The use of Genetic Algorithms and Decision Trees in Distance Education

In the research of Dimitris Kalles,Christos Pierrakeas [2006] they tried to used genetic algorithm and decision tree based classification on student data to understand the different learning capacities of the students. In their research they based the applicability of these algorithms on different sets of students under different course modules. 

In this research they mainly used the genetic algorithm based decision tree implementation of GATREE which is built using the GALIB library. The genetic operators on the tree representations are relatively straightforward where a mutation may modify the test attribute at a node or the class label at a leaf and a cross-over may substitute whole parts of a decision tree by parts of another decision tree.

For creating the dataset the students’ key demographic characteristics of students such as age, sex, residence and their marks in written assignments and their presence or absence in plenary meetings were considered to create the training dataset. 

In this research they used the GATREE system and experimented with to 150 generations and up to 150 members per generation. To ensure the validity of the experimentation they used the same data sets of the original experimentation which includes demographic data and quantized data.

They observed that GATREE induced trees provide good accuracy estimation, even without the cross-validation testing phase. Their initial findings suggested that when compared to conventional decision-tree classifiers this approach produces significantly more accurate trees.

However it was noted that GATREE has been generating closer estimations even with the quantized formats which gives an indication that GATREE can produce quality results even in the presence of noise.

Reference: D. Kalles and C. Pierrakeas, “Analyzing student performance in distance learning with genetic algorithms and decision trees,” Applied Artificial Intelligence, vol. 20, no. 8, pp. 655–674, 2006.