Implementation a Context-Aware Plant Ecology Mobile Learning System

Abstract

Mobile devices are becoming ubiquitous methodologies and tools, providing application for learning and teaching field. On the basis of the widespread use of wireless devices and mobile computing technology, this study proposes a context-aware plant ecology learning system (CAPELS) based on context-aware technology; adapting deep neural networks (DNN) and leaf vein and shape identification algorithm which can identify plant leaves, this system automatically provides relevant botanical and growth environment knowledge to the learners. Therefore, during outdoor education, it can assist learners in accurately obtaining the required relevant botanical and growth environment knowledge. The experimental results indicate that students who used CAPELS performed better learning about plant ecology than those who did not. We also delivered questionnaires to those who used CAPELS and analyzed the results by using the partial least squares (PLS) method. The results have shown that CAPELS can encourage student’s learning motivation and thus improve their learning effectiveness. Thus, CAPELS provides a new educational platform for promoting ecology learning approach and effectively improves student learning efficiency and motivation.

Design of a Novel Edge-Centric Cloud Architecture for m-Learning Performance Effectiveness by Leveraging Distributed Computing Paradigms’ Potentials

“…This research explored emerging computing paradigm characteristics for improving the effectiveness of mlearning performance. Therefore, this study was designed in three folds (i) an investigative approach (Almaiah & Al-Khasawneh, 2020) was adopted to determine the potential features of emerging computing paradigms for m-learning effectiveness, (ii) a novel edge-centric cloudlayered architecture was designed to leverage distributed computing paradigms' resources for improving mlearning performance effectiveness, and (iii) a real-time use case (learning assignment) (C. C. Wang et al, 2020) was executed using simulation tools CloudSim and EdgeCloudSim. Additionally, a SWOT analysis was performed to evaluate the proposed architecture's effectiveness and analyze the m-learning execution performance across other emerging architectures (Baccari et al, 2017).…”

“…Besides, it is a fully responsive learning system with effective course delivery tools and supports learners in improving their learning effectiveness. Furthermore, there is another mobile intelligent (e.g., context-aware) tutoring system (ITS) based on the mobile cloud framework that offers a knowledge environment to the learners (C. C. Wang et al, 2020).…”

“…Significantly, ECPs shape new m-learning architectures, enhance user communication efficiency during data processing, and address quality control (Parlakkılıc x, 2019). Architectures developed using DCPs support robust performance, context-aware (C. C. Wang et al, 2020) processing, and low latency (Elazhary, 2019). Indeed, the integration of ECPs with m-learning gives phenomenal momentum (Raman, 2019) with tremendous benefits to m-learning actors (Parlakkılıc x, 2019;Pinjari et al, 2018).…”

“…Besides, it is a fully responsive learning system with effective course delivery tools and supports learners in improving their learning effectiveness. Furthermore, there is another mobile intelligent (e.g., context-aware) tutoring system (ITS) based on the mobile cloud framework that offers a knowledge environment to the learners (C. C. Wang et al, 2020). In addition, such LMSs provide learning skills videos, enable students to record their learning progress, and are helpful for teachers in generating student performance reports (Elazhary, 2019).…”

“…It offers an environment for executing m-learning applications. It is more effective for resource-demanding applications: context-aware (C. C. Wang et al, 2020) and delay-sensitive applications.…”