Tendency of Educational Data Mining in Digital Learning Platform

With the advancement of technology learning process have been more reachable and interactive like never before...

hint-driven or failure-driven and find common misconceptions that students possess, to identify learner's motivation to lower drop-out rates, to predict/classify students when using intelligent tutoring systems are some of the educational objectives to use classification [5]. Clustering is one of the data mining techniques. It can be used to discover the new categories, which share the similar interest.
In clustering the similar instances are grouped together. Among the different available clustering methods, K-Means algorithm is generally used to divide data into natural groups based on their behavior for a larger dataset. In the K-Means clustering method, the number of clusters, denoted by K is needed to be predefined to apply the technique. The biggest problem in K-Means is the finalizing of the optimum number of clusters. WEKA and R were used for this study due its availability to download as open source software and compatibleness with CSV files [6].
The objective of clustering is to find high-quality clusters such that the Inter-cluster distances are maximized and the Intra-cluster distances are minimized. The clustering method applied in the paper "Mining Educational Data to Improve Students' Performance: A Case Study" is the k-means; the objective of this k-means test is to choose the best cluster center to be the centroid. The k-means algorithm requires the change of nominal attributes into numerical [7]. The conference paper "Examining students' online interaction in a live video streaming environment using data mining and text mining" which uses E-learning as the platform of research with the objective of mining the student online assessment data used classification, clustering, and association rule analysis as the data mining task [8]. Hung and Zhang [9] have done a study on undergraduate students using the decision tree technique to propose a predictive model of user performance and reveal the students online learning behavioral patterns. They have indicated that the majority of the students were passive learners and only tend to access e-materials, but did not seek any peer collaborations. However, the few active learners showed a high performance level.
They have proposed a decision tree for predicting performance.
According to Ratnapala et al. [6], the aim of their research was to use EDM techniques to conduct a qualitative analysis of students' interaction with E-learning system. The total of 412 students (enrolled in instructor-led non-graded and graded courses) was being researched on access behavior in an E-learning environment.
K-means clustering method divided the student population into five access groups based on their course access behavior. This research concluded that the difference in the learning environments could change the online access behavior of a student group. 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.

Research Problem
There are various reasons to choose data mining. The major reason is that data mining involves the use of data analysis tools that helps to discover previously unknown, patterns and relationships in large data sets. In this research, the focus is on Educational data.
Both researchers have academic backgrounds, and they aimed to find the best way to show that Educational Data Mining is important and helpful for educational Institutions in decision making.
The researchers sought to answer the following questions: 1.
What kind of data should we be working on?

2.
How educational data are collected from students (whether from use of interactive learning environments, computer-supported collaborative learning, or administrative data from schools and universities)? 3.
How useful data mining is in the educational analysis?

4.
What data mining tools and techniques are best to be used?

Findings from Literature Review
With introduction to different online learning platforms it has been more accessible for students to learn at desired way. To improve the learning environment and to cope with the desired pace of learning of the students it is important to analyze the data collected from those E-learning environments. The data mining of these data help in finding the patterns of online learning behavior and promotes the decision making in introducing innovative and more interactive way of E-learning to students. It also helps to find

Conclusion
Hence, we conclude that the educational data mining in the online learning environment have been following some similar manner only differing in the data set which raises the trustworthiness of the result of the studies. Therefore it's high time to discover different other technique in the EDM. One of the best ways is to use more than one technique together to contrast and compensate the pros and cons of the techniques used.

Future Scope and Recommendation
Here some of the suggestions to be considered for making results of the research more trustworthy: a) For the accurate outcomes the data sets are to be as large as possible. Similarly, the process of collecting data needs to be reorganized to show in data the true and sensible picture of the real world system.
b) The researches show that only one technique was used by isolating from other techniques. There is a high demand to explore hybrid techniques or alternatives for conventional algorithms that can better perform the data mining.
c) The data mining tool has to be integrated into the online learning environment as another author tool so that the data analysis goes on side by side in a single application. Feedback and results obtained with data mining can be directly applied to the e-learning environment.