Measurement & Evaluation of HCC Systems
HCC 6400
This is the syllabus website for Clemson University Spring 2025 course HCC 6400: Measurement & Evaluation of HCC Systems.
Meeting information:
Credit hours: 3
Location: McAdams Hall 107 and remote
Day and time: Monday & Wednesday, 2:30 – 3:45 pm
Instructor information:
Prof. Bart Knijnenburg
Email: bartk@clemson.edu
Office hours: Wednesday, 3:45 – 4:45 pm, upon request
Important: The information below may change!
Changes will be announced in class and via Canvas/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 COVID precautions
Those enrolled in remote sections of this course may join using Zoom. The Zoom link may also be used by anyone who feels under the weather, is in quarantine, or is in any other way concerned about their health and safety. Since some people in class have unvaccinated children, I strongly prefer that you are vaccinated and boosted if you join the class in person. Furthermore, if you have a cough or a runny nose, I strongly prefer that you wear a KN95 mask. The adoption of basic public health measures is entirely consistent with the exemplary role we embody as academics.
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 Monday (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 or consult the Internet or an AI system, 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). If you found something online, add a citation. If you used AI, acknowledge this by mentioning the system and the prompt that you used to get to the answer.
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, use AI, or look stuff up online.
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.
Grading
- 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:
- Correlation
- Linear regression
- Independent t-test
- Dependent t-test
- ANOVA
- Factorial ANOVA
- Logistic regression (and Poisson and ordered logistic regression)
- Chi-square test li>Repeated and mixed ANOVA
- Linear mixed-effects models
- Generalized linear mixed-effects models
Course schedule
For your convenience, you can add the course schedule to your calendar (ICAL or HTML).
| Week | Dates | Topic and contents | Work |
| 1.2 | Wednesday Jan 8 | Read BEFORE class: Handbook chapter, Chapter 4 of MacKenzie | |
| 2.1 | Monday Jan 13 | Read before class: Chapter 5 of MacKenzie | |
| 2.2 | Wednesday Jan 15 | Read before class: Chapters 1-4 of Field | |
| 3.1 | Monday Jan 20 | MLKjr day (no class) | |
| 3.2 | Wednesday Jan 22 | Dealing with data - Part 2 | |
| 4.1 | Monday Jan 27 | Read before class: Chapter 5 of Field | |
| 4.2 | Wednesday Jan 29 | Assumptions, sample size - Part 2 | |
| 5.1 | Monday Feb 3 | Read before class: Chapter 6 of Field | |
| 5.2 | Wednesday Feb 5 | Read before class: Chapter 7 of Field | |
| 6.1 | Monday Feb 10 | Regression - Part 2 | Homework 1 available |
| 6.2 | Wednesday Feb 12 | Read before class: Chapter 9 of Field | |
| 7.1 | Monday Feb 17 | Read before class: Chapter 10 of Field | |
| 7.2 | Wednesday Feb 19 | ANOVA - part 2 | Due before class: Homework 1 |
| 8.1 | Monday Feb 24 | Review session 1 | Midterm 1 released after class |
| 8.2 | Wednesday Feb 26 | Due before class: Midterm 1 Read before class: Chapter 12 of Field Homework 2 available | |
| 9.1 | Monday Mar 3 | Midterm 1 feedback | |
| 9.2 | Wednesday Mar 5 | Read before class: Chapter 8 of Field (except 8.9) | |
| 10.1 | Monday Mar 10 | Logistic regression - Part 2 | |
| 10.2 | Wednesday Mar 12 | Read before class: Chapter 18 of Field (except 18.7-18.12) Due before class: Homework 2 Homework 3 available | |
| Mar 17+19 | No class - Spring Break | ||
| 11.1 | Monday Mar 24 | Review session 2 (possibly virtual) | Midterm 2 released after class |
| 11.2 | Wednesday Mar 26 | Make-up lecture (if needed) | Due before class: Midterm 2 |
| 12.1 | Monday Mar 31 | Midterm 2 feedback | Read before class: Chapter 13 of Field |
| 12.2 | Wednesday Apr 2 | Due before class: Homework 3 Read before class: Chapter 14 of Field | |
| 13.1 | Monday Apr 7 | Review session 3 | Midterm 3 released after class |
| 13.2 | Wednesday Apr 9 | Due before class: Midterm 3 Read before class: Chapter 19 of Field | |
| 14.1 | Monday Apr 14 | Multilevel linear models - Part 2 | Homework 4 available |
| 14.2 | Wednesday Apr 16 | Midterm 3 feedback Generalized multilevel linear models - Part 1 | |
| 15.1 | Monday Apr 21 | Generalized multilevel linear models - Part 2 | Due before class: Homework 4 |
| 15.2 | Wednesday Apr 23 | Review session 4 + preview of Advanced Measurement & Evaluation | |
| exam | Final exam released Tuesday April 29 at 5:30pm | Final exam due Thursday May 1 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: http://www.clemson.edu/academics/integrity/plagiarism.html.
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 http://www.clemson.edu/campus-life/campus-services/access/title-ix/. 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: alesias@clemson.edu.