Course Content
Orientation, introduction to the course
0/1
Introduction to course
02:46
1. Human-Robot Interaction (HRI)
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Introduction
01:22
1.1 How has Human-Robot interaction evolved from Human-Computer interaction?
01:34
What are the main facets of Human-Computer interaction?
00:00
What are the main facets of Human-Robot interaction?
00:00
Summary and Exercises 1.1
00:00
1.1 Quiz
1.2 Why is studying Human-Robot interaction essential today?
01:20
What are the diverse roles of robots in today’s world?
00:00
How are robots revolutionizing customer service and sales?
00:00
How are robots enhancing the learning experience?
00:00
In what ways are robots entertaining us?
00:00
How are robots making a mark in healthcare and therapy?
00:00
How can robots serve as efficient personal assistants?
00:00
What roles do robots play in operations and support tasks?
00:00
How are collaborative robots changing the workplace?
00:00
How do self-driving cars communicate with humans?
00:00
How do remotely operated robots function and where are they used?
00:00
Summary and Exercises 1.2
00:00
1.2 Quiz
1.3 What are the existing challenges and limitations in the field of Robotics?
01:37
How do robotics address user expectations effectively?
00:00
How does robot interaction pose a risk for addiction?
00:00
How might robots inadvertently steal users’ attention?
00:00
Why might users lose interest in robots?
00:00
How is robot abuse being identified and tackled?
00:00
Summary and Exercises 1.3
00:00
1.3 Quiz
1.4 The Design process and usability
01:40
How has robot design shifted for better human interaction?
00:00
How is design particularly tailored for Human-Robot interaction?
00:00
How does anthropomorphization influence HRI Design?
00:00
What methods are utilized in HRI design?
00:00
Which tools are essential for prototyping in HRI?
00:00
Summary and Exercises 1.4
00:00
1.4 Quiz
2. Research Methods in Human-Robot Interaction
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Introduction
01:50
2.1 How are descriptive statistics utilized in Human-Robot Interaction research?
03:20
Measures of Central Tendency
00:00
How do measures of spread provide insights in HRI data?
00:00
Why are skewness and kurtosis vital in HRI data analysis?
00:00
How should data be presented and reported in HRI studies?
00:00
Summary and Exercises 2.1
01:23
2.1 Quiz
2.2 How is data prepared and significance tested in HRI research?
05:46
Why is data prepared and significance tested in HRI research?
00:00
How are research hypotheses formulated and tested in HRI?
00:00
What types of errors can occur in hypothesis testing in HRI?
00:00
How do researchers determine the right significance test for their HRI study?
00:00
Summary and Exercises 2.2
01:12
2.2 Quiz
2.3 How do parametric and non-parametric tests differ in HRI research?
02:39
Why are normality tests crucial in HRI data analysis?
00:00
When and why is the one-sample t-test used in HRI?
00:00
How does the independent samples t-test apply to HRI studies?
00:00
Why and when would researchers use a paired samples t-test in HRI?
00:00
When is a one-way ANOVA appropriate for HRI data?
00:00
How does the repeated measures ANOVA cater to HRI research needs?
00:00
When are non-parametric tests preferred in HRI research?
00:00
How is the chi-square test relevant to HRI studies?
00:00
Why might a researcher choose the Mann-Whitney U Test for their HRI data?
00:00
Summary and Exercises 2.3
01:45
2.3 Quiz
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.
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Introduction
02:17
3.1 What are the foundational concepts behind smart cities?
03:04
What is a Smart City?
00:00
How is world urbanization influencing the evolution of smart cities?
00:00
What makes cities Smart?
00:00
What are the anticipated requirements for the next generation of smart citis?
00:00
Exercises
00:00
Quiz 3.1
3.2 Infrastructure, technology, and innovation in smart cities
02:53
What are smart infrastructures?
00:00
How many types of smart infrastructures are there?
00:00
What are the advantages and functionalities of these infrastructures?
00:00
What are the enabling technologies in a smart city?
00:00
How is innovation shaping smart cities?
00:00
3.2 Quiz
3.3 Applications of robotics in Smart Cities
02:30
How are robotic services being applied in smart cities?
00:00
In what ways are robots being integrated into smart cities?
00:00
Exercises
00:00
3.3 Quiz
3.4 How is human-robot coexistence shaping the urban environment?
04:23
What are the opportunities and challenges of integrating robots into urban life?
00:00
How can robots be designed for social acceptability in cities?
00:00
What does it mean to design robots for “cityness”?
00:00
Robots as responsible urban innovation
00:00
3.4 Quiz
3.5 Which cities are pioneering smart city initiatives?
01:25
Which are the top smart cities in the world?
00:00
What are the major initiatives being undertaken by leading smart cities?
00:00
What does future research hold for smart cities and their evolution?
00:00
Exercises
00:00
Human-Robot Interaction
About Lesson

Normality Tests

A normality test is used to determine whether sample data has been drawn from a normally distributed population (within some tolerance). Assumption of normality means that you should make sure your data roughly fits a bell curve shape before running certain statistical tests or regression, as we will see in the assumptions for the parametric tests. 

Two of the most well-known tests of normality are the Kolmogorov-Smirnov Test and the Shapiro-Wilk Test (Lazar, 2017). The Shapiro-Wilk Test is more appropriate for small sample sizes (< 50 samples) but can also handle sample sizes as large as 2000. 

 

For further reading about normality tests and how to use normality test using SPSS you may read this: 

https://statistics.laerd.com/spss-tutorials/testing-for-normality-using-spss-statistics.php

To understand how to conduct a Shapiro-Wilk Normality Test in SPSS, watch this video: 

To understand how to conduct a Kolmogorov-Smirnov Normality Test in SPSS, watch this video: 

References 

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

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

Salcedo, J., & McCormick, K. (2020). SPSS statistics for dummies. John Wiley & Sons.
 
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.
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