Course Content
Orientation, introduction to the course
1. Human-Robot Interaction (HRI)
2. Research Methods in Human-Robot Interaction
3. Smart Cities & HRI
The demand for city living is already high, and it appears that this trend will continue. According to the United Nations World Cities Report, by 2050, more than 70% of the world's population will be living and working in cities — one of many reports predicting that cities will play an important role in our future (UN-Habitat, 2022). Thus, as cities are growing in size and scope, it is shaped into complex urban landscape where things, data, and people interact with each other. Everything and everyone has become so connected that Wifi too often fails to meet digital needs, online orders don't arrive fast enough, traffic jams still clog the roads and environmental pollution still weighs on cities. New technologies, technical intelligence, and robots can contribute to the direction of finding solutions to ever-increasing problems and assist the evolution of the growing urban space.
Human-Robot Interaction
About Lesson


This unit discusses two crucial steps in experimental research: data cleaning and hypothesis testing. Data cleaning is necessary to identify and correct or remove errors, inconsistencies, and formatting issues in the data collected. The cleaned data must be coded to be interpreted by statistical software, particularly for categorical variables. Hypothesis testing is a statistical analysis that involves testing assumptions about population parameters based on the research hypothesis. A null hypothesis and an alternative hypothesis are formulated, and statistical tests are performed to decide whether to reject or fail to reject the null hypothesis. The discussion in this unit highlights the importance of avoiding too many hypotheses in a single experiment, which increases the complexity and risk of design flaws. Finally, this unit discusses the types of errors in hypothesis testing, such as Type I and Type II errors, and their consequences. Type I errors are more severe than Type II errors as they may lead to a situation worse than the current one. Researchers need to be aware of these errors and their potential consequences when conducting hypothesis testing. 


Lazar, J. , Feng, J. H., Hochheiser, H.  (2017), Research methods in human-computer interaction: Morgan Kaufmann, 2017. 

Rosenthal, R., Rosnow, R., 2008. Essentials of Behavioral Research: Methods and Data Analysis, third ed. McGraw Hill, Boston, MA. 

Sauro, J., and Lewis, J. R., (2016).  Quantifying the user experience: Practical statistics for user research: Morgan Kaufmann. 

Dix, A. (2020). Statistics for HCI: Making Sense of Quantitative Data. Morgan & Claypool Publishers.
Robertson, J., & Kaptein, M. (2016). An introduction to modern statistical methods in HCI (pp. 1-14). Springer International Publishing.
Larson-Hall, J. (2015). A guide to doing statistics in second language research using SPSS and R. Routledge.
Aldrich, J. O. (2018). Using IBM SPSS statistics: An interactive hands-on approach. Sage Publications.