Data
user-knowledge

user-knowledge

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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  • mf_less_than_80 study_123 study_50 study_7 study_88
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Author: Source: UCI Please cite: H. T. Kahraman, Sagiroglu, S., Colak, I., Developing intuitive knowledge classifier and modeling of users' domain dependent data in web, Knowledge Based Systems, vol. 37, pp. 283-295, 2013. * Title: User Knowledge Modeling Data Set * Abstract: It is the real dataset about the students' knowledge status about the subject of Electrical DC Machines. The dataset had been obtained from Ph.D. Thesis. * Source: -- Creators: Hamdi Tolga Kahraman (htolgakahraman '@' yahoo.com) -- Institution: Faculty of Technology, Department of Software Engineering, Karadeniz Technical University, Trabzon, Turkiye -- Creators: Ilhami Colak (icolak '@' gazi.edu.tr) -- Institution: Faculty of Technology, Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkiye -- Creators: Seref Sagiroglu (ss '@' gazi.edu.tr) -- Institution: Faculty of Technology, Department of Computer Engineering, Gazi University, Ankara, Turkiye -- Donor: undergraduate students of Department of Electrical Education of Gazi University in the 2009 semester -- Date: October, 2009 * Data Set Information: -- The users' knowledge class were classified by the authors using intuitive knowledge classifier (a hybrid ML technique of k-NN and meta-heuristic exploring methods), k-nearest neighbor algorithm. See article for more details on how the users' data was collected and evaluated by the user modeling server. H. T. Kahraman, Sagiroglu, S., Colak, I., Developing intuitive knowledge classifier and modeling of users' domain dependent data in web, Knowledge Based Systems, vol. 37, pp. 283-295, 2013. * Attribute Information: STG (The degree of study time for goal object materails), (input value) SCG (The degree of repetition number of user for goal object materails) (input value) STR (The degree of study time of user for related objects with goal object) (input value) LPR (The exam performance of user for related objects with goal object) (input value) PEG (The exam performance of user for goal objects) (input value) UNS (The knowledge level of user) (target value) * Relevant Papers: 1. H. T. Kahraman, Sagiroglu, S., Colak, I., Developing intuitive knowledge classifier and modeling of users' domain dependent data in web, Knowledge Based Systems, vol. 37, pp. 283-295, 2013. 2. Kahraman, H. T. (2009). Designing and Application of Web-Based Adaptive Intelligent Education System. Gazi University Ph. D. Thesis, Turkey, 1-156.

6 features

Class (target)nominal5 unique values
0 missing
V1numeric112 unique values
0 missing
V2numeric103 unique values
0 missing
V3numeric94 unique values
0 missing
V4numeric93 unique values
0 missing
V5numeric89 unique values
0 missing

62 properties

403
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
5
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.
5
Number of numeric attributes.
1
Number of nominal attributes.
0.11
First quartile of skewness among attributes of the numeric type.
0.41
Mean of means among attributes of the numeric type.
0.21
First quartile of standard deviation of attributes of the numeric type.
0.22
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
2.05
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.
-0.99
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
5
Average number of distinct values among the attributes of the nominal type.
0.43
Second quartile (Median) of means 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.
0.39
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
32.01
Percentage of instances belonging to the most frequent class.
0.24
Mean standard deviation of attributes of the numeric type.
0.41
Second quartile (Median) of skewness among attributes of the numeric type.
129
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.25
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
-0.17
Maximum kurtosis among attributes of the numeric type.
0.35
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-0.27
Third quartile of kurtosis among attributes of the numeric type.
0.46
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
83.33
Percentage of numeric attributes.
0.46
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
5
The minimal number of distinct values among attributes of the nominal type.
16.67
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
5
The maximum number of distinct values among attributes of the nominal type.
0.02
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
0.66
Third quartile of skewness among attributes of the numeric type.
0.7
Maximum skewness among attributes of the numeric type.
0.21
Minimum standard deviation of attributes of the numeric type.
-1.16
First quartile of kurtosis among attributes of the numeric type.
0.26
Third quartile of standard deviation of attributes of the numeric type.
0.27
Maximum standard deviation of attributes of the numeric type.
5.96
Percentage of instances belonging to the least frequent class.
24
Number of instances belonging to the least frequent class.
0.35
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.77
Mean kurtosis among attributes of the numeric type.

5 tasks

122 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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