Data Mining in E-Learning

Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.

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How e-Learning Experience Enhances the Social Presence in Community of Practice

Virtual Community Building and the Information Society ◽

10.4018/978-1-60960-869-9.ch005 ◽

2012 ◽

pp. 75-120 ◽

Cited By ~ 4

Author(s):

Constanta-Nicoleta Bodea ◽

Radu Mogos ◽

Maria-Iuliana Dascalu

Keyword(s):

Data Mining ◽

Project Management ◽

Community Of Practice ◽

Social Presence ◽

Learning Experience ◽

Main Research ◽

The Social ◽

Analytical Perspective ◽

Management Area ◽

E Learning

The chapter presents a study made in order to find out how the e-learning experience enhances the social presence in the community of practice. The study was carried out for the online master degree programme in project management, delivered by the Academy of Economic Studies, Bucharest. The main research method was a survey and the research instrument was a questionnaire. Statistics and data mining were applied. Statistics was applied to check hypothesis and quantify the correlation significance. Due to the large number of the variables and the indirect relationships, the analysis paths become very complex and it would be extremely difficult to manage the analysis workflow. So, the data mining approach was chosen. As a theoretical framework and analytical perspective for this research, Wenger’s theories of learning in Community of practice (CoP), and the social presence model of Garisson et al., are applied. The study revealed that the characteristics of the online social presence in learning environments enhanced the students’ interest for CoPs. Another finding of this study is that for project management area there is not a significant correlation between the learning domain and that of the CoPs chosen to get involved. The reason is that most of the project personnel hold a first degree in an area other than project management.

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Institutional Research Using Data Mining

Cases on Institutional Research Systems ◽

10.4018/978-1-60960-857-6.ch005 ◽

2011 ◽

pp. 66-102 ◽

Cited By ~ 1

Author(s):

Constanta-Nicoleta Bodea ◽

Vasile Bodea ◽

Radu Mogos

Keyword(s):

Data Mining ◽

Academic Performance ◽

Virtual Learning Environment ◽

Student Activities ◽

Participatory Behavior ◽

Student Profiles ◽

Clustering And Classification ◽

E Learning ◽

Using Data ◽

And Behavior

The aim of this chapter is to explore the application of data mining for analyzing academic performance in connection with the participatory behavior of the students enrolled in an online two-year Master degree program in project management. The main data sources were the operational database with the students’ records and the log files and statistics provided by the e-learning platform. One hundred eighty-one enrolled students, and more than 150 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining was chosen, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to identify the factors explaining the students’ performance and the relationship between academic performance and behavior in the virtual learning environment. Data mining has revealed interesting patterns in data. These patterns indicate that academic performance is related to the intensity of the student activities in virtual environment. If the student understands how to work and she/he is motivated to communicate with others, then he might have a good academic performance. Based on clustering analysis, different student profiles were discovered, explaining the academic performance. The results are very encouraging and suggest several future developments.

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Observation of Success Status of Employees in E-Learning Courses in Organizations with Data Mining

International Journal of E-Adoption ◽

10.4018/ijea.2017010104 ◽

2017 ◽

Vol 9 (1) ◽

pp. 38-49

Author(s):

Fatma Önay Koçoğlu ◽

İlkim Ecem Emre ◽

Çiğdem Selçukcan Erol

Keyword(s):

Data Mining ◽

Evaluation Criteria ◽

Data Set ◽

Completion Status ◽

Completion Date ◽

E Learning ◽

Pharmaceutical Industries ◽

Mining Methods ◽

Using Data ◽

Performance Results

The aim of this study is to analyze success in e-learning with data mining methods and find out potential patterns. In this context, 374.073 data of 2013-14 period taken from an institution serving in e-learning field in Turkey are used. Data set, which is collected from information technology, banking and pharmaceutical industries, includes success and industry of employees', trainings which they complete, whether the trainings are completed, first login and last logout dates, training completion date and duration of experience in training. Using this data set, success status of participants is observed by using data mining methods (C5.0, Random Forest and Gini). By observing using accuracy, error rate, specificity and f- score from performance evaluation criteria, C5.0 has chosen the algorithm which gives the best performance results. According to the results of the study, it has been determined that the sectors of the employees are not important, on the contrary the ones that are important are the completion status, the duration of experience and training.

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