Data
TurkiyeStudentEvaluation

TurkiyeStudentEvaluation

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Abstract: This data set contains a total 5820 evaluation scores provided by students from Gazi University in Ankara (Turkey). There is a total of 28 course specific questions and additional 5 attributes. Source: Ernest Fokoue Center for Quality and Applied Statistics Rochester Institute of Technology 98 Lomb Memorial Drive Rochester, NY 14623, USA eMaıl: epfeqa '@' rit.edu Necla Gunduz Department of Statistics Faculty of Science, Gazi University Teknikokullar,06500 Ankara, Turkey eMail: ngunduz '@' gazi.edu.tr gunduznecla '@' yahoo.com Data Set Information: N/A Attribute Information: instr: Instructor's identifier; values taken from {1,2,3} class: Course code (descriptor); values taken from {1-13} repeat: Number of times the student is taking this course; values taken from {0,1,2,3,...} attendance: Code of the level of attendance; values from {0, 1, 2, 3, 4} difficulty: Level of difficulty of the course as perceived by the student; values taken from {1,2,3,4,5} Q1: The semester course content, teaching method and evaluation system were provided at the start. Q2: The course aims and objectives were clearly stated at the beginning of the period. Q3: The course was worth the amount of credit assigned to it. Q4: The course was taught according to the syllabus announced on the first day of class. Q5: The class discussions, homework assignments, applications and studies were satisfactory. Q6: The textbook and other courses resources were sufficient and up to date. Q7: The course allowed field work, applications, laboratory, discussion and other studies. Q8: The quizzes, assignments, projects and exams contributed to helping the learning. Q9: I greatly enjoyed the class and was eager to actively participate during the lectures. Q10: My initial expectations about the course were met at the end of the period or year. Q11: The course was relevant and beneficial to my professional development. Q12: The course helped me look at life and the world with a new perspective. Q13: The Instructor's knowledge was relevant and up to date. Q14: The Instructor came prepared for classes. Q15: The Instructor taught in accordance with the announced lesson plan. Q16: The Instructor was committed to the course and was understandable. Q17: The Instructor arrived on time for classes. Q18: The Instructor has a smooth and easy to follow delivery/speech. Q19: The Instructor made effective use of class hours. Q20: The Instructor explained the course and was eager to be helpful to students. Q21: The Instructor demonstrated a positive approach to students. Q22: The Instructor was open and respectful of the views of students about the course. Q23: The Instructor encouraged participation in the course. Q24: The Instructor gave relevant homework assignments/projects, and helped/guided students. Q25: The Instructor responded to questions about the course inside and outside of the course. Q26: The Instructor's evaluation system (midterm and final questions, projects, assignments, etc.) effectively measured the course objectives. Q27: The Instructor provided solutions to exams and discussed them with students. Q28: The Instructor treated all students in a right and objective manner. Q1-Q28 are all Likert-type, meaning that the values are taken from {1,2,3,4,5} Relevant Papers: N/A Citation Request: If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository: Gunduz, G. & Fokoue, E. (2013). UCI Machine Learning Repository [[Web Link]]. Irvine, CA: University of California, School of Information and Computer Science. Here is a BiBTeX citation as well: @misc{GunduzFokoue:2013 , author = 'Gunduz, N. and Fokoue, E.', year = '2013', title = '{UCI} Machine Learning Repository', url = '[Web Link]', institution = 'University of California, Irvine, School of Information and Computer Sciences' }

33 features

instrnumeric3 unique values
0 missing
classnumeric13 unique values
0 missing
nb.repeatnumeric3 unique values
0 missing
attendancenumeric5 unique values
0 missing
difficultynumeric5 unique values
0 missing
Q1numeric5 unique values
0 missing
Q2numeric5 unique values
0 missing
Q3numeric5 unique values
0 missing
Q4numeric5 unique values
0 missing
Q5numeric5 unique values
0 missing
Q6numeric5 unique values
0 missing
Q7numeric5 unique values
0 missing
Q8numeric5 unique values
0 missing
Q9numeric5 unique values
0 missing
Q10numeric5 unique values
0 missing
Q11numeric5 unique values
0 missing
Q12numeric5 unique values
0 missing
Q13numeric5 unique values
0 missing
Q14numeric5 unique values
0 missing
Q15numeric5 unique values
0 missing
Q16numeric5 unique values
0 missing
Q17numeric5 unique values
0 missing
Q18numeric5 unique values
0 missing
Q19numeric5 unique values
0 missing
Q20numeric5 unique values
0 missing
Q21numeric5 unique values
0 missing
Q22numeric5 unique values
0 missing
Q23numeric5 unique values
0 missing
Q24numeric5 unique values
0 missing
Q25numeric5 unique values
0 missing
Q26numeric5 unique values
0 missing
Q27numeric5 unique values
0 missing
Q28numeric5 unique values
0 missing

62 properties

5820
Number of instances (rows) of the dataset.
33
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
33
Number of numeric attributes.
0
Number of nominal attributes.
3.29
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-1.03
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
-0.2
Third quartile of skewness among attributes of the numeric type.
2.44
Maximum skewness among attributes of the numeric type.
0.53
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.29
Third quartile of standard deviation of attributes of the numeric type.
3.69
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.97
First quartile of kurtosis among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
3.07
First quartile of means among attributes of the numeric type.
-0.72
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
3.17
Mean of means among attributes of the numeric type.
-0.41
First quartile of skewness among attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
1.27
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.01
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
-0.87
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-0.21
Mean skewness among attributes of the numeric type.
3.17
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
1.32
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.29
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.41
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
1.28
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
4.79
Maximum kurtosis among attributes of the numeric type.
1.21
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
-0.78
Third quartile of kurtosis among attributes of the numeric type.
7.28
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

5 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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