Rutgers University Student Instructional Rating
(Online Survey - Sakai)
Zijian Guo
Fall 2017, 16:958:588:01 — Data Mining (index #09568)
Enrollment= 46, Responses= 28

Part A: University-wide Questions:
Student Responses Weighted Means
No response
Section Course Level Dept
1. The instructor was prepared for class and presented the material in an organized manner. 0 0 1 3 24 0 4.82 4.82 4.73 4.73
2. The instructor responded effectively to student comments and questions. 0 0 0 7 21 0 4.75 4.75 4.72 4.72
3. The instructor generated interest in the course material. 0 0 2 6 20 0 4.64 4.64 4.48 4.48
4. The instructor had a positive attitude toward assisting all students in understanding course material. 0 0 0 2 26 0 4.93 4.93 4.76 4.76
5. The instructor assigned grades fairly. 0 0 1 5 22 0 4.75 4.75 4.78 4.78
6. The instructional methods encouraged student learning. 0 0 0 4 24 0 4.86 4.86 4.57 4.57
7. I learned a great deal in this course. 0 0 2 3 23 0 4.75 4.75 4.57 4.57
8. I had a strong prior interest in the subject matter and wanted to take this course. 0 0 3 2 22 1 4.70 4.70 4.48 4.48
9. I rate the teaching effectiveness of the instructor as: 0 0 0 5 23 0 4.82 4.82 4.60 4.60
10. I rate the overall quality of the course as: 0 0 0 7 21 0 4.75 4.75 4.57 4.57

What do you like best about this course?:

The course is well-structured and prepared, we can understand the subject matter thoroughly.

Ease of teaching

Instructor was very patient in understanding the questions what we asked. Explained tough topics in a detailed manner...repeated the topics twice which he felt were tough.

professor Guo will introduce every detail behind models to us.

enough notes and explanation

Data mining shows us great view into the world of machine learning, which is very helpful.

the project

I learned a lot regarding machine learning methods.

The material covered is very useful and the corresponding code section is available on book !

Instructor was able to make students understand hard concepts in a easy way.

If you were teaching this course, what would you do differently?:




more math

I would relate the course more to financial market.


More real world example could have been given to understand the concept in real world

In what ways, if any, has this course or the instructor encouraged your intellectual growth and progress?:

We can generate the knowledge learnt here to more applications.

Able to visualize it better

Helped in learning data mining


Make me more interested in ML algorithms and their wide applications.

yes, very well

Since some content in the course is kind of covered in previous course, and when it appeared again in the course, it is deeper than before, which raised my interest to learn relative knowledge by myself.

Give more detail on practical application of material

Other comments or suggestions::

The only part I'm not totally satisfied is our TA, I think they should devote more time and thoughts into this course. Our assignment is graded in a "robust" way, that is we should have a exactly same answer with the one TA has. It's not realistic in real-world problem. The TA should grade the course in terms of the method itself.


Very good course.

good class

I think the course could be more condensed so that more stuff could be taught in one semester. Since some part we could learn it by ourselves after class.

It would be better if providing the solutions of homework