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Learner Reviews & Feedback for Designing, Running, and Analyzing Experiments by University of California San Diego

3.6
stars
581 ratings

About the Course

You may never be sure whether you have an effective user experience until you have tested it with users. In this course, you’ll learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. You will work through real-world examples of experiments from the fields of UX, IxD, and HCI, understanding issues in experiment design and analysis. You will analyze multiple data sets using recipes given to you in the R statistical programming language -- no prior programming experience is assumed or required, but you will be required to read, understand, and modify code snippets provided to you. By the end of the course, you will be able to knowledgeably design, run, and analyze your own experiments that give statistical weight to your designs....

Top reviews

PK

Nov 17, 2020

One of the best courses I have taken in relation to UX. Very good design, engaging lectures and examples, and well designed exams. I learned alot and enjoyed listening to Dr. Webbrock. Kudos to him.

MS

Nov 28, 2020

Great course.

Highly recommended. It was very clear and I'm very thakful because there were many subjects I only understood partially before this course but are now very clear to me.

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126 - 150 of 218 Reviews for Designing, Running, and Analyzing Experiments

By Denys K

•

Mar 29, 2017

Some times i felt lack of explanation. Because there is a lot of math. And quizzes are too big (10 + 32 questions on Week 7). But overall i would say that this course is the only one to which i will definetly come back

By Alice

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Dec 27, 2019

It's a hard course. It's better to have some statistic knowledge. I got a big picture of various methods after finishing this course and I think I need to search for more material to better understand them.

By Ram

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Feb 27, 2019

Without any background in R programming and experiment design, I am able to learn a lot of useful stuff in this course. I wish the last three lessons and quizzes are a little more beginner friendly.

By Lucille

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Jul 2, 2020

This course covers a very wide range of statistical methods but it could use more references to explain in more detail the different tests used. Overall this is a good overview.

By Aswin J E

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Sep 9, 2020

While the course if useful as an introduction to spectrum of tests used in experiment design, heavy external reading is required to truly understand the concepts in depth

By Louis S

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Nov 8, 2017

Great module but it was very difficult for me as I am a 'non-coder' and R Studio was at times very buggy, so my suggestion is to structure the course differently.

By Yao W

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Jul 23, 2017

Students need to acquire additional knowledge to really understand the content.

The courses tend to deliver arcane content in a very sketchy way.

By Javier I R

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Oct 20, 2020

Great course. There is some mistakes in some of the quiz. Nevertheless, the professor gives swift feedback to the questions send.

By kumku q

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Oct 26, 2017

I have noticed some of the tests require knowledge that is gained in the next week. I would suggest fixing that.

By Andrea L

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Jun 17, 2016

The instructor for this course was great. He was very responsive to students' questions concerns.

By Ken O

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Oct 17, 2016

Designing experiments wasn't what I thought, and had a very steep learning curve.

By riley i

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Mar 14, 2016

Thank you for designing a course that demonstrates that UX is not just painting!

By Vin

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Jul 29, 2018

Great course, a little too long compared to other courses

By Ujjwal D

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Mar 23, 2019

Very very helpful for my game design project.

Thank you.

By Lauren Y

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Aug 31, 2018

More comprehensive than in-depth.

By Mohini D

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Jul 11, 2020

Very analytical and challenging!

By Saira B G

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Aug 19, 2016

It looks great

By Yemao

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Aug 8, 2019

Although the lecturer has explained many theoretical aspects regarding experiments preparation and analysis well in the course, I honestly don't enjoy this course. The reasons are: 1. this is a stats course to a large extent. So you better have a good understanding of stats using in psychological studies. Otherwise you will GET LOST. 2. This course uses R instead of Python. If you are R novice like me, you WILL HAVE trouble installing packages, modules or using them because of non-compatible version of R etc. The lecturer sometimes will explicitly point out the "right" codes to execute, but it did not work as many other students suggested. Then you asked a question in the forum but unfortunately you still dont get any answer even after you complete this course. In my opinion this also shows poor preparation of the course materials and lack of updating teaching/exercise materials. Copy and paste code seems to become the pattern for completing course exercises, but i really doubt how much you can really apply for real-world cases. With all due respect this course shows a huge contrast with previous course in the interaction design specialisation and really make me feel a bit disappointed.

By Calogero A

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Feb 22, 2022

Seeing all the negative reviews of this unit really discouraged me, but now, having completed it with full marks with zero prior experience in stastistical analysis, I think the hate is unwarranted.

I do agree that this unit feels out of place in this specialization, and I'd have preferred just a general overview of the topic. But the course is well paced, the instructor very clear, and the provided files very helpful when taking the assessments.

That said, a few of the assessments were at points frustrating and a couple questions required the wrong answer to give you the point (little tip: if you're sure you have the correct result but the quiz won't accept it, try truncating rather than approximating).

All in all I'm glad I did it and I'm sure being able to add some R experience on my CV isn't such a bad thing.

By Santiago B

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May 24, 2019

I have to be honest, I hated this course mostly because I suffered from the start to the end, but don't get me wrong, the concepts are great and very interesting, I didn't know how much information can you get from a simple CSV file; the thing is the course is based in the RStudio tool and I struggled very much with it; missing libraries, constant crashes, and at some point I lost the thread, the concepts start to seem too complex for someone who hasn't much experience with coding so my motivation went away and at the end I was just following instructions to make it through. Maybe if the concepts stay centered on the meaning and not the tool this would be more enjoyable. But I'm very grateful because now I have the sense of data and its importance to backup important design desitions, Thanks!

By JoAnne

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May 15, 2017

The content of this course is quite difficult, especially if you are new to R, and there are no moderators or TAs available to respond to questions or discussions about the content. Nobody replies to the students in the discussion forums. This is surprisingly inconsistent with the rest of my experience with the courses in this specialization. I would warn anyone thinking about taking this lengthy course to realize that you will be alone throughout the class and you should be ready to figure things out alone. Also, during Weeks 7-9, the quiz questions don't seem to relate to Prof. Wobbrock's lecture material at all. I took copious notes and that didn't help me identify where the quiz questions came from.

By Seetha T

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Jun 20, 2017

The professor for the course was able to explain well on most of the concepts on the r programming behind the designing, running and analyzing experiments. He went a little fast on the videos but I was able to catch up by reviewing the transcripts and pausing /playing the videos. The part I was not happy about was that there was no mentor in this course who helped out the students on the discussion board in facing issues of setting up r programming and r studio and also understanding how to do r programming. I was a bit surprised that R is used in interactive design because I heard the main programming languages when it comes for UX researchers and designers are focused on java, c++, and sql.

By Jade O

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Apr 17, 2022

This was a challenging course for a beginner/novice but definitelyl informative. My rating is largely due to the assessment practices. There were errors in the quizzes, and there was often questions on the quizzes that weren't covered in the lectures - in particular - questions on the week 9 quiz. If you're wanting to "extend" students knowledge, do not do it in marked assessments, rather, do it in assignments and/or practice activities. Utlimately this, along with the higher level of R-Studio knowledge that was expected, were among my biggest frustrations. Content was good, if you know R-Studio a bit, it'll be much easier to manipulate the code/formulas.

By Nicholas I

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Nov 3, 2017

Great intro to R-Studio entering pre-made functions with data tables. I feel a little more familiar with basic concepts for quantitative experiments but in terms of understanding the actual statistical functions I still don't feel confident running know when and where to apply the statistical analysis to the data (Seems out of scope for this one course and I would take this course again for more practice). This was the most difficult course in the specialization. Working in R-studio is tricky because you need to know how run the correct version of R, and how to install missing packages in order to run the functions used in the quizzes.

By Matt S

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Jan 17, 2017

Interesting to try R hands on. Could have made more definitive statements on the usefulness of each test (there was lots of 'you could have a look at this analysis, or this one, or this one' without covering why each one would be particularly good for this case). Some of the later exercises also made quite of a leap in terms of what you were expected to know about R syntax, although the follow up answers were good if you didn't understand what was expected.