Learning management system with prediction model and course-content recommendation module

Abstract

Aim/Purpose This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background It utilized five-year historical data to find patterns for predicting student perfor-mance in Java Programming to generate appropriate course-content recommen-dations for the students based on their predicted performance. Methodology The author used two models for the system development: these are the Fayyad knowledge discovery in databases (KDD) process model for the data mining phase and the evolutionary prototyping for system development. WEKKA and SPSS were used to find meaningful patterns in the historical data, while Ruby on Rails platform was used to develop the software. Contribution The contribution of this study is the development of an LMS architecture that can be used to augment the capabilities of the existing systems by integrating a data mining technique for modelling the leaners profile; developing of an algo-rithm for generating predictions; and making the most appropriate recommenda-tions for the learners based on prior knowledge and learning styles. Findings The result shows that J48 was the best data mining algorithm to be implemented for finding patterns in the data sets used in this study. Attributes such as age, gen-der, class schedule, and grades in other programming subjects were found rele-vant in predicting student performance in Java. Recommendations for Practitioners It is recommended that collaboration between the academe and IT industry be strengthened to develop a more advanced LMS which could enhance classroom teaching and improve the learning process. Recommendation for Researchers Combination of multiple algorithm in classifying data set is recommended to fur-ther improve the algorithm and rule sets of prediction. Inclusion of intrinsic at-tributes as part of data set aside from personal and academic records is also rec-ommended. LMS with Prediction Model and Course-content Recommendation 438 Impact on Society This LMS can be used to produce independent learners. Future Research Study about the impact of implementing this LMS in classroom environment will be conducted on the second phase.