Measurement & Evaluation of HCC Systems

HCC 6400

This is the syllabus website for Clemson University Spring 2024 course HCC 6400: Measurement & Evaluation of HCC Systems.

Meeting information:

Credit hours: 3

Location: Humanities Hall 264 and remote

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

Instructor information:

Prof. Bart Knijnenburg

Email: bartk@clemson.edu

Office hours: Monday, 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:

Course schedule

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


WeekDatesTopic and contentsWork
1.2Wednesday Jan 10

Overview and welcome

Read BEFORE class: Handbook chapter, Chapter 4 of MacKenzie

2.1Monday Jan 15

MLKjr day (no class)

2.2Wednesday Jan 17

Introduction to HCI evaluation (video, partial)

Read before class: Chapter 5 of MacKenzie

3.1Monday Jan 22

Dealing with data - Part 1 (video)

(dataset)

Read before class: Chapters 1-4 of Field

3.2Wednesday Jan 24

Dealing with data - Part 2 (video)

4.1Monday Jan 29

Assumptions, sample size (video)

(dataset)

Read before class: Chapter 5 of Field

4.2Wednesday Jan 31

Assumptions, sample size - Part 2 (video)

5.1Monday Feb 5

Correlation (video)

Read before class: Chapter 6 of Field

5.2Wednesday Feb 7

Regression - Part 1 (video)

(dataset)

Read before class: Chapter 7 of Field

6.1Monday Feb 12

Regression - Part 2 (video)

Homework 1 available

(dataset)

6.2Wednesday Feb 14

T-tests (video)

(dataset)

Read before class: Chapter 9 of Field

7.1Monday Feb 19

ANOVA (video)

(dataset)

Read before class: Chapter 10 of Field

7.2Wednesday Feb 21

ANOVA - part 2 (video)

Due before class: Homework 1

8.1Monday Feb 26

Review session 1

Midterm 1 released after class

8.2Wednesday Feb 28

Factorial ANOVA (video)

(dataset)

Due before class: Midterm 1

Read before class: Chapter 12 of Field

Homework 2 available

9.1Monday Mar 4

Midterm 1 feedback (video)

9.2Wednesday Mar 6

Logistic regression - Part 1(video)

(dataset)

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

10.1Monday Mar 11

Logistic regression - Part 2 (video)

Due before class: Homework 2

10.2Wednesday Mar 13

No class

Mar 18+20

No class - Spring Break

11.1Monday Mar 25

Review session 2

Midterm 2 released after class

11.2Wednesday Mar 27

Categorical data (video)

(dataset)

Due before class: Midterm 2

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

Homework 3 available

(dataset)

12.1Monday Apr 1

Midterm 2 feedback

Repeated measures (video)

(dataset)

Read before class: Chapter 13 of Field

12.2Wednesday Apr 3

Mixed designs (video)

(dataset)

Due before class: Homework 3

Read before class: Chapter 14 of Field

13.1Monday Apr 8

Review session 3

Midterm 3 released after class

13.2Wednesday Apr 10

Multilevel linear models - Part 1 (video)

(dataset)

Due before class: Midterm 3

Read before class: Chapter 19 of Field

14.1Monday Apr 15

Multilevel linear models - Part 2

Homework 4 available

(dataset)

14.2Wednesday Apr 17

Midterm 3 feedback

Generalized multilevel linear models - Part 1

15.1Monday Apr 22

Generalized multilevel linear models - Part 2

Due before class: Homework 4

15.2Wednesday Apr 24

Review session 4 + preview of Advanced Measurement & Evaluation

exam

Final exam released Tuesday April 30 at 5:30pm

Final exam due Thursday May 2 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.