Identification of learning attitude by grouping using K-Prototypes clustering for Learner motivational activities in E-Learning–an Implementation

E-Learning platforms became very common and popular nowadays. It also so frequent that some of the learners enrolled may not complete the course within the specified time. There comes a necessity to increase course completion rate where the E-learning environment should be improved to boost the learning attitude of the learners by monitoring the learner’s attitude. The learners should be grouped based on similarity of their learning characteristics. A clustering and ranking system is created as a REST API (Representational State Transfer Application Programming Interface) service. This is integrated to an E-Learning solution. The service can take the preprocessed data of learner activity information as input and, next by using K-Prototypes clustering algorithm group or cluster all learners by their learning attitude based on the features, then give the category of learner attitude as response. Each data object is also ranked according to the closeness to the centroid within each cluster. The response generated by the service is taken by the E-Learning system, can now not only can identify the group of learners and but also each individual learner by their learning attitude thus involve in motivational activity. A scheduler is implemented to request the clustering and ranking API with data from E-Learning system at designated times. Using it the learner status at that instant can be obtained by the E-Learning system.