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Abstract
Ubiquitous learning (u-learning) refers to anytime and anywhere learning. U-learning has progressed to be considered a conventional teaching and learning approach in schools and is adopted to continue with the school curriculum when learners cannot attend schools for face-to-face lessons. Computer Science, namely the field of Artificial Intelligence (AI) presents tools and techniques to support the growth of u-learning and provide recommendations and insights to academic practitioners and AI researchers. Aim: The aim of this study was to conduct a meta-analysis of Artificial Intelligence works in ubiquitous learning environments and technologies to present state from the plethora of research. Method: The mining of related articles was devised according to the technique of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The complement of included research articles was sourced from the broadly used databases, namely, Science Direct, Springer Link, Semantic Scholar, Academia, and IEEE. Results: A total of 16 scientific research publications were shortlisted for this study from 330 articles identified through database searching. Using random-effects model, the estimated pooled estimate of artificial intelligence works in ubiquitous learning environments and technologies reported was 10% (95% CI: 3%, 22%; I2 = 99.46%, P = 0.00) which indicates the presence of considerable heterogeneity. Conclusion: It can be concluded based on the experimental results from the sub group analysis that machine learning studies [18% (95% CI: 11%, 25%), I2 = 99.83%] was considerably more heterogeneous (I2 = 99.83%) than intelligent decision support systems, intelligent systems and educational data mining. However, this does not mean that intelligent decision support systems, intelligent systems and educational data mining is not efficient.
Highlights
Ubiquitous learning (u-learning) is a shift from the elearning paradigm which describes an environment that permits the use of ubiquitous computing devices to access teaching and learning contents by means of wireless networks at any time and in any location
Using random-effects model [28], the estimated pooled estimate of artificial intelligence works in ubiquitous learning environments and technologies reported by the 16 studies was 10% which indicates the presence of heterogeneity
This study analysed different artificial intelligence works in ubiquitous learning environments and technologies based on the four major scientific approaches – intelligent decision support systems, intelligent systems, machine learning and educational data mining
Summary
Ubiquitous learning (u-learning) is a shift from the elearning paradigm which describes an environment that permits the use of ubiquitous computing devices to access teaching and learning contents by means of wireless networks at any time and in any location. U-learning is characterized by accessibility where the information is readily available whenever learners need its utilization. In u-learning the information remains on the platform and is always available to learners. There is immediacy where the information can be acquired instantly by the learners. The u-learning environment is interactive which allows learners to interact with teachers, peers, and experts effectively and efficiently via different media. U-learning is context-aware where the environment can adjust to learners‟ real situations to necessitate adequate information for them [1,2,3]. U-learning has progressed in the recent unprecedented times of COVID19 and climate change and its adoption is considered germane as conventional teaching and learning [4]
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