Cloud-supported machine learning system for context-aware adaptive M-learning

Abstract

It is a knotty task to amicably identify the sporadically changing real-world context information of a learnerduring M-learning processes. Contextual information varies greatly during the learning process. Contextual informationthat affects the learner during a learning process includes background knowledge, learning time, learning location,and environmental situation. The computer programming skills of learners improve rapidly if they are encouraged tosolve real-world programming problems. It is important to guide learners based on their contextual information inorder to maximize their learning performance. In this paper, we proposed a cloud-supported machine learning system(CSMLS), which assists learners in learning practical and applied computer programming based on their contextualinformation. Learners’ contextual information is extracted from their mobile devices and is processed by an unsupervisedmachine learning algorithm called density-based spatial clustering of applications with noise (DBSCAN) with a rule-basedinference engine running on a back-end cloud. CSMLS is able to provide real-time, adaptive, and active learning supportto students based on their contextual information characteristics. A total of 150 students evaluated the performanceand acceptance of CSMLS for a complete academic semester, i.e. 6 months. Experimental results revealed the threefoldsuccess of CSMLS: extraction of students’ context information, supporting them in appropriate decision-making, andsubsequently increasing their computer programming skills.