Data

user-knowledge

active
ARFF
Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani

0 likes downloaded by 6 people , 6 total downloads 0 issues 0 downvotes

0 likes downloaded by 6 people , 6 total downloads 0 issues 0 downvotes

Issue | #Downvotes for this reason | By |
---|

Loading wiki

Help us complete this description
Edit

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.

Class (target) | nominal | 5 unique values 0 missing | |

V1 | numeric | 112 unique values 0 missing | |

V2 | numeric | 103 unique values 0 missing | |

V3 | numeric | 94 unique values 0 missing | |

V4 | numeric | 93 unique values 0 missing | |

V5 | numeric | 89 unique values 0 missing |

0.25

Second quartile (Median) of standard deviation of attributes of the numeric type.

-0.27

Third quartile of kurtosis among attributes of the numeric type.

Minimal mutual information between the nominal attributes and the target attribute.

Maximum mutual information between the nominal attributes and the target attribute.

5

The minimal number of distinct values among attributes of the nominal type.

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.

0.66

Third quartile of skewness among 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

Standard deviation of the number of distinct values among attributes of the nominal type.

First quartile of mutual information between the nominal attributes and the target attribute.

0.11

First quartile of skewness among attributes of the numeric type.

0.21

First quartile of standard deviation of attributes of the numeric type.

Average mutual information between the nominal attributes and the target attribute.

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.

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.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.41

Second quartile (Median) of skewness among attributes of the numeric type.