Quick introduction to field experiments in usability and user experience

This entry tries to be a very short guide on how to perform field experiments for usability and user experience. Fields experiments is reported to have many advantages over laboratory experiments as can be read in the HCI literature [1]. What we try to obtain with field experiments is to overcome the complexity that real contexts represents and cannot be reproduced in a laboratory. As the literature also said, these experiments cannot be replaced by expert evaluations [1] because field experiments focus on the participants and the context: using real users in real context: the weather, user profiles, effectiveness of the locations-based systems, screen resolutions, keyboards… The only way to see how the user and the system performs is taking a ride and practice. As Nielsen [3] and Brewster [2] say, field experiments are always difficult to perform due to the problem that sufficient data must be acquired without interfering in the experiment neither conditioning the participants. Talking about mobile devices in general, its usability is an special concern because of the context and the environment the devices may be used. There are a lot of services or functionalities that depends on the context like location-based services and applications in outdoors which are difficult to simulate in a laboratory. So usability testing in the laboratory will be very limited and will never simulated a fully user case when testing usability in real context with real users.

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The Pythonic Way

Python is a very powerful programming language that understands structural, functional and object oriented programming paradigms. New comers to Python from other languages tend to carry with them their mother (programming) tongue culture. Although they achieve the required task, they might have fallen in the trap of using Python the wrong way. In this post, we cover some efficient tricks to achieve tasks in Python; we call it the Pythonic way. Find an IPython Notebook for all tricks here on our GitHub repository.

Lists, Tuples, Dictionaries and Sets

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From Human Brains to Computer Brains

Intelligent Systems, Artificial Intelligence, Smart Recommenders, Machine Learning and the list of endless fancy words that popup here and there over websites will always have a mystery behind. Over the past few years, we have witnessed great advancements in computer systems. Computers can now take over tasks that we, humans, never thought a computer would be able to do – including tasks that no human brain can efficiently and quickly perform such as looking through thousands of text files and drawing connections between them, reading millions of medical papers and connecting genes to potential diseases. The latter is the job of IBM Watson’s Discovery Advisor, a tool for researchers.

https://www.youtube.com/watch?v=CuLJYdyJSFo

This way it seems that many researchers around the world strive to build computers that can substitute humans completely. The question that arises is: are we going to see computer brains that completely mimic human brains? In our post today, we cover some basics of the research in this direction trying to figure out an answer for the million cells question ..

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R software and tools for everyday use

Long, long time ago … I started with Octave and Matlab.They were amazing and allowed me to solve a lot of interesting problems in my research. I loved the command window of Octave, but I needed the productivity an IDE gives when developing complex calculations. None of the available IDE’s for Octave were not as powerful as the Matlab IDE. The problem was that Matlab was not GNU and buying a license was very expensive. Then, I found R and I realized that none Octave neither Matlab were the tool I needed for my research. I needed advanced project and file management through repositories, fast data manipulation, an easy way to export my calculations, a creative way of authoring reports and a powerful IDE that let me access my beloved command window. Now R gives me all I need and is an important part of my everyday toolbox. For those who does not known R, I must say that R is a well known programming language that is widely used on mathematics, economy, biology… Its main benefits includes the ability to work easily with statistics and data manipulation. R is very popular on academics and research, is GNU, very powerful and have a lot of packages that allows do magical things in a few clicks or with a few commands.

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Women Who Paved the Way for the Internet of Things: A Teacher Finds Her Voice

If my story connects with a single person, I will have succeeded.

I am Kayalvizhi Jayavel, an assistant professor in information technology at Sri Ramaswamy Memorial  (SRM) University in India. I love my job, but 20 years ago I never imagined teaching as my calling.

This is my story.
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