What is the level of accuracy can be achieve in predicting student performances ???

With the recent developments in the Internet allowed many leading educational institutions to offer online teaching and learning through Learning Management Systems. Systems with different capabilities and approaches have been developed to deliver online education which makes the communication channel between student and teacher into a much more virtual one.
 

The most important consideration would be to identify the student’s performance accurately and provide the necessary support to the students to improve their knowledge levels. But the main problem of measuring the students in accurately and to classify them correctly in to group to predict their performance levels is a huge challenge.

Behrouz Minaei-Bidgoli, Deborah A. Kashy, Gerd Kortemeyer, William F. Punch [2003] have researched on applying data mining methodologies to classify the students in to different groups and try to predict their performance achievement for the future.

For this research they used Quadratic Bayesian classifier, 1-nearest neighbor (1-NN), k-nearest neighbor (k-NN), Parzen-window, multilayer perceptron (MLP), and Decision Tree as the data mining mechanisms and by combining multiple classifiers they hoped to improve classifier performance. For the decision trees they have used C5.0, CART, QUEST, CRUISE algorithms.

In this paper they focused on using a Genetic Algorithm to optimize a combination of classifiers. They used GAToolBox for MATLAB to implement a Genetic Algorithm to optimize classification performance and to find a population of best weights for every feature vector which minimize the classification error rate.

Finally they concluded that using Genetic Algorithm more than a 10% performance improvement can be achieved and having the information generated the instructor would be able to identify students at risk early.

Reference : B. Minaei-Bidgoli, D. A. Kashy, G. Kortmeyer, and W. F. Punch, “Predicting student performance: an application of data mining methods with an educational web-based system,” in Frontiers in Education, 2003. FIE 2003 33rd Annual, 2003, vol. 1, p. T2A–13.

How we can enhance the performance of the students???

Web based online learning systems are widely used around the world to provide various types of educational programs. With the increase in the networking and the use of Internet resources enables these educational programs to be offered through web based learning management systems and several web based educational systems with various functionalities have been developed to deliver online education more effectively to the students.

 

Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F. Punch [2004] have tried to research on classifying the students in to different group and predict the final grade that they will achieve in the end of the course modules. This research is based on the web based learning management systems which is known as Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA) developed at Michigan State University.

For this research they concerned on the three kinds of large data sets available in the LON-CAPA which can be categorized as educational resources, information about users and the activity log databases which log actions taken by students.  

For this study they restricted four different classifiers using the LON-CAPA dataset which were Quadratic Bayesian classifier, 1-nearest neighbor (1-NN), k-nearest neighbor (k-NN), Parzen-window. The approach they used in this research is to combine the classifiers together to increase the performance of the classifiers and this is known as online classification fusion.

Another important aspect they highlighted in this research is the use of Genetic Algorithms as an optimization tool for resetting the parameters in other classifiers. The main idea was to use the Genetic Algorithms to find a population of best weights for every feature vector, which minimize the classification error rate.

As the conclusions they mentioned that a combination of multiple classifiers leads to a significant accuracy improvement in the given data sets. Weighing the features and using a genetic algorithm to minimize the error rate improves the prediction accuracy by at least 10% test cases as well. Finally they suggested that use of these algorithms as tools can be used to identify those students who are at risk in very large classes and it will help the instructors to provide appropriate advising in more effective manner.

Reference: B. Minaei-Bidgoli, G. Kortemeyer, and W. F. Punch, “Enhancing online learning performance: an application of data mining methods,” in The 7th IASTED International Conference on Computers and Advanced Technology in Education (CATE 2004), 2004, pp. 173–178.

Classification Approaches in Learning Analytics, Does it always give the better results ????

In educational data mining many researchers have tried many different approaches available in data mining context to predict the learning patterns of the students to achieve better and quality results. All these approaches are mainly focusing on getting the results according to a particular student domain which highlights various specific features indicated through the Learning Management Systems.

Virtual learning is growing enormously and the student population those connect with these Learning management systems are increasing by numbers every day. With the necessity of understanding of each student learning pattern teachers should have a better way of predicting the performance of their students. In response to this necessity different classification techniques can be used to compare and interpret the educational data and improve the modeling of students in to different categories.

In order to make the student modeling process much easier Diego Garcia Saiz and Marta Zorrilla [2011] have researched on applying different classification techniques on the student data to predict their performances. In their research they tried to implement a tool known as Elearning Web Miner (EIWM) to discovering how the students are behaving and progress in the courses which is very helpful for the tutors to identify the students who need more attention among from a larger set of students.

One of the main reason that applying learning analytics in educational data sources is challengeable because of the dataset becomes very small comparing to the other application we see around us. Even though the number of student information which contains in a database is huge, most of these are dynamic and contain many variations among them. Since for this research they found it difficult to collect required data which made them to use the data for past three academic years for average student enrollment of 70  per year for a specific course module. For all of these student instances they considered attributes with mean values such as total time spent, number of sessions carried out, number of sessions per week, average time spent per week and average time per session.   

With the intention of analyzing and choosing  best classification algorithms for educational datasets they  analyzed four of the most common machine learning techniques, namely Rule-based algorithms, Decision Trees, Bayesian classifiers and Instance-based learner classifiers which are mainly were OneR, J48, Naive Bayes, BayesNet TAN and NNge. 

They tested these five algorithms using different parameter settings and different numbers of folds for cross validation, in order to discover whether they have a great effect on the result. In the evaluation process they found that Bayes algorithms perform better in accuracy and is comparable to J48 algorithm although it is worse at predicting than Naive Bayes which is the best in this aspect. Due to the results they achieved they highlighted that OneR suffered from over-fitting in this dataset, so that it should be discarded as a suitable classifier for very small datasets.

They also observed that NNge improves its performance in this dataset although the great number of rules which it offers as output makes it less interpretative for instructors than the rest of the models. Finally they conclude that Bayes Networks are suitable for small datasets in performing better than the Naive Bayes when the sample is smaller. As consequence of the fact that BayesNet TAN model is more difficult to interpret for a non-expert users and J48 is similar in accuracy to it.

One significant result which I see in their research is that the pre-processing step which they followed. In the dataset they found that there are instances which can be considered as outliers in the statistical sense and they suggested a mechanism to remove or eliminate those outliers in the data set which can improve the results by 20%. This makes a huge advantage when the data set is larger in size and provide with better quality results for the users.

What I believe about this research is that even though they suggested these approaches in classify the students, it cannot be proved that the same algorithm is suit for every situation we have in the educational domains. Some algorithms can perform well with small datasets and some can perform well with larger data samples and some are providing more interpretable results and some are not. Therefore depending on the problem situation and the context we have to choose the best algorithm that can be used for the specific process so that we get more acceptable quality output as final results.

Reference: D. García-Saiz and M. Zorrilla, “Comparing classication methods for predicting distance students’ performance,” 2011.