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.
Highlights
Due to the popularity of wireless devices and progress in mobile computing technology, mobile devices are becoming ubiquitous tools in everyone’s life
The present study focused on the outdoor education needs of a university forestry department and constructed a set of context learning–based individualized plant ecology m-learning systems termed as the context-aware plant ecology learning system (CAPELS)
Comparing the experimental group and control group questions and their difficulty levels revealed that the average difficulty levels of the learning efficiency questions were 0.5 and 0.54, the average degrees of distinctiveness were 0.51 and 0.54, Cronbach’s credibility values were .77 and .79, respectively; each set of questions complied with the credibility and reliability assessment
Summary
Due to the popularity of wireless devices and progress in mobile computing technology, mobile devices are becoming ubiquitous tools in everyone’s life. Based on the above-mentioned information, it can be deduced that this study would aim at learning about plant ecology and constructing a plant ecology m-learning with context-aware system This system can provide appropriate information about plant ecology with the help of sensors and, according to the situation factors at any given time, can be implemented in a wireless network environment. Environment, indicating the need for a high-precision and generalized classifier Due to this reason, in addition to the above-mentioned plant leaf characteristics, this study uses deep learning for identification. It can reach 97.25% plant recognition accuracy on the self-collected data set At present, these plant identification technologies generally use pictures in the plant database as test data; at the actual teaching site, the plant data set must be extremely diverse, and the images are collected in the natural. Lee and Hong (2013) used the leaf vein and shape identification algorithm for the selection of plant features, which primarily consists of the following five steps: 1. Grayscale conversion: The system automatically converts the obtained image into a grayscale image with 300 × 375 pixels
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