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

Muhammad Adnan,Asad Habib,Jawad Ashraf,Shafaq Mussadiq

doi:10.3906/elk-1811-196

Abstract

It is a knotty task to amicably identify the sporadically changing real-world context information of a learner during M-learning processes. Contextual information varies greatly during the learning process. Contextual information that 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 to solve real-world programming problems. It is important to guide learners based on their contextual information in order 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 contextual information. Learners? contextual information is extracted from their mobile devices and is processed by an unsupervised machine learning algorithm called density-based spatial clustering of applications with noise (DBSCAN) with a rule-based inference engine running on a back-end cloud. CSMLS is able to provide real-time, adaptive, and active learning support to students based on their contextual information characteristics. A total of 150 students evaluated the performance and acceptance of CSMLS for a complete academic semester, i.e. 6 months. Experimental results revealed the threefold success of CSMLS: extraction of students? context information, supporting them in appropriate decision-making, and subsequently increasing their computer programming skills.

Highlights

It has been recognized that the computer programming abilities of learners increase if they are encouraged to solve problems that they encounter in the real world [1]
The density-based spatial clustering of applications with noise (DBSCAN) algorithm together with a rule-based inference engine was administered on Firebase and Google Cloud Machine learning (ML) Engine to recommend appropriate programming exercises and content to learners on their smartphones
The context analysis layer used the DBSCAN algorithm to cluster the learners based on their geographical information

Summary

Introduction

It has been recognized that the computer programming abilities of learners increase if they are encouraged to solve problems that they encounter in the real world [1]. In order to provide suitable and tailored programming exercises, detailed learning context data must be first extracted from learners’ mobile devices. Keeping the above-mentioned facts in mind, we cannot use context data to recommend appropriate computer programming learning content on learners’ mobile devices in the same way that learning content is delivered while learning English, history, medicine, etc. Environmental context data are useful in framing real-world programming exercises for learners and can be delivered to them on their mobile devices at appropriate learning times. The Android client app was responsible for sending the learner’s location statistics to the Firebase database every time a learner entered or left a particular Google maps geofenced zone. The latitude, longitude, and radius defined the geofence for four different monitored locations. The following Android code snippet shows how geofencing was defined

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Conclusion