Measurement & Evaluation of HCC Systems

HCC 6400

This is the syllabus website for Clemson University Spring 2023 course HCC 6400: Measurement & Evaluation of HCC Systems.

Meeting information:

Credit hours: 3

Location: Barre Hall B108

Day and time: Monday & Wednesday, 2:30 – 3:45 pm

Instructor information:

Prof. Bart Knijnenburg


Office hours: Wednesday, 3:45 – 4:45 pm, upon request

Important: The information below may change!

Changes will be announced in class and through email.

Course description

This course will teach you how to scientifically evaluate computing systems using a quantitative, user-centric approach. By the end of this course you will be able to statistically evaluate data obtained from a user experiment, a survey, or system usage log files. The basic idea of this class is explained in this TEDx-talk.

What are we going to do?

Course content and structure: This class will be a lot of work, but the advanced methods will give you a competitive advantage over other HCC students at other institutions. This course roughly consists of 3 parts:

  • Part 1 (week 1-4): The practice of experimental evaluation (stuff you may have learned in Research Methods)
  • Part 2 (week 5-8): Basic statistical methods: Correlation, Regression, ANOVA, and T-test (stuff you have forgotten from undergraduate statistics)
  • Part 3 (week 9-15): Non-linear and multilevel statistics (stats for “messy” HCC variables)

Course modality and masks (due to COVID-19)

To promote the safety of my family, and your safety (whom I also consider family), and to give everyone the flexibility to be as safe as possible, I will have an online meeting room open during every class for those who feel under the weather, are in quarantine, or are in any other way concerned about their health and safety. Since some people in class have unvaccinated children, I strongly prefer that you wear a KN95 mask (not just a cloth mask) and are vaccinated and boosted if you join the class in person. The adoption of basic public health measures is entirely consistent with the exemplary role we embody as academics. This includes:

  • Getting the vaccine if you have the opportunity
  • Wearing a mask when you are indoors (and outdoors if distancing is impossible)
  • Respecting each others’ (extended) personal space

Course structure

Course materials: This course uses the following resources:

  • Knijnenburg B. P. and Willemsen, M. C. “Evaluating Recommender Systems with User Experiments”: author copy available for free here.
  • Chapters 4 and 5 of MacKenzie, I. S. “Human-Computer Interaction: An Empirical Research Perspective”: available for free via our library proxy.
  • Almost all chapters of Field, A. et al. “Discovering Statistics Using R”, 1st ed.: for sale or rent on Amazon.

Students are strongly recommended to buy the Field book, since it is an invaluable reference guide for future research projects.

Software: For the most part, we will use R and RStudio. R is like a programming language, and RStudio is an IDE for R (like how Eclipse is an IDE for Java). R and RStudio are both free. For sample size calculations we will use G*Power (also free).

Office hours: Office hours will be directly after class on Wednesday (3:45-4:45 pm). If you want to meet outside of office hours, please send me an email with a few times you are available and the topic you want to talk about.

Slides: Presentation slides are linked in the course schedule below (topics listed in orange are clickable and link to the slides).

Assignments: There will be 4 assignments for this class. There will be some “insight” questions and some data analysis questions. Insight questions usually require a short (1-2 sentence) answer. Data analysis questions should be done in R (unless suggested otherwise), and the requisite dataset will be provided. The answers to data analysis questions should contain the executed R commands, a summary of the output (only the parts that answer the question), and an explanation/description of the results in your own words.

Assignments are each worth 10% of your grade. You are allowed to discuss the assignments, but you have to write your own write-up (i.e. you can discuss, but not copy). If you collaborate with others, please add a collaboration statement to your assignment (a simple statement saying “I collaborated with [name(s)]” is sufficient).

Midterms and final exam: The midterms and the final are take-home and are each worth 15% of your grade. They will be very similar to the assignments, but you have a shorter amount of time for them, and you are not allowed to collaborate.

Prerequisites: This course has no prerequisites, but you will get the most out of it if you have a basic understanding of human-centered computing and quantitative research methods.


  • Assignments: 40% (10% each)
  • Midterms and final: 60% (15% each)

In unusual circumstances these percentages could change, but I do not expect that to happen. Your final grade will be calculated by multiplying the percentages with the points you achieve on each assignment and midterm.

Graduate course grades: In my default graduate grading scheme, 85+ is an A, 80+ is an A-, 75+ is a B+, 70+ is a B, 65+ is a B-, 60+ is a C+, 55+ is a C, 50+ is a C-, 45+ is a D, and less than 45 is an F. I sometimes apply a curve to lower some of these thresholds (this has historically happened mostly for the threshold between B and C).

Cheat sheets

For the homeworks, students should learn to apply the methods using the book and the lecture slides. For the midterms and the final, I will provide a cheat sheet for each method. Cheat sheets will become available after each homework.

I have created the following cheat sheets for your convenience:

Course schedule

For your convenience, you can add the course schedule to your calendar (ICAL or HTML).

WeekDatesTopic and contentsWork
1.2Wednesday Jan 11

Overview and welcome (ONLINE ONLY)

Read BEFORE class: Handbook chapter, Chapter 4 of MacKenzie

2.1Monday Jan 16

MLKjr day (no class)

2.2Wednesday Jan 18

No class

3.1Monday Jan 23

Introduction to HCI evaluation (video)

Read before class: Chapter 5 of MacKenzie

3.2Wednesday Jan 25

Dealing with data - Part 1 (video)


Read before class: Chapters 1-4 of Field

4.1Monday Jan 30

Dealing with data - Part 2 (video)

4.2Wednesday Feb 1

Assumptions, sample size - Part 1 (video)


Read before class: Chapter 5 of Field

5.1Monday Feb 6

Assumptions, sample size - Part 2 (video)

5.2Wednesday Feb 8

Correlation (video)

Read before class: Chapter 6 of Field

6.1Monday Feb 13

Regression - Part 1 (video)


Read before class: Chapter 7 of Field

6.2Wednesday Feb 15

Regression - Part 2 (video)

Homework 1 available


7.1Monday Feb 20

T-tests (video)


Read before class: Chapter 9 of Field

7.2Wednesday Feb 22

ANOVA (video)


Read before class: Chapter 10 of Field

8.1Monday Feb 27

ANOVA - part 2 (video)

Due before class: Homework 1

8.2Wednesday Mar 1

Factorial ANOVA (video)


Read before class: Chapter 12 of Field

Homework 2 available

9.1Monday Mar 6

Review session 1

Midterm 1 released after class

9.2Wednesday Mar 8

Logistic regression - Part 1 (video)


Due before class: Midterm 1

Read before class: Chapter 8 of Field (except 8.9)

10.1Monday Mar 13

Logistic regression - Part 2 (video)

10.2Wednesday Mar 15

Categorical data (video)


Read before class: Chapter 18 of Field (except 18.7-18.12)

Homework 3 available


Mar 20+22

No class - Spring Break

Due by Mar 22, 2:30pm: Homework 2

11.1Monday Mar 27

Review session 2

Midterm 2 released after class

11.2Wednesday Mar 29

Repeated measures (video)


Due before class: Midterm 2

Read before class: Chapter 13 of Field

12.1Monday Apr 3

Mixed designs (video)


Read before class: Chapter 14 of Field

12.2Wednesday Apr 5

Multilevel linear models - Part 1 (video)


Read before class: Chapter 19 of Field

Due before class: Homework 3

13.1Monday Apr 10

Multilevel linear models - Part 2 (video)

Homework 4 available


13.2Wednesday Apr 12

Generalized multilevel linear models

14.1Monday Apr 17

Review session 3

Midterm 3 released after class

14.2Wednesday Apr 19

Make-up class slot (if needed)

Due before class: Midterm 3

15.1Monday Apr 24

Discuss midterm 3 answers

15.2Wednesday Apr 26

Review + preview of Advanced Measurement & Evaluation

Due before class: Homework 4


Final exam released Tuesday May 2 at 5:30pm

Final exam due Thursday May 4 at 5:30pm

Attending class, etc.

Things discussed in class are part of the course materials, and although the slides and recordings will be put on this website, I cannot guarantee that no additional material are discussed in class. Classes will include “follow along” examples, so please have R and RStudio installed on your computer. For the midterms and the final you will also work on your laptop, so make sure it is properly working on exam days!

You will get an email notification in the event that class is cancelled. If the instructor is more than 15 minutes late, you can assume a last-minute cancellation. Hopefully this will not happen!

Academic integrity

Please refer to the following official statement on academic integrity:

As members of the Clemson University community, we are supposed to have a mutual commitment to truthfulness, honor, and responsibility, without which we cannot earn trust and respect of others. Futhermore, we are supposed to recognize that academic dishonesty detracts from the value of a Clemson degree.

Practically speaking: Do not cheat (e.g.: do not collaborate on the midterms and/or final). Plagiarism will not be tolerated, and be dealt with through official university channels, see:

Cheat sheets are created for your convenience to help you with the midterms and the exam. Please do not use cheat sheets (e.g., from last year) when making the homework. Using last years‘ cheat sheets on the homeworks is considered cheating, and will be dealth with accordingly.

Disability access

Students with disabilities requesting accommodations should contact the Office of Student Disability Services in Suite 239, Academic Success Center building 864-656-6848, to discuss specific needs within the first month of classes. Students should present a Faculty Accommodation Letter from Student Disability Services when they meet with instructors. Accomodations are not retroactive and new Faculty Accommodation Letters must be presented each semester.

Title IX (Sexual Harassment) statement

Clemson University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, pregnancy, national origin, age, disability, veteran‘s status, genetic information or protected activity (e.g., opposition to prohibited discrimination or participation in any complaint process, etc.) in employment, educational programs and activities, admissions and financial aid. This includes a prohibition against sexual harassment and sexual violence as mandated by Title IX of the Education Amendments of 1972. This policy is located at Alesia Smith is the Clemson University Title IX Coordinator. She is also the the Executive Director of Equity Compliance. Her office is located at 223 Brackett Hall, phone: 864-656-3181, email: