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Data-use as a tool for improving teaching, management and student results.

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Data-feedback in teacher training

A study of the improvement of the quality of student teachers’ lessons in interactive (story)book reading through the use of data-feedback on observed lessons. Variables regarding the optimal time use, the quality of instruction and the student teachers’ pedagogical relation with pupils were included in a one group pre-test post-test design.

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Fostering effective teaching behavior through the use of data-feedback

In this study data-feedback in a cyclic model of data-driven teaching was used to enhance the teaching behavior of students registered in a master course for teachers. Differences between pre- and post-test measures in a simple one-group pre-test post-tests design proved to be significant with effect sizes ranging from d = 0.29 to d = 0.76. Improving teaching behavior in a time span of only six weeks on average is remarkable since earlier studies indicated that it takes over 15 years to master complex teaching skills with a ‘natural development’ of teaching skills of about 25% of a standard deviation.

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Perspectives on the Acceptance and Social Implications of Smart Glasses

Smart glasses were perceived to be potentially revolutionary for healthcare, however, there is only limited research on the acceptance and social implications of smart glasses in healthcare. This study aims to get a better insight into the theoretical foundations and the purpose was to identify themes regarding adoption, mediation, and the use of smart glasses from the perspective of healthcare professionals. A qualitative research design with focus groups was used to collect data. Three focus groups with 22 participants were conducted. Data were analyzed using content analysis. Our analysis revealed six overarching themes related to the anticipated adoption of smart glasses: knowledge, innovativeness, use cases, ethical issues, persuasion, and attitude. Nine themes were found related to anticipated mediation and use of smart glasses: attention, emotions, social influences, design, context, camera use, risks, comparisons to known products, and expected reaction and might influence the acceptance of smart glasses.

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Perspectives on the Acceptance and Social Implications of Smart Glasses

Smart glasses were perceived to be potentially revolutionary for healthcare, however, there is only limited research on the acceptance and social implications of smart glasses in healthcare. This study aims to get a better insight into the theoretical foundations and the purpose was to identify themes regarding adoption, mediation, and the use of smart glasses from the perspective of healthcare professionals. A qualitative research design with focus groups was used to collect data. Three focus groups with 22 participants were conducted. Data were analyzed using content analysis. Our analysis revealed six overarching themes related to the anticipated adoption of smart glasses: knowledge, innovativeness, use cases, ethical issues, persuasion, and attitude. Nine themes were found related to anticipated mediation and use of smart glasses: attention, emotions, social influences, design, context, camera use, risks, comparisons to known products, and expected reaction and might influence the acceptance of smart glasses.

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Does Science-Fiction predict [or change] the future?

This research paper looks at a selection of science-fiction films and its connection with the progression of the use of television, telephone and print media. It also analyzes statistical data obtained from a questionnaire conducted by the research group regarding the use of communication media.

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Does Science-Fiction predict [or change] the future?

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Fostering effective teaching behavior through the use of data-feedback

In this study data-feedback in a cyclic model of data-driven teaching was used to enhance the teaching behavior of students registered in a master course for teachers.

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Cold case Solved andamp; Unsolved: Use of digital tools and data science techniques

On average 125 murders take place in the Netherlands on an annual basis. However, not all such incidents can be solved. Currently there are more than 1700 unsolved homicide cases on the shelf at the National Police that classify as a andapos;cold caseandapos;. Investigation into these types of capital offenses takes a lot of time, money, and capacity. Applications of the current working method and available techniques are very labor-intensive and time-consuming. In addition, the pressure on the executive Police officers is high-from the Police organization, the Public Prosecution Service, the media, the next of kin, as well as society in general. From an investigative point of view, it is relevant to provide direction in the criminal investigation and formulate and evaluate various case scenarios, while reducing a risk of andapos;tunnel visionandapos;. From a scientific point of view, more research into homicide cases in the Netherlands is of eminent importance. Remarkably little has been written in scientific literature about this type of crime. The project andapos;Cold Case: Solved andamp; Unsolvedandapos; focused on the use of open, publicly available information sources to collect the data and gain more insight into homicide cases in The Netherlands. Applicability of various modern techniques, such as web-scraping, API software and Artificial Intelligence (AI) was explored to facilitate and automate data collection and processing tasks. A first concept of a andapos;smartandapos; database was proposed, combining a web-based database platform with AI modules to filter and (pre-)process the data. With further development and training of AI modules, such a database might eventually support data-driven generation and/or prioritization of investigative scenarios. The data collected in the process was used in three scientific studies aimed at uncovering the relationships and patterns in the homicide data for The Netherlands.

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Cold case Solved andamp; Unsolved: Use of digital tools and data science techniques

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How to use Data to Increase Bike Use by Travellers

Abstract The Government of the Netherlands wants to be energy neutral by 2050 (Rijksoverheid, sd). A transition towards non-fossil energy sources also affects transport, which is one of the industries significantly contributing to CO2 emission (Centraal Bureau Statistiek, 2019). Road authorities at municipalities and provinces want a shift from fossil fuel-consuming to zero-emission transport choices by their inhabitants. For this the Province of Utrecht has data available. However, they struggle how to deploy data to positively influence inhabitants' mobility behavior. A problem analysis scoped the research and a survey revealed the gap between the province's current data-item approach that is infrastructure oriented and the required approach that adopts traveler’s personas to successfully stimulate cycling. For this more precisely defined captured data is needed and the focus should shift from already motivated cyclists to non-cyclers.

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Products 6.134

Data-use as a tool for improving teaching, management and student results.

Data-feedback in teacher training

Fostering effective teaching behavior through the use of data-feedback

Perspectives on the Acceptance and Social Implications of Smart Glasses

Perspectives on the Acceptance and Social Implications of Smart Glasses

Does Science-Fiction predict [or change] the future?

Fostering effective teaching behavior through the use of data-feedback

Cold case Solved andamp; Unsolved: Use of digital tools and data science techniques

How to use Data to Increase Bike Use by Travellers

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Products 6.134

Data-use as a tool for improving teaching, management and student results.

Data-feedback in teacher training

Fostering effective teaching behavior through the use of data-feedback

Perspectives on the Acceptance and Social Implications of Smart Glasses

Perspectives on the Acceptance and Social Implications of Smart Glasses

Does Science-Fiction predict [or change] the future?

Fostering effective teaching behavior through the use of data-feedback

Cold case Solved andamp; Unsolved: Use of digital tools and data science techniques

How to use Data to Increase Bike Use by Travellers