the multifunctional statistical Fisher’s F-test was used. Analysis of the implementation of the developed technology in the conditions of face-to-face and distance learning showed a positive dynamics of experience formation of the student’s personality manifestation. This allowed the authors to consider the developed pedagogical means effective.

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mentioning

confidence: 99%

Innovations in Education—The Development of a New Pedagogical Technology of a Combinational Type, Focused on the Development of Personality of Students

Zhurakovskaya¹,

Sichinava²,

Simakova³

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

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“…Interestingly, nothing better than O d (log d+1/2 n) was known even non-constructively, even for d = 2, prior to our work. After our result was announced Nikolov [Nik16] further improved this discrepancy bound to O d (log d−1/2 n) using a clever application of Banaszczyk's results [Ban98,Ban12], coming tantalizingly close to the Ω(log d−1 ) lower bound [MNT14]. However, this result is not algorithmic.…”

Section: Applicationsmentioning

confidence: 90%

Algorithmic discrepancy beyond partial coloring

Bansal

Garg

2017

Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing

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The partial coloring method is one of the most powerful and widely used method in combinatorial discrepancy problems. However, in many cases it leads to sub-optimal bounds as the partial coloring step must be iterated a logarithmic number of times, and the errors can add up in an adversarial way.We give a new and general algorithmic framework that overcomes the limitations of the partial coloring method and can be applied in a black-box manner to various problems. Using this framework, we give new improved bounds and algorithms for several classic problems in discrepancy. In particular, for Tusnady's problem, we give an improved O(log 2 n) bound for discrepancy of axis-parallel rectangles and more generally an O d (log d n) bound for d-dimensional boxes in R d . Previously, even non-constructively, the best bounds were O(log 2.5 n) and O d (log d+0.5 n) respectively. Similarly, for the Steinitz problem we give the first algorithm that matches the best known non-constructive bounds due to Banaszczyk [Ban12] in the ℓ ∞ case, and improves the previous algorithmic bounds substantially in the ℓ 2 case. Our framework is based upon a substantial generalization of the techniques developed recently in the context of the Komlós discrepancy problem [BDG16]. project no. 022.005.025. 0 Let (V, S) be a finite set system, with V = {1, . . . , n} and S = {S 1 , . . . , S m } a collection of subsets of V . For a two-coloring χ : V → {−1, 1}, the discrepancy of χ for a set S is defined as χ(S) = | j∈S χ(j)| and measures the imbalance from an even-split for S. The discrepancy of the system (V, S) is defined as disc(S) = minThat is, it is the minimum imbalance for all sets in S, over all possible two-colorings χ. More generally for any matrix A, its discrepancy is defined as disc(A) = min x∈{−1,1} n Ax ∞ . Discrepancy is a widely studied topic and has applications to many areas in mathematics and computer science. In particular in computer science, it arises naturally in computational geometry, data structure lower bounds, rounding in approximation algorithms, combinatorial optimization, communication complexity and pseudorandomness. For much more on these connections we refer the reader to the books [Cha00, Mat09, CST + 14].Partial Coloring Method: One of the most important and widely used technique in discrepancy is the partial coloring method developed in the early 80's by Beck, and its refinement by Spencer to the entropy method [Bec81b, Spe85]. An essentially similar approach, but based on ideas from convex geometry was developed independently by Gluskin [Glu89]. Besides being powerful, an important reason for its success is that it can be applied easily to many problems in a black-box manner and for most problems in discrepancy the best known bounds are achieved using this method. While these original arguments were based on the pigeonhole principle and were nonalgorithmic, in recent years several new algorithmic versions of the partial coloring method have been developed [Ban10, LM12, Rot14a, HSS14, ES14]. In particular, all ...

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“…It is useful to attempt to define smart learning for the purposes of discussion in this paper as definitions vary within the smart learning research community. Much of the relevant research concerns learning within smart cities, or smart learning that is conceptualised and determined by technology, infrastructure and the production and analysis of large datasets (Nikolov et al 2016;Liu et al 2017a;Giannakos et al 2016). In contrast to this are publications concerned with citizens, either as inhabitants of smart cities or as learners within ad-hoc smart learning environments (Giovanella et al 2016;Thomas et al 2016;Mullagh et al 2014).…”

Section: Defining Smart Learningmentioning

confidence: 99%

A smarter knowledge commons for smart learning

Lister

2018

Smart Learn. Environ.

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This paper takes the form of a discussion relating to a smarter knowledge commons, having come about due to implications arising from research into the development of a pragmatic pedagogical 'guide to learning' for smart learning environments. The paper does not discuss any research findings (which have not yet been established), but rather is about attempting to discover through examination of early adopter use cases the underlying challenge for smart learning design in relation to the delivery of personalised geo-spatially relevant knowledge. Solutions for the mapping and delivery of the knowledge web are tentatively suggested, making use of an existing meta-property framework, the Open Graph. Smart learning environments focus on learning in geo-spatially relevant learning locations, with tutors or learners engaged in tasks that may frequently require the searching and selecting of knowledge content to contribute to learning or in the further production of new digital knowledge content. This has led to considerations regarding where and how knowledge content is obtained, provided, produced or shared, and this paper examines issues related to the producing, searching and finding of knowledge content in these learning contexts. Practical examples are provided to illustrate how digital knowledge content plays a pivotal role in learning design and learner interactions taking place in smart learning, both for the content of learning and as part of the process for learning.Emphasis is on open access smart learning in relation to connected and collaborative pedagogical approaches. Considering the future development and pedagogies of open-access smart learning environments, we must ask how the knowledge commons, an integral part of this learning, can become 'smarter' for learning and teaching.

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Learning in a Smart City Environment

Cited by 29 publications

References 11 publications

Innovations in Education—The Development of a New Pedagogical Technology of a Combinational Type, Focused on the Development of Personality of Students

Innovations in Education—The Development of a New Pedagogical Technology of a Combinational Type, Focused on the Development of Personality of Students

Algorithmic discrepancy beyond partial coloring

A smarter knowledge commons for smart learning

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