Classification of the Usage of Wikipedia as a Tool of Teaching in Higher Education With Decision Tree Model

Classification of the Usage of Wikipedia as a Tool of Teaching in Higher Education With Decision Tree Model

Cengiz Gazeloğlu (Suleyman Demirel University, Turkey), Zeynep Hande Toyganözü (Suleyman Demirel University, Turkey), Cüneyt Toyganözü (Suleyman Demirel University, Turkey) and Murat Kemal Keleş (Applied Sciences University of Isparta, Turkey)
Copyright: © 2019 |Pages: 20
DOI: 10.4018/978-1-5225-8238-0.ch003

Abstract

Wikipedia is a source that has been used at many universities around the world for students to gain some skills and be motivated positively. In higher education, some academicians have a positive view on the teaching usefulness of Wikipedia, and some of them are determined to use classical teaching. In this chapter, teaching use of Wikipedia in all faculty members of the Universitat Oberta de Catalunya are used as data. Then an entropy-based decision tree algorithm was developed. Wikipedia users and non-users are classified according to some aspects with this decision tree. Thus, it can be understood that whether Wikipedia has been used as a teaching tool by academicians or not. So, researchers can have information about the usefulness of Wikipedia in teaching and the intentions in use of it by academicians.
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Background

Data mining recognized as a sub-process of Knowledge Discovery in Databases (KDD) is a process to extract information from big data. It is an interdisciplinary field of finding correlations that enable us to make predictions about the future from large data sets to provide us information bringing algorithms and methods together from different areas such as artificial intelligence, machine learning, statistics, mathematics, and database systems.

Figure 1.

The data mining process

Source: (Bigus, 1996)

The main goal of the data mining is to transform the extracted information into a comprehensible structure for further use.

Data mining functionalities are used to specify the kinds of patterns or knowledge to be found in data mining tasks. The functionalities include characterization and discrimination; the mining of frequent patterns, associations, and correlations; classification and regression; cluster analysis; and outlier detection (Han and Kamber, 2012).

Table 1 includes some of the historical developments of data mining and, in brief, the processes that the data mining has gone through from the 1960s to the present.

Key Terms in this Chapter

C4.5: A well-known decision tree algorithm to select the best attribute for classification.

Weka: A software program which includes most of the widely used data mining algorithms and methods.

Entropy: A measurement of uncertainty or randomness in a data set.

Decision Tree Model: A convenient method for classification and estimation problem.

Wikipedia: Is one of the world's most visited and used online encyclopedias.

OpenCourseWare: (OCW): Course lessons created at universities and published for free via the internet.

Data Mining: A process to extract information from big data.

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