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

Using K-Nearest Neighbor Algorithm on Student Data

The k-nearest neighbor data mining method is a classical prediction method among the machine learning techniques available in data mining. It has been widely used due to its simplicity and adaptability in predicting many different types of data. The main advantage of using KNN in prediction processes is that the KNN is a lazy method which does not require a model to represent the statistics and distribution of the original training data rather it can be apply on the actual instances of the training data. Even though the KNN is a simple predictive algorithm which we can rely on and it does not make any assumption about the prior probabilities of the training dat. Also the KNN is satisfactorily used on the situations when the data set is included with noisy and incomplete data.

Due to the advantages and the simplicity of the algorithm T. Tanner and H. Toivonen [2010] has tried to implement a model using K nearest neighbor data mining algorithm to identify the student who are at high risk of failing in a specific course. By this research they suggested that good results in predicting final scores indicate that students with learning problems can be found reliably. What they have been using on the student data is to make prediction on the performance of a given student based on the similarity to all instances in the training set and find the k most similar objects in the data set. This similarity is calculated by using a Euclidean distance between the features of the test subject and the corresponding features of each instant in the training set.

In their research they showed that KNN can produce predictions accurately for the final scores even after the first lesson. Another interesting result they found is that in any skill based courses early tests on skills can be used as the predictors for the final scores and they suggested that predicting final scores for the courses can be used to identify the students with learning problems and can be used directly to implement as a early warning features for the teachers so that the students can be altered if they are likely to fail the final tests. Based on the information or features they used for the experiment with the KNN algorithm they suggest that KNN method could be just as effective in other learning management systems (LMSs) such as Moodle where only a single lesson score is available for student assessment and especially other skill-based courses could be a good fit for the KNN method.

Reference : T. Tanner and H. Toivonen, “Predicting and preventing student failure–using the k-nearest neighbour method to predict student performance in an online course environment,” International Journal of Learning Technology, vol. 5, no. 4, pp. 356–377, 2010.

How we can implement Learning Analytics???

Academic analytics or Learning analytics is a wide term used recently to describe the use of data mining in educational data sources. Researches have used various data mining methodologies in different ways to understand or identify the learning models and learning patterns of the students. Various learning management systems can be used for providing the study programs for the students those who can be connected in open distance mode or either in a much more hybrid manner. But applying the same teaching principle on all the students in the same way will not cope with the ultimate goal of any learning management systems available which is to help the students to learn rather helping them to pass.

By understanding the way these students are learning it will help the teachers and the academics to change the teaching methodologies they used to cope with the requirements of the students. In order to identify how these students are behaving performance models can be used. It can be used as a monitoring tool to take necessary actions for the issues related to student learning. As the academic analytics suggest the knowledge can be created about the students by applying the statistical analysis and predictive modeling on educational data sources.

For building the predictive model we can use the continue streams of data which is created within the learning management systems with the data mining techniques and it can be used as a decision making tool for teachers. Before building the predictive model it is a must to acquire proper information out from the student data since not all information available in the data sources is important for the analyzing. The outcomes of these prediction models will be to provide guidelines or forecasting about the event that can be occurring based on the observations or the scenarios.

In any data mining research some steps can be given as the basic steps that we need to follow in the knowledge discovery process. The same principles can also be applied in the learning analytics processes where the data collecting and data pre-processing steps will be continued in the same manner. According to the requirements in the data mining methodology data reductions or data cleaning steps can be done. After the prediction model is been implemented using data mining approaches it needs to verify that the predictive accuracy is acceptable on the given student data. The main advantage which I see in the given context is that most of the data which are available in the data sources are labeled and they contains information about the student characteristics as well as the course management events where the modeling process can be applied in many ways.