Recommendation System for Engineering Students’ Specialization Selection Using Predictive Modeling

Educational data mining (EDM) can be used in extracting useful patterns in students’ academic records which may aid in management decision making on determination of engineering students’ specialization track once the general engineering academic requirements were completed. The objective of the research is to provide a specialization selection recommendation for engineering students through application of data mining algorithm and adoption of the rule sets generated by a predictive model. The attributes that may be significant in creating prediction were determined using correlation-based feature selection. Comparative analysis among known algorithms shows that the highest accuracy was considered. A decision tree classification model using WEKA and J48 produced an accuracy value of 80.06. The study revealed that Gender, Algebra, Calculus and physics courses found to have significant effect in predicting the engineering specialization, thus strengthening the general notion that for engineering students to be more successful, the academic performance with the above courses should be highly considered.