scholarly journals Backstaging the teacher: On learner-driven, school-driven and data-driven change in educational technology discourse

2019 ◽  
Author(s):  
Felicitas Macgilchrist
Keyword(s):  
Educational Technology ◽  
Data Analytics ◽  
Data Driven ◽  
Non Profit ◽  
For Profit ◽  
Ethnographic Interviews ◽  
Dimension 2 ◽  
Contemporary Education ◽  
Individual Dimension ◽  
Ongoing Project

It is generally assumed that edtech providers aim to erase power and politics from contemporary education. To explore the discourse on digital education, this paper draws on discourse analysis of ethnographic interviews with for-profit and non-profit organizations in the field. It asks (i) what industry insiders describe as driving change in contemporary educational technology (edtech), and (ii) whether new actors/technologies are shaping a novel educational hegemony, and if so, what this hegemony looks like. This paper presents initial findings from an ongoing project. It observes a shift in who/what drives change: The teacher used to be seen as key. Today, three different drivers are named: (1) learners (individual dimension), (2) schools (systemic dimension), (3) data (analytics dimension). Overall, the paper suggests that power and politics are not at all erased from the edtech industry’s accounts of digital technologies. It illustrates how the socio-material affordances engineered into contemporary technologies effect a change from "education" to "learning", invite particular teaching practices, and thus affect power relations in education.

scholarly journals Backstaging the teacher: On learner-driven, school-driven and data-driven change in educational technology discourse

2019 ◽  
Vol 12 (2) ◽  
pp. 83-103
Author(s):  
Felicitas Macgilchrist
Keyword(s):  
Educational Technology ◽  
New Media ◽  
Digital Technologies ◽  
Policy And Practice ◽  
Non Profit ◽  
For Profit ◽  
Ethnographic Interviews ◽  
The Individual ◽  
Contemporary Education ◽  
Printed Educational Materials

As digital technologies become more prominent in schools, and a host of new media products appear in classrooms, critical questions are being asked about the erasure of power and politics in contemporary education. To explore the discourse on digital education, this paper draws on discourse analysis of ethnographic interviews with for-profit and non-profit organizations in the field. It asks (i) what industry insiders describe as driving change in contemporary educational technology (edtech), and (ii) whether new actors/technologies shaping a novel educational hegemony, and if so, what this hegemony looks like. Initial findings suggest that while the teacher was seen as key to driving change in printed educational materials, three different discourses appear when describing change in today’s educational technology. In the first, learners drive change; the focus lies on the individual dimension. In the second, schools drive change; the systemic dimension. In the third, data drive change; the analytics dimension. Linking these three discourses is a shift from “education” to “learning”. The accounts of educational technology simultaneously advocate for improving opportunities for all students, especially weaker or disadvantaged learners, and also strengthen the hegemonic shift across policy and practice towards an instrumental understanding of education. Overall, the paper suggests that power and politics are by no means erased from the edtech industry’s accounts of digital technologies and datafication. The socio-material affordances engineered into the technologies invite particular teaching practices and thus affect power relations in education.

scholarly journals Performance Evaluation of Sheltered Workshops. Does Legal Status Matter?

2021 ◽  
Vol 13 (3) ◽  
pp. 1100
Author(s):  
María-Celia López-Penabad ◽  
José Manuel Maside-Sanfiz ◽  
Juan Torrelles-Manent ◽  
Carmen López-Andión
Keyword(s):  
Economic Efficiency ◽  
Social Enterprise ◽  
Market Integration ◽  
Legal Status ◽  
Economic Returns ◽  
Truncated Regression ◽  
Social Efficiency ◽  
Non Profit ◽  
Sheltered Workshops ◽  
For Profit

Social enterprise pursues both social and economic goals and is recognized as a formula for achieving sustainable development. Sheltered workshops (SWs) are a manifestation of this phenomenon, their main objective being the labor market integration of disabled people. In this paper, the efficiency of SWs has been studied taking into account the operational and the core social aspects, as well as their distinct nature, namely for-profit or non-profit status. Additionally, we have analyzed the relationship between the social efficiency and the economic returns of these entities. To do this, a semiparametric methodology, combining different data envelopment analysis (DEA) models with truncated regression estimation has been used. It is the non-profit and top-performing SWs that achieve the best social and economic efficiency. For-profit and low-performing SWs show further reductions in social efficiency as a result of the economic crisis and uncertainty in subsidy-related public policies. Their extensive social proactiveness and high economic strength in the crisis period positively influenced their social and economic efficiency. We have also proven that it is the most profitable SWs that have the greatest social efficiency. We consider that our results constitute a useful complement to other evaluation models for social enterprise.

Data-driven campaigns in public sensemaking: Discursive positions, contextualization, and maneuvers in American, British, and German debates around computational politics

2020 ◽  
Vol 45 (s1) ◽  
pp. 535-559
Author(s):  
Christian Pentzold ◽  
Lena Fölsche
Keyword(s):  
United States ◽  
United Kingdom ◽  
Presidential Election ◽  
Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Driven ◽  
Data Sets ◽  
Federal Election ◽  
Online Comments

AbstractOur article examines how journalistic reports and online comments have made sense of computational politics. It treats the discourse around data-driven campaigns as its object of analysis and codifies four main perspectives that have structured the debates about the use of large data sets and data analytics in elections. We study American, British, and German sources on the 2016 United States presidential election, the 2017 United Kingdom general election, and the 2017 German federal election. There, groups of speakers maneuvered between enthusiastic, skeptical, agnostic, or admonitory stances and so cannot be clearly mapped onto these four discursive positions. Coming along with the inconsistent accounts, public sensemaking was marked by an atmosphere of speculation about the substance and effects of computational politics. We conclude that this equivocality helped journalists and commentators to sideline prior reporting on the issue in order to repeatedly rediscover the practices they had already covered.

scholarly journals The truth must prevail: citizens’ rights to know the truth during the era of COVID-19

2020 ◽  
Author(s):  
Philip Joseph D Sarmiento ◽  
John Federick C Yap ◽  
Kevin Aldrin G Espinosa ◽  
Ria P Ignacio ◽  
Carisma A Caro
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Data Analytics ◽  
Data Driven ◽  
Short Report ◽  
Ordinary People ◽  
Health Crisis ◽  
Public Health Crisis

ABSTRACT In a recent short report, the necessity of sophisticated practices in gathering records that would facilitate data sharing yields data-driven analysis in time of COVID-19. Consequently, there is a need to present the truth in data analytics in the era of COVID-19. This paper discusses the urgent call for people handling the COVID-19 data to be ethically responsible in their handling, processing, and reporting that impacts the lives of ordinary people especially in this time of pandemic as public health crisis.

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

2021 ◽  
Keyword(s):  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Reproduction Number ◽  
Healthcare Systems ◽  
Regional Level ◽  
Data Driven ◽  
Effective Response ◽  
Reproduction Numbers ◽  
Exploratory Data ◽  
Using Data

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.

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