Abstract
Normal 0 21 false false false TR JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Times New Roman; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} The purpose of this study is to develop a tool by which non-experts can carry out basic data mining analyses on logs they obtained via the Moodle learning management system. The study also includes findings obtained by applying the developed tool on a data set from a real course. The developed tool automatically extracts features regarding student interactions with the learning system by using their click-stream data, and analyzes these data by using the data mining libraries available in the R programming language. The tool has enabled users who do not have any expertise in data mining or programming to automatically carry out data mining analyses. The information generated by the tool will help researchers and educators alike in grouping students by their interaction levels, determining at-risk students, monitoring students' interaction levels, and identifying important features that impact students’ academic performances. The data processed by the tool can also be exported to be used in various other analyses.
Highlights
Data mining is used in numerous fields for the purpose of discovering hidden patterns and making predictions by utilizing the available data
Data stored in the databases of online learning environments constitute a significant portion of the data resources used in educational data mining studies (Peña-Ayala, 2014)
The purpose of this study is to develop a tool (MoodleMiner) by which users who are inexperienced in programming and data mining can carry out basic data mining analyses on log records they obtained via the Moodle learning management system
Summary
Data mining is used in numerous fields for the purpose of discovering hidden patterns and making predictions by utilizing the available data. There is an ever-increasing number of successful implementations in the field of education. The increasing amount of stored data on students learning processes and the tools used in analyses becoming more widely-utilized are among its most significant reasons. Data stored in the databases of online learning environments constitute a significant portion of the data resources used in educational data mining studies (Peña-Ayala, 2014). Generating learning-related features from these records that are kept in learning record stores or relational databases, and carrying out data mining analyses on this data is a time-consuming and challenging process for non-experts (Chatti, Dyckhoff, Schroeder, & Thüs, 2012; Chatti et al, 2014)
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