EDMiner (Education Data Miner)

7th International Conference on Information and Automation for Sustainability, 2014

The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.

Educational data mining (EDM) is an eme mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics such as student retention and attrition, personal recommender systems within education, and how data mining can be used to analyze course management system data. Gaps in the current literature and opportunities for further research are presented.

2010

There is growing perception in academic community that internet and web resources are dominantly used for non-academic and social purposes. There have been some studies to validate this perception. Major objective of our analysis was to investigate the relationship between usage patterns of web sites which do not contribute directly to curricular activities and their relationships with academic performances of students from gender groups This paper presents results of an analysis of log files of a technical institute's proxy server for a period of over one year. The contents of log files were pre-processed to hide the identities of users and to create relational database of fields of our interests. We have implemented algorithms for statistical analysis and validated our results using Rapid-miner. Our results confirm the hypothesis that the students dominantly use internet resources for activities which do not directly contribute to curricular program. However, a very significant percentage of this non-academic usage is directed to desirable extracurricular activities such as news, sports, media and online games etc. It is interesting to note that the female students dominantly use social networking sites where as male students dominantly use entertainment websites. A very small percentage (9%) of the total time is spent on socially unacceptable and undesirable websites which include drugs and adult websites.

3C Tecnología_Glosas de innovación aplicadas a la pyme

Predicting students' performance is a very important task in any educational system. Therefore, to predict the learner's behavior towards studies many data mining techniques are used like clustering, classification, regression. In this paper, new student's performance prediction model and new features are introduced that have a great influence on student's academic achievement i.e. student absence days in class and parents' involvement in the learning process. In this paper, considerable attention is on the punctuality of students and the effect of participation of parents in the learning process. This category of features is concerned with the learner's interaction with the e-learning management system. Three different classifiers such as Naive Bayes, Decision Tree, and Artificial Neural Network are used to examine the effect of these features on students' educational performance. The accuracy of the proposed model achieved up to 10% to 15% and is much improved as compared to the results when such features are removed.

Data

Educational Data Mining (EDM) is used to extract and discover interesting patterns from educational institution datasets using Machine Learning (ML) algorithms. There is much academic information related to students available. Therefore, it is helpful to apply data mining to extract factors affecting students’ academic performance. In this paper, a web-based system for predicting academic performance and identifying students at risk of failure through academic and demographic factors is developed. The ML model is developed to predict the total score of a course at the early stages. Several ML algorithms are applied, namely: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Linear Regression (LR). This model applies to the data of female students of the Computer Science Department at Imam Abdulrahman bin Faisal University (IAU). The dataset contains 842 instances for 168 students. Moreover, the results showed that the pr...

Proceedings of conference on advanced technology …, 2001

Web-based technology is often the technology of choice for distance education given the ease of use of the tools to browse the resources on the Web, the relative affordability of accessing the ubiquitous Web, and the simplicity of deploying and maintaining resources on the World-Wide Web. Many sophisticated web-based learning environments have been developed and are in use around the world. The same technology is being used for electronic commerce and has become extremely popular. However, while there are clever tools developed to understand online customer's behaviours in order to increase sales and profit, there is very little done to automatically discover access patterns to understand learners' behaviour on webbased distance learning. Educators, using on-line learning environments and tools, have very little support to evaluate learners' activities and discriminate between different learners' on-line behaviours. In this paper, we discuss some data mining and machine learning techniques that could be used to enhance web-based learning environments for the educator to better evaluate the leaning process, as well as for the learners to help them in their learning endeavour.

The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.

2010

Understanding the students' temporal and periodic Internet usage behaviors are essential for administrators, instructors and course coordinators for evaluating Internet usage behaviors related to those students' undertaken courses or programs. In this investigation we propose a new Web usage mining approach for automatic extraction and visualization of periodic and temporal based Internet usage behaviors of individual users. Our model is able to extract each individual user's changes on periodic behaviors during different periods of a semester.