A Cloud-based Ubiquitous Learning System Using Flow Learning Model – A Case Study of the Instruction on Nature and Life Sciences of Elementary School

Abstract

In recent years, due to the rapid development of information technology and internet technology, and the use of cloud technology has become increasingly popular, learning through internet has turned into one of the most convenient and flexible channels for knowledge search and knowledge learning. Therefore, a cloud-based plant structure recognition ubiquitous learning system is designed in this research to implement the study on the adaptive learning model, and to discover the differences of the learning performances on the campus plants recognition between the learning through the proposed learning system and the learning through traditional outdoor learning, and furthermore to realize the satisfaction of the students on using the proposed learning system. In this study, there were 60 students of two classes (30 students each) randomly selected from four fifth grade classes of an elementary school in Nantou County of Taiwan participated in the experiment. The students were evenly divided into experimental and control groups with 30 students in each group. Before the experimental instruction, a pre-test on understanding of campus plants were administered to the students of both experimental and control groups, and the outcomes of the pre-test were analyzed. The learning status information of individual student was stored in the learning portfolio database. Moreover, after the students log into the cloud-based adaptive ubiquitous learning system, when the student move to the front of the specific plant and using the RFID reader to scan the RFID tag, the handheld device will access the back-end system through wireless internet, and deliver the adaptive learning contents to the individual student according to their learning status obtained in the pre-test. After the experimental instruction, a post-test was administered to evaluate the learning performances of the students, and recommendations on the learning deficiencies were provided to individual student. Meanwhile, the outcomes of the post-test will be stored into the database as the reference for future adaptive instructions. The outcomes of the post-test reveal that the proposed cloud-based adaptive ubiquitous learning system can significantly improve the learning effectiveness of the students.