User Knowledge Modeling
Donated on 6/25/2013
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.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification, Clustering
Feature Type
Integer
# Instances
403
# Features
5
Dataset Information
Additional 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.
Has Missing Values?
No
Introductory Paper
By H. Kahraman, Ş. Sağiroğlu, I. Colak. 2013
Published in Knowledge-Based Systems
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| STG | Feature | Continuous | no | ||
| SCG | Feature | Continuous | no | ||
| STR | Feature | Continuous | no | ||
| LPR | Feature | Continuous | no | ||
| PEG | Feature | Continuous | no | ||
| UNS | Target | Categorical | no |
0 to 6 of 6
Additional Variable 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)
Class Labels
Very Low: 50 Low:129 Middle: 122 High 130
Dataset Files
| File | Size |
|---|---|
| Data_User_Modeling_Dataset_Hamdi Tolga KAHRAMAN.xls | 56.5 KB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset user_knowledge_modeling = fetch_ucirepo(id=257) # data (as pandas dataframes) X = user_knowledge_modeling.data.features y = user_knowledge_modeling.data.targets # metadata print(user_knowledge_modeling.metadata) # variable information print(user_knowledge_modeling.variables)
Kahraman, H., Colak, I., & Sagiroglu, S. (2009). User Knowledge Modeling [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5231X.
Creators
Hamdi Kahraman
Ilhami Colak
Seref Sagiroglu
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.