Human-computer interaction (HCI) is a multidisciplinary field that explores the interaction between humans and computers, emphasizing especially the design and use of computer technology. Within this domain, the notion of sense of agency, usually defined as the users perceiving their actions as influencing the system, is of crucial importance. Another central notion is that of socio-technical systems, referring to the interplay between social and technical elements. In this work, we delve into users’ perceptions of their interactions with smart devices, drawing insights from a dataset collected during the COVID-19 outbreak in 2020.

The dataset, gathered through an expansive survey, was distributed in professional and personal networks, offers a window into the perspectives of 587 respondents, predominantly highly educated professionals with significant technological familiarity. While the sample’s specificity limits the generalizability of findings, it offers valuable insights into how tech-savvy users perceive their interactions with smart devices, illuminating the nuanced dynamics of HCI within socio-technical contexts.

One of the survey’s notable aspects was its focus on users’ choice of a single smart device to consider throughout their responses. This deliberate framing encourages a deeper exploration of users’ relationships with specific technologies, shedding light on the intricacies of their interactions. For respondents who did not own multiple smart devices, the instruction was to choose their primary device, such as a computer or mobile phone, thus ensuring consistency in the examination of user experiences.

Within the dataset, various dimensions of users’ interactions with smart devices emerged, encompassing cognitive, emotional, and behavioral aspects. Users’ perceptions of agency, defined as their sense of control and influence over the device, are particularly intriguing. Analysis of the dataset reveals diverse manifestations of agency, ranging from feelings of empowerment and efficiency to frustrations stemming from perceived limitations or complexities in device operation.

Moreover, the socio-technical lens allows us to delve deeper into the contextual factors shaping users’ interactions. The dataset captures not only users’ individual experiences but also the broader socio-cultural and technological landscapes influencing these interactions. Factors such as educational background, professional expertise, age, and gender are among the variables examined, offering insights into how these socio-demographic factors intersect with users’ perceptions of smart device interactions.

The dataset’s utilization in previous research endeavors underscores its significance in advancing our understanding of human-computer interaction, user behavior analysis, and agency. By building upon existing research and leveraging the data available, researchers can uncover deeper insights into the complexities of smart device interactions and contribute to the development of more user-centered technologies.

The COVID-19 context during which the data were collected adds another layer of complexity to the analysis. Due to the increased time spent at home and the increase in remote work, the global pandemic catalyzed increased reliance on technology for work, education, and socialization. Understanding users’ interactions with smart devices within this unique context provides valuable implications for designing technologies that accommodate evolving user needs and preferences.

In conclusion, users’ notions of their interactions with smart devices offer a rich tapestry of insights that intersect with key concepts in HCI, including agency and socio-technical systems. Through the analysis of a dataset collected during the COVID-19 pandemic, we gain valuable insights into users’ perceptions, behaviors, and experiences with smart devices within the context of a rapidly evolving technological landscape. This dataset serves as a springboard for further exploration in the field, driving forward our understanding of human-computer interactions in an increasingly digital world.

Where to find more:

• Agency in Human-Smart Device Relationship: A work in progress description of a research done with this dataset

Initial call for collecting the responses

Link to the dataset (Open Access)

https://doi.org/10.3390/data9040056

https://doi.org/10.34894/traony

Main Personal Related Publication

https://doi.org/10.3390/fi16030090

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• After the initial pain, and maybe a bit of a slow start, your thesis will advance ten times faster and you will be capable to achieve more than you can imagine.
• What you will be learning will not be lost. Many companies use a similar environment (if not the same).
• Many of these resources are free and you will be able to use them for other projects after the end of this academic endeavor.

In other words, you have nothing to lose. All you have to do is to endure a bit of the initial annoyance that is required for a proper warm up of your programming environment. However, the reward is much greater than the initial investment.

In this article, I list only a very few resources and only the ones that are free. In the internet you can find many more.

Programming skills: no matter how much you know, there is always room for knowing more. Most libraries, both public and university, have free subscriptions to courses. For example, the library in my university gives free access to DataCamp to all the students that request it.

If you are not familiar with the platform, it contains a set of courses at all levels and many of them have code examples. As they cover a large range of cases, some of them will be relevant for your thesis. All you have to do is to dig into the list and familiarize with the code that is most promising for the work that you plan to do with your supervisor.

Get a student account in GitHub, it is a free repository that you can use for storing your code. Calming student status will grant you a pro version for free as well as many discounts for products of other companies. In addition, you will also not loose these perks after the graduation – some of them could be useful for you in the future.

Get an account in Kaggle, again, it is a free service that has courses and datasets. They also offer a web based programming environment with the idea that if you will find yourself in need of additional computation, you will use their services. As a general rule, I would recommend to have a local working environment instead of a cloud based like the one of Kaggle. However, a cloud based environment may be useful under certain conditions.

Set up Anaconda (or Eclipse) in your laptop. An Integrated Development Environment is extremely useful when you have to do some professional programming. Anaconda is free, open source and perfect for Python. Eclipse is its counterpart for Java. In the article below you can read about some of the benefits of an IDE. Once you will start using one you will never go back:

This article is a mini-tutorial on how to set up the environment for Anaconda:

Finally, in the article below, you can familiarize with the debug functionality. It is one of the most useful features of an IDE and can really save you a tremendous amount of time:

I would strongly encourage you in enduring the pain of getting familiar with the tools and the resources that I shared with you in this article. In the long run your future self will be extremely thankful.

A working environment is a very personal thing and I can imagine that every supervisor has their own. Consequently, I would encourage you to discuss the details during your first meeting. At the same time, I can imagine that these tools will be, in a way or an other, useful for your future work. In addition, the fact that you will have your working environment ready will allow you to advance conversations that are more specific to your thesis and you will result in a better use of the (limited) time that you will share with your supervisor.

#free #Resources that will #SHARP your #programming #environnement. #Java #pythonprogramming #pythoncode #DataScience #DataAnalytics #data
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By the way, here you can fine my profiles in GitHub Kaggle and DataCamp:

• https://github.com/Nym77
• https://kaggle.com/francescolelli
• https://www.datacamp.com/profile/flelli
• https://www.freecodecamp.org/francesco-lelli

Are you really committed in improving your programming skills?

Free Code Camp is an open source free course that will guide you in a project based journey towards advanced programming skills. It contains also a session on how to pass a programming oriented job interview.

Introduction to Computer Science from Harvard This is a free edX course from Harvard. You can also get a certificate but this will cost you something. Nevertheless the entire course is free.

Something else:

This article (Free Resources That Will Warm Up Your Programming Environment) is part of the miniseries on how to do a good thesis, you can see the full list of post at the following link:

This article is also part of a miniseries on how to become a better programmer. You can see the full list of posts at the following link:

The post Free Resources That Will Warm Up Your Programming Environment appeared first on Francesco Lelli.

Data come in all shapes and forms. If you are doing your thesis, you are in search of a proof of concept In other words, you are attempting to prove the validity of an idea or concept, not to produce an industry/ready solution . Therefore, most of the time, you do not need particularly large datasets. However, you want to be sure that they are of a sufficient quality. After all, you need to elaborate them without introducing too much noise.

What follows is a selected set of resources. It is not exhaustive and I am pretty sure that you will be able to find more by searching the Internet and asking your supervisor. However, this list is a good starting point in case you are trying to build a research/thesis proposal or you are stuck and in search of an idea.

Google datasets search:

• Link: https://datasetsearch.research.google.com/

You guessed right. Google has a dedicated search engine for datasets. It is freely available and index data that implement a particular schema.org format. Some of the data may be behind paywalls. However, academics and students usually can contact the data provider in order to get access to them or to a selected portion that is sufficient for their study.

EU Open Data Portal:

• Link: https://data.europa.eu/euodp/en/data/

Once again, you guessed right. The European Union is committed to foster a strong data transparency policy. At the time of writing this article, I was able to find 15399 different datasets that are freely available. They cover a large variety of topics and are related to all the various arguments under the jurisdiction of the EU.

Yahoo Finance:

• Link: https://finance.yahoo.com/
• Link to the help center: https://help.yahoo.com/kb/download-historical-data-yahoo-finance-sln2311.html

There are plenty of financial DB in the web. Yahoo finance is the most known and straightforward. In case you are looking for daily quotations of various financial assets, this is the place for you. The second link will explain how to download historical data.

More on financial data:

• Link Thomson Reuters API: https://customers.reuters.com/developer/apis_tech.aspx

Some programming knowledge is required. An Application Programming Interface (API) gives you the possibility to access to the data of a website in a programmatic way. Reuters has a dataset that includes financial news and press releases. All you have to do is to write a few lines of code for accessing this information.

Kaggle.com, a community approach:

• Link: https://www.kaggle.com/datasets

Kaggle offers for its members the possibility to access and share data. In addition, it offers also a set of demo code for accessing and manipulating the data. The community is data science driven. However, some of the datasets do not require particular programming skills.

Get your data by yourself:

• link: https://www.programmableweb.com/

There is an ever growing amount of websites that offer the possibility to access them programmatically. Programmableweb is a directory that tries to list them. In addition, it lists links to the proper resources for using them. Note that use the APIs will require some programming. However, you may end up creating original datasets and this is already a valuable outcome of your thesis.

Where to get data for your thesis? In summary:

I hope that by now you realized that datasets are far from been a sparse resource. This collection or resources can serve you as the starting point for finding what you are looking for or for refining/developing your research idea.

Do you need #data? Here you can find a collection of resources for finding your dream datasets. #DataScience #BigData #OpenData
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This article (Where to get Data: a collection of resources for your thesis) is part of the miniseries on how to do a good thesis, you can see the full list of posts at the following link:

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