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.

Predicting Student Performance in Distance Learning Systems

In any distance learning environment ability of predicting a student’s performance is very important which is advantageous for the teachers and tutors to identify the students with different capabilities and their capacities. When it comes to University education where many students are accessing or following their studies through open and distance environment it requires a identification process upon the students to measure whether they achieve the required level of performance. Otherwise due to the nature of the distance education some students can be lagging behind while peer students have passed them by miles. If teachers and tutors can recognize them at the early stage of the course module necessary steps or decisions can be made in order to prevent them from dropping out from the course modules.

S. Kotsiantis, C. Pierrakeas, and P. Pintelas [2003] have suggested an approach which has used machine learning algorithms with the LMS data to prevent, student dropouts in university distance education. They tried to investigate the efficiency of machine learning techniques in such an environment with trained data sets provided by the “informatics” course of the Hellenic Open University.

In their research they used five different algorithms to study student data and they found that these algorithms can be used more appropriately to predict the student dropouts in study programs. In this research they used most common machine learning techniques which are Decision Trees, Bayesian Nets, Perceptron-based Learning, Instance-Based Learning and Rule-learning.

In their data collection process they collected student data under two categories of attributes which are Demographic attributes and Performance attributes. The Demographic attributes were collected by concerning students’ sex, age, marital status, number of children and occupation and Performance attributes represents attributes which were collected from tutors’ records concerning students’ marks on the written assignments and their presence or absence in face-to-face meetings.

In the above mentioned algorithms categories they used C4.5 algorithm for representing the decision tree, Naive Bayes algorithm was the representative of the Bayesian networks, the RIPPER algorithm was the representative of the rule-learning techniques, WINNOW as the representative of perceptron-based algorithms and finally 3-NN or 3- Nearest Neighbor as the Instance-Based Learning algorithm.

In order to rank the representative algorithms they used the prediction accuracy criterion was used. In the evaluation of the algorithms they found that there was no statistically significant difference between algorithms, but it showed that the Naive Bayes algorithm and the RIPPER had the best accuracy than the others. Among the Naive Bayes algorithm and the RIPPER, Naive Bayes has the advantage short computational time requirement and importantly Naive Bayes classifier can use data with missing values as inputs, whereas RIPPER cannot work with which gives a indication that the Naive Bayes is the most appropriate learning algorithm to be used for the construction of a software support tool in Learning Management Systems.

Other than the above it was found that there exist some obvious and some less obvious attributes that demonstrate a strong correlation with student performance where some gives the higher importance in consideration. Also it can be argued that the learning algorithms could enable tutors to predict student performance with satisfying accuracy long before final examination. 

Reference: S. Kotsiantis, C. Pierrakeas, and P. Pintelas, “Efficiency of Machine Learning Techniques in Predicting Students’ Performance in Distance Learning Systems,” Citeseer, 2002.