Learning Analytics Approach with Sakai

As I have discussed in my previous posts the Sakai is an Open Source Learning Management System which is been widely used in academic context for their study programs. Lauria and Joshua [2011] have tried to implement a predictive model within the Sakai for predicting the performance of the students and to take the decisions for making corrective actions. In their research they came up with a methodology which contains six phase on the knowledge discovery process.

 

When collecting the required data they extracted information from diverse sources and followed several pre-processing steps to handle the missing value, outliers and incomplete records. All the data which were logged through Sakai were aggregated to produce consolidated records per course and student. In order to remove the variations in different course contents all the data were collected as ratio values rather than an absolute value.

 

After the data collection process they followed some steps to reduce the dimensionalities on the available data. In order to maintain a proper level of query accuracy and efficiency the number of variables and parameters requiring for the estimation were selected properly and unnecessary features were removed.

 

After the necessary data was selected the transformation and rescaling phase was carried out to make sure that all the attribute data were formatted according to the requirement of the data mining algorithms they used. After the data was converted or transformed the partitioning step was used to divide the data in to several groups. They carried out this partitioning process on the data set to make sure that required amount of data is available for the training of the data model and for the validation with testing step. 

 

For building the data models four different types of data mining approaches were selected. Logistic regression, C5.0 decision tree, support vector machine and Bayesian networks were used for creating the train models with the data set. After the models were created they were validated using the validation data set. For validating the data models they measure the prediction accuracy on the data to verify that the required level of accuracy or the quality can be achieved by the models.

 

Reference :

Eitel J.M. Lauría, Joshua Baron, Mining Sakai to Measure Student Performance: Opportunities and Challenges in Academic Analytics, 2011