What is the loop of interaction?
In human-computer interaction, or HCI, the loop of interaction refers to the flow of information between ourselves (humans) and the tablet, computer, or device we are using. The loop of interaction includes sensory-based information, like visuals or audio signals, as well as some of the things we may or may not be able to see in our environments. For example, part of the loop of interaction involves how well we can complete our task (fit). If we are using a tablet or smartphone to make a complex model of a building, our loop of interaction would involve a bad task-technology fit. We cannot successfully build a model if we do not have the right tools or the right computer. Or we may have the right tools, but not know how to use them. Therefore, the loop of interaction is not optimized to help the user (us) complete our task (building the model).
What is cognitive dissonance?
We all know those people that say one thing and then do the opposite. For example, you might have a friend who values the environment and sustainability, but drives an expensive car that is not fuel efficient. Cognitive dissonance strikes when we know we should be writing our research paper, but Netflix shows are more intriguing. We then tell ourselves, "it's been a stressful week so I deserve to relax." We stay up late watching "just one more episode" and then we start our paper four hours before it is due. We promise ourselves, "next time I will start my assignments early." This justification of our behaviors is our way of adapting to the stress we created when our behaviors did not reflect our value of being a good student and getting work done on time. We refer to this in psychology as cognitive dissonance. It is applicable to people from all walks of life, not just students.
What are measures of central tendency and how do I use them to interpret a set of data?
Measures of central tendency give us a single value that can help us explain our data. You can think of these measures: the mean, median, and mode just like the three musketeers. They each shed light on a data set in a different way, and you will have to "fight" through their limitations (and their advantages) to better understand what these numbers tell you. The mean represents the arithmetic average. You can calculate it by adding all the numbers in the set together and dividing by the exact number of values in the set. It will give you an "overall score," which is common in things like sports (batting averages for baseball players). Usually the mean is not a number found in your original set of data and it can be affected by extremely high or low numbers in the data (outliers). The median is the "middle point" of the set. This number represents the number that splits the data set in half. It is less affected by extreme outliers and it sometimes must be calculated. The mode represents the most frequent number in the data set. It is important because it is not affected by outliers as much as the other measures, and it can be found in both numerical and categorical data sets. For example, you can have two students with the same GPA in your class or two soccer players with the same first name on the team roster. It can also be conceptualized as the "most popular" value.