Digital Footprint and Education: Some Remarks

2021 ◽  
pp. 485-493
Author(s):  
A. A. Balyakin ◽  
M. V. Mamonov ◽  
M. V. Nurbina ◽  
S. B. Taranenko
Keyword(s):  
Digital Footprint

Diagnostics of professional competence of IT students based on digital footprint data

2020 ◽  
pp. 4-11
Author(s):  
I. G. Zakharova ◽  
Yu. V. Boganyuk ◽  
M. S. Vorobyova ◽  
E. A. Pavlova
Keyword(s):  
Information Technology ◽  
Educational Program ◽  
Professional Competence ◽  
Objective Data ◽  
Text Data ◽  
Data Set ◽  
Job Requirements ◽  
Digital Footprint ◽  
Graduate Employment ◽  
The University

The article goal is to demonstrate the possibilities of the approach to diagnosing the level of IT graduates’ professional competence, based on the analysis of the student’s digital footprint and the content of the corresponding educational program. We describe methods for extracting student professional level indicators from digital footprint text data — courses’ descriptions and graduation qualification works. We show methods of comparing these indicators with the formalized requirements of employers, reflected in the texts of vacancies in the field of information technology. The proposed approach was applied at the Institute of Mathematics and Computer Science of the University of Tyumen. We performed diagnostics using a data set that included texts of courses’ descriptions for IT areas of undergraduate studies, 542 graduation qualification works in these areas, 879 descriptions of job requirements and information on graduate employment. The presented approach allows us to evaluate the relevance of the educational program as a whole and the level of professional competence of each student based on objective data. The results were used to update the content of some major courses and to include new elective courses in the curriculum.

Digital Footprint of Crimes : Monograph

2021 ◽  
Author(s):  
A. M. Bagmet ◽  
V. V. Bychkov ◽  
S. Yu. Skobelin ◽  
N. N. Ilyin
Keyword(s):  
Digital Footprint

Terror, Territoriality, Temporality: Hypermedia Events in the Age of Islamic State

2017 ◽  
Vol 19 (2) ◽  
pp. 170-176 ◽  
Author(s):  
Marwan M. Kraidy
Keyword(s):  
Global Network ◽  
Islamic State ◽  
Productive Force ◽  
Digital Footprint ◽  
Global Event ◽  
Space And Time ◽  
Media Events ◽  
War Machine

Considering the group that calls itself Islamic State (IS) as a “war machine,” an ever-shifting combination of humans and technology, this article articulates, from a Deleuzian perspective, terror, territoriality, and temporality as constitutive of events. It explores terrorism as a hypermedia event that resists conceptual containment in Dayan and Katz’s three categories of “contest,” “conquest,” or “coronation.” It builds on work that recognizes the globality of media events. The article uses the rise of IS to explore events as a peculiar articulation of space and time, and draws on the global “network-archive” that IS created (its digital footprint), the referentiality of which means that we experience IS depredations as one continuous “global event chain.” In this analysis, media events are a productive force that articulates territoriality and temporality through affect.

Defining Terms for Climate Literacy

2021 ◽  
Author(s):  
Katie Singer
Keyword(s):  
Supply Chain ◽  
Energy Use ◽  
Internet User ◽  
Solar Panels ◽  
Solar Grade Silicon ◽  
The Public ◽  
Digital Footprint ◽  
Carbon Neutrality ◽  
Grade Silicon ◽  
Climate Literacy

<div> </div><div>To move forward with substantial, constructive actions that reduce overall consumption and emissions, the public, scientists and policymakers need agreement about our terms. Terms like "sustainability," "zero-emissions" and "carbon-neutrality" tend to focus on a device or vehicle's energy use and emissions during operation--and to exclude energy use and emission during extraction, smelting, manufacturing and recycling or discard. What do such exclusions mean for e-vehicles, smartphones and solar panels? How can we encourage learning about and reducing electronics' true costs? Katie Singer will describe the process involved in manufacturing electronic-grade silicon (similar to solar-grade silicon), and propose that every Internet user learn the international supply chain of one substance (of 1000+) in their device. She will also propose ways to counterbalance a digital footprint. </div>

Digital footprints in the university space

2021 ◽  
Vol 4 (2) ◽  
pp. 52-72
Author(s):  
Timur Khusyainov
Keyword(s):  
Personal Data ◽  
Educational Environment ◽  
Educational Process ◽  
Global Network ◽  
Ethical Standards ◽  
Digital Footprint ◽  
Digital Environments ◽  
The Moment ◽  
The University ◽  
Digital Footprints

This work considers the use of digital traces in the educational environment and the specifics of their collection and analysis at the university. One way or another, all participants in the educational process, as well as those who can potentially become them, for example, applicants, leave digital traces in the digital environments of the university and the Global Network in general, and these traces can be analyzed. At the same time, even the university itself as an organization leaves a certain digital footprint on the Internet. At the moment, most researchers are very optimistic, contemplating on what positive changes can be brought by the analysis of digital traces of applicants, students and teachers for the development of the university itself, the educa-tional process, and the formation of individual learning paths. In contrast to this, the author identifies a number of possible prospects for the analysis of Big Data and the use of Artificial Intelligence for education at the university of the future. Attention is focused on how this can affect the safety of the environment and conflict with ethical standards. Participants in the educational process, falling under the analysis of their digital traces, can both suffer because of them, even if their activities have not been in any way connected with the university, and begin to hide their true digital identity, creating «false» digital traces and becoming anon-ymous. The author assumes that an increase in such control covering actions, thoughts and emotions naturally results in the emergence of the concept of a «Dark» University, which distances itself as much as possible from such methods of analyzing personal data.

scholarly journals Equity and Digital Literacies: Issues of Access, Ethics and Engagement in a Youth Community Setting

2018 ◽  
Vol 20 (3) ◽  
pp. 67-72
Author(s):  
Colin Andrew Ford
Keyword(s):  
Digital Technology ◽  
Digital Literacy ◽  
Community Setting ◽  
Unintended Consequences ◽  
Ethical Challenges ◽  
Ethical Practices ◽  
Digital Footprint ◽  
Youth Agency ◽  
Youth Community ◽  
Prevalent Form

This article reports on the issue of confidentiality faced by a community youth agency that provides access to digital technology for homeless or street-involved youth. Social media is the prevalent form of communication in displaced communities and presents certain ethical challenges as a result of creating and sharing media with potential unintended audiences. Ensuring ethical practices is a key aspect of the ongoing process of developing digital literacy that changes as technology evolves. It requires the facilitator’s focused attention to guide the youth in their ability to consider their digital footprint and potential unintended consequences of their practices.

Predictive Credit Risk Analytics Using Borrowers' Digital Footprint and Methods of Statistical Machine Learning

2021 ◽  
Vol 12 (7) ◽  
pp. 358-372
Author(s):  
E. V. Orlova ◽  
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Risk Profile ◽  
Classification Model ◽  
Gradient Boosting ◽  
Suggested Approach ◽  
Digital Footprint ◽  
Credit Risks ◽  
Stochastic Gradient Boosting ◽  
Boosting Method

The article considers the problem of reducing the banks credit risks associated with the insolvency of borrowers — individuals using financial, socio-economic factors and additional data about borrowers digital footprint. A critical analysis of existing approaches, methods and models in this area has been carried out and a number of significant shortcomings identified that limit their application. There is no comprehensive approach to identifying a borrowers creditworthiness based on information, including data from social networks and search engines. The new methodological approach for assessing the borrowers risk profile based on the phased processing of quantitative and qualitative data and modeling using methods of statistical analysis and machine learning is proposed. Machine learning methods are supposed to solve clustering and classification problems. They allow to automatically determine the data structure and make decisions through flexible and local training on the data. The method of hierarchical clustering and the k-means method are used to identify similar social, anthropometric and financial indicators, as well as indicators characterizing the digital footprint of borrowers, and to determine the borrowers risk profile over group. The obtained homogeneous groups of borrowers with a unique risk profile are further used for detailed data analysis in the predictive classification model. The classification model is based on the stochastic gradient boosting method to predict the risk profile of a potencial borrower. The suggested approach for individuals creditworthiness assessing will reduce the banks credit risks, increase its stability and profitability. The implementation results are of practical importance. Comparative analysis of the effectiveness of the existing and the proposed methodology for assessing credit risk showed that the new methodology provides predictive ana­lytics of heterogeneous information about a potential borrower and the accuracy of analytics is higher. The proposed techniques are the core for the decision support system for justification of individuals credit conditions, minimizing the aggregate credit risks.

Representing the Self Through the Visualization of Personal Data

2021 ◽  
Vol 12 (1) ◽  
pp. 1-12
Author(s):  
Catarina Sampaio ◽  
Luísa Ribas
Keyword(s):  
Digital Media ◽  
Data Visualization ◽  
Personal Data ◽  
Physical Reality ◽  
Digital Footprint ◽  
Identity Based ◽  
The Common ◽  
Project Data ◽  
Visualization Techniques

The representation of identity in digital media does not necessarily have to be conceived on the basis of criteria that mimic physical reality. This article presents a model for representing individual identity, based on the recording of human experience in the form of personal data, as an alternative to the common forms of mimetic portraiture. As such, the authors developed the project Data Self-Portrait that aims to explore the creative possibilities associated with the concept of data portrait. It can be described as a means of representing and expressing identity through the application of data visualization techniques to the domain of portraiture, according to an exploratory design approach, based on visualizing the digital footprint. It thus seeks to develop design proposals for representing identity that respond to the growing dematerialization of human activities and explores the representational and expressive role of data visualization, according to a creative use of computational technologies.

scholarly journals Comparison of ratio loaded and unloaded foot area of flat foot and healthy foot in younger adults

2018 ◽  
Vol 159 ◽  
pp. 02019 ◽  
Author(s):  
Gunawan Dwi Haryadi ◽  
Dwi Basuki Wibowo ◽  
Achmad Widodo ◽  
Agus Suprihanto
Keyword(s):  
Clinical Examination ◽  
Area Ratio ◽  
3D Scanner ◽  
Flat Foot ◽  
Younger Adults ◽  
Digital Footprint ◽  
Footprint Area

This study is aimed to investigate loaded and unloaded foot area ratio (RFA, ratio of foot area) as special tests for the basis of clinical examination of flat foot and healthy foot. Type of foot is determined by Cavanagh’s arch indexes (AI) which is the ratio between mid foot area to entire footprint area excluding the toes. Type of foot is called high arch when AI<0.21, normal/healthy foot when 0.26>AI≥0.21 and flat foot when AI>0.26. The entire loaded foot and footprint area for evaluating AI derived from a digital footprint is modified from document scanner, while the entire unloaded foot area derived from a 3D scanner. One hundred and two healthy students (87 males and 15 females, average aged 20 years and average BMI 22.51 kg/m2) is asked voluntarily for doing footprint and scan. From 102 subjects found 63 participants identified as flat foot and 31 subjects are healthy feet. This study proves that the higher the value of AI the higher the value of RFA and foot type can be predicted by the value of RFA. For type of foot is high arch RFA<0.49, for healthy foot 0.55>RFA≥0.49 and for flat foot RFA>0.55.

Sign in / Sign up

Export Citation Format

Share Document