STUDENTS' RATINGS ON TEACHER

Faculty Member:  KAN MIN-YEN
Department:  COMPUTER SCIENCE Academic Year:  2004/2005
Faculty:  SCHOOL OF COMPUTING Semester:  2
Module:FOUNDATIONS OF ARTIFICIAL INTELLIGENCE - CS3243
Activity Type:LECTURE
Class Size  /  Response Size  /  Response Rate :53  /  36  /  67.92%
QnItems EvaluatedFac. Member Avg ScoreFac. Member Avg Score Std. DevDept Avg ScoreFac. Avg Score
(a)     (b)(c)     (d)






1The teacher has enhanced my thinking ability. 4.194 0.822 3.747 ( 3.631) 3.736 ( 3.767)
2The teacher provides timely and useful feedback. 4.194 0.822 3.764 ( 3.625) 3.762 ( 3.777)
3The teacher is approachable for consultation. 4.257 0.852 3.820 ( 3.757) 3.800 ( 3.817)
4The teacher has helped me advance my research (if applicable). 4.053 1.079 3.599 ( 3.574) 3.583 ( 3.655)
5The teacher has increased my interest in the subject. 4.222 0.898 3.593 ( 3.543) 3.608 ( 3.674)
6The teacher has helped me acquire valuable/relevant knowledge in the field. 4.167 0.878 3.760 ( 3.664) 3.778 ( 3.805)
7The teacher has helped me understand complex ideas. 4.056 0.924 3.687 ( 3.563) 3.684 ( 3.688)
Average of Qn 1-7 4.171 0.877 3.717 ( 3.625) 3.715 ( 3.745)
8Overall the teacher is effective. 4.222 0.866 3.787 ( 3.653) 3.786 ( 3.807)

Frequency Distribution of responses for Qn 8

Nos. of Respondents(% of Respondents)


|






ITEM\SCORE

|

5

4

3

2

1


|






Self

|

15 (41.67%)

16 (44.44%)

4 (11.11%)

0 (.00%)

1 (2.78%)

Teachers teaching all Modules of the Same Activity Type (Lecture), at the same level within Department

|

132 (16.67%)

363 (45.83%)

215 (27.15%)

54 (6.82%)

28 (3.54%)

Teachers teaching all Modules of the Same Activity Type (Lecture), at the same level within Faculty

|

322 (18.12%)

928 (52.22%)

422 (23.75%)

72 (4.05%)

33 (1.86%)

Note:
1. A 5-point scale is used for the scores. The higher the score, the better the rating.
2. Fac. Member Avg Score: The mean of all the scores for each question for the faculty member.
3. Fac. Member Avg Score Std. Dev: A measure of the range of variability. It measures the extent to which a faculty member's Average Score differs from all the scores in the faculty member's evaluation. The smaller the standard deviation, the greater the robustness of the number given as average.
4. Dept Avg Score :
 (a) the mean score of same activity type (Lecture) within the department.
 (b) the mean score of same activity type (Lecture), at the same module level ( level 3000 ) within the department.
5. Fac. Avg Score :
 (c) the mean score of same activity type (Lecture) within the faculty.
 (d) the mean score of same activity type (Lecture), at the same module level ( level 3000 ) within the faculty.

STUDENTS' COMMENTS ON TEACHER

Faculty Member:  KAN MIN-YEN
Department:  COMPUTER SCIENCE Academic Year:  2004/2005
Faculty:  SCHOOL OF COMPUTING Semester:  2
Module:FOUNDATIONS OF ARTIFICIAL INTELLIGENCE - CS3243
Activity Type:LECTURE

Q9  What are the teacher's strengths?
1.Mr Kan is a good teacher and was very helpful during the tutorials and was always readyly available to provide help/feedback.
2.clear delivery, willingness to clear doubts
3.Very patient in engaging the class for discussion and interactivity
4.Very clear and informative lectures, approachable and friendly. Lectures are interesting and projects are challenging and interesting as well.
5.Very articulate, responsible, focused, dedicated.
6.friendly, initiative
7.Able to explain things in a clear and detailed manner. Have a deep knowledge of the topic.
8.Approachable.
9.Make students actively participate in the lectures by asking questions. This is also a way for him to know whether students understand what he is teaching or not.
10.able to explain concepts well
11.patient

Q10  What improvements would you suggest to the teacher?
1.None.
2.go beyond the book
3.I just can't find anything wrong to say. But i know something isn't working.
4.No comment.
5.none
6.elaborate more on the details

STUDENTS' RATINGS ON TEACHER

Faculty Member:  KAN MIN-YEN
Department:  COMPUTER SCIENCE Academic Year:  2004/2005
Faculty:  SCHOOL OF COMPUTING Semester:  2
Module:FOUNDATIONS OF ARTIFICIAL INTELLIGENCE - CS3243
Activity Type:TUTORIAL
Class Size  /  Response Size  /  Response Rate :42  /  36  /  85.71%
QnItems EvaluatedFac. Member Avg ScoreFac. Member Avg Score Std. DevDept Avg ScoreFac. Avg Score
(a)     (b)(c)     (d)






1The teacher has enhanced my thinking ability. 4.194 0.822 3.843 ( 3.695) 3.868 ( 3.824)
2The teacher provides timely and useful feedback. 4.222 0.832 3.836 ( 3.698) 3.898 ( 3.855)
3The teacher is approachable for consultation. 4.257 0.886 3.913 ( 3.767) 3.967 ( 3.906)
4The teacher has helped me advance my research (if applicable). 4.053 1.079 3.665 ( 3.671) 3.704 ( 3.726)
5The teacher has increased my interest in the subject. 4.194 0.889 3.691 ( 3.587) 3.726 ( 3.716)
6The teacher has helped me acquire valuable/relevant knowledge in the field. 4.250 0.841 3.842 ( 3.704) 3.878 ( 3.841)
7The teacher has helped me understand complex ideas. 4.167 0.878 3.833 ( 3.685) 3.860 ( 3.808)
Average of Qn 1-7 4.201 0.868 3.812 ( 3.687) 3.851 ( 3.816)
8Overall the teacher is effective. 4.222 0.866 3.881 ( 3.738) 3.928 ( 3.877)

Frequency Distribution of responses for Qn 8

Nos. of Respondents(% of Respondents)


|






ITEM\SCORE

|

5

4

3

2

1


|






Self

|

15 (41.67%)

16 (44.44%)

4 (11.11%)

0 (.00%)

1 (2.78%)

Teachers teaching all Modules of the Same Activity Type (Tutorial), at the same level within Department

|

137 (16.61%)

412 (49.94%)

215 (26.06%)

45 (5.45%)

16 (1.94%)

Teachers teaching all Modules of the Same Activity Type (Tutorial), at the same level within Faculty

|

342 (19.59%)

956 (54.75%)

363 (20.79%)

61 (3.49%)

24 (1.37%)

Note:
1. A 5-point scale is used for the scores. The higher the score, the better the rating.
2. Fac. Member Avg Score: The mean of all the scores for each question for the faculty member.
3. Fac. Member Avg Score Std. Dev: A measure of the range of variability. It measures the extent to which a faculty member's Average Score differs from all the scores in the faculty member's evaluation. The smaller the standard deviation, the greater the robustness of the number given as average.
4. Dept Avg Score :
 (a) the mean score of same activity type (Tutorial) within the department.
 (b) the mean score of same activity type (Tutorial), at the same module level ( level 3000 ) within the department.
5. Fac. Avg Score :
 (c) the mean score of same activity type (Tutorial) within the faculty.
 (d) the mean score of same activity type (Tutorial), at the same module level ( level 3000 ) within the faculty.

STUDENTS' COMMENTS ON TEACHER

Faculty Member:  KAN MIN-YEN
Department:  COMPUTER SCIENCE Academic Year:  2004/2005
Faculty:  SCHOOL OF COMPUTING Semester:  2
Module:FOUNDATIONS OF ARTIFICIAL INTELLIGENCE - CS3243
Activity Type:TUTORIAL

Q9  What are the teacher's strengths?
1.nil
2.Min is a very patient tutor, who will always explains concepts which i'm not clear about. Besides that, he's very approachable, making it easier to seek consultation from him. I guess, i slowly begin to understand why he likes to set tough assignments(tough on our brains and on our time!), i guess it's through these assignments that we'll truely learn things from this course. The assignments given, are very challenging, but nonetheless interesting and fun, and very relevant to this course.
3.Very good tutorial style. Keeps me very focused in class. Asks very good questions in class, thus makes me think hard during tutorials.
4.friendly, initiative
5.Able to explain things in a clear and detailed manner. Have a deep knowledge of the topic.
6.He is teaching very well and he is very smart. His english is very fluent.
7.As above.
8.same

Q10  What improvements would you suggest to the teacher?
1.nil
2.newer questions
3.No comment.
4.same

STUDENTS' NOMINATIONS FOR BEST TEACHING

Faculty Member:  KAN MIN-YEN
Department:  COMPUTER SCIENCE Academic Year:  2004/2005
Faculty:  SCHOOL OF COMPUTING Semester:  2

Module Code:CS3243No of Nominations:17

1.Delivers his lecture very clearly and always willing to clear any doubts that his students may be holding. An extremely sincere lecturer.
2.Prof Kan deserves it. A unique interactive lecture style. =)
3.Min is a very patient tutor, who will always explains concepts which i'm not clear about. Besides that, he's very approachable, making it easier to seek consultation from him. I begin to understand why he likes to set tough assignments. It's through these assignments that we'll truely learn things from this course. The assignments given, are very challenging, but nonetheless interesting and fun, and very relevant to this course. Overall, Prof Kan had taught me well with respect to AI and he did arouse interest in me to pursue more on this area.
4.His lectures are very clear and makes us think about the concepts throughout the lectures. His tutorial style is very flexible and I like it. He is very approachable for consultation, which is extremely helpful in a tough module like this.
5.A very friendly and hardworking professor, who introduced refreshing ideas
6.Nice teaching!
7.His way of teaching is very effective and he knows his responsibility to students. He tries to do as much as he can do. His communication skills are perfect. He is a very nice person and he is very smart.
8.He has a good teaching manner