Difference between revisions of "Motivating Factors and Potential Deterrents of Using Wikipedia in Teaching in Higher Education"
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| + | | title = Motivating Factors and Potential Deterrents of Using Wikipedia in Teaching in Higher Education | ||
| + | | date = 2018 | ||
| + | | authors = [[Sharefah A. Al-Ghamdi]]<br />[[Sumayah Al-Rabiaah]] | ||
| + | | doi = 10.1109/cais.2018.8441946 | ||
| + | | link = http://xplorestaging.ieee.org/ielx7/8410645/8441936/08441946.pdf?arnumber=8441946 | ||
| + | }} | ||
'''Motivating Factors and Potential Deterrents of Using Wikipedia in Teaching in Higher Education''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Sharefah A. Al-Ghamdi]] and [[Sumayah Al-Rabiaah]]. | '''Motivating Factors and Potential Deterrents of Using Wikipedia in Teaching in Higher Education''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Sharefah A. Al-Ghamdi]] and [[Sumayah Al-Rabiaah]]. | ||
== Overview == | == Overview == | ||
Data mining techniques have been shown its success in analyzing data to assist factors and make decisions in many different applications. In this work, authors analyzed universities' faculty members attitudes toward using [[Wikipedia]] in teaching in higher education. This may help in developing appropriate solutions to improve Wikipedia's effectiveness in education and provide the developers of Wikipedia with factors that may negatively impact using Wikipedia in teaching in higher education. Three different classification algorithms (Jrip Rule, Decision tree (J48) and Naive Bayes) have been carried out in the free and [[open source]] software WEKA (Waikato Environment for Knowledge Analysis) on used dataset. The accuracy and Receiver Operating Characteristic curves for all classifiers discussed. Authors results show that there are some factors influences the faculty using of Wikipedia in activity teaching which are unobserved in other studies. | Data mining techniques have been shown its success in analyzing data to assist factors and make decisions in many different applications. In this work, authors analyzed universities' faculty members attitudes toward using [[Wikipedia]] in teaching in higher education. This may help in developing appropriate solutions to improve Wikipedia's effectiveness in education and provide the developers of Wikipedia with factors that may negatively impact using Wikipedia in teaching in higher education. Three different classification algorithms (Jrip Rule, Decision tree (J48) and Naive Bayes) have been carried out in the free and [[open source]] software WEKA (Waikato Environment for Knowledge Analysis) on used dataset. The accuracy and Receiver Operating Characteristic curves for all classifiers discussed. Authors results show that there are some factors influences the faculty using of Wikipedia in activity teaching which are unobserved in other studies. | ||
Revision as of 08:26, 25 January 2021
Authors | Sharefah A. Al-Ghamdi Sumayah Al-Rabiaah |
|---|---|
Publication date | 2018 |
DOI | 10.1109/cais.2018.8441946 |
Links | Original |
Motivating Factors and Potential Deterrents of Using Wikipedia in Teaching in Higher Education - scientific work related to Wikipedia quality published in 2018, written by Sharefah A. Al-Ghamdi and Sumayah Al-Rabiaah.
Overview
Data mining techniques have been shown its success in analyzing data to assist factors and make decisions in many different applications. In this work, authors analyzed universities' faculty members attitudes toward using Wikipedia in teaching in higher education. This may help in developing appropriate solutions to improve Wikipedia's effectiveness in education and provide the developers of Wikipedia with factors that may negatively impact using Wikipedia in teaching in higher education. Three different classification algorithms (Jrip Rule, Decision tree (J48) and Naive Bayes) have been carried out in the free and open source software WEKA (Waikato Environment for Knowledge Analysis) on used dataset. The accuracy and Receiver Operating Characteristic curves for all classifiers discussed. Authors results show that there are some factors influences the faculty using of Wikipedia in activity teaching which are unobserved in other studies.
