Can we prevent school dropout in Distance Learning???

Distance and open education is a emerging educational principle where many universities and institutions are using e-learning in distance education for their study programs. Due to the nature of virtual communication the teachers and students are not meeting each other face to face. Because of this learning nature many students are dropping out from the educational programs since they cannot cope with the requirement of the study programs. Therefore understanding the performance level of each student will help the teachers to identify different capability levels of the student which will help them to climb up the ladder with their peers. 

S. B. Kotsiantis, C. J. Pierrakeas, and P. E. Pintelas [2003] have tried to apply data mining methodologies on educational data to limit student dropout in university-level distance learning. According to them the dropout can be caused by professional, academic, health, family and personal reasons and varies depending on the education system adopted by the institution providing distance learning, as well as the selected subject of studies.

They based their research on a course module which was offered in Hellenic Open University which is based their educational programs mainly on distance mode. The built a data set of 365 student instances and based on the data the attributes were divided in to two groups which were the ‘Curriculum-based' group and the ‘Students' performance' group. The ‘Curriculum-based' group represented attributes of students' sex, age, marital status, number of children and occupation and the group represented attributes concerning students' marks on the first two written assignments and their presence or absence in the first two face-to-face meetings.  

In this research they used six machine learning techniques which are Decision Trees, Neural Networks, Naive Bayes algorithm, Instance-Based Learning Algorithms, Logistic Regression and Support Vector Machines. For each of these algorithms they used a representative algorithm as C4.5 algorithm for the decision trees algorithm and to estimate the values of the weights of a neural network the Back Propagation (BP) algorithm was used. The Naive Bayes (NB) algorithm was used for the Bayers algorithm and 3-Nearest Neighbour algorithm was also used. Maximum Likelihood Estimation (MLE) was the used statistical method for estimating the coefficients of the logistic model and finally, the Sequential Minimal Optimization (or SMO) algorithm was the representative of the Support Vector Machine.

Based on these six algorithms they found that Naive Bayes algorithm and Back Propagation (BP) algorithm had the best accuracy with the data sets. However they mentioned that the differences were generally small and because they were only based on one course module and it may possible that the ranking in another data set of the same domain is different. Also they concluded that Naive Bayes has the  short training time and effective communicated way of predicting and the small programming cost than the other algorithms.

Reference: S. Kotsiantis, C. Pierrakeas, and P. Pintelas, “Preventing student dropout in distance learning using machine learning techniques,” in Knowledge-Based Intelligent Information and Engineering Systems, 2003, pp. 267–274.

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