Meta-Analysis of Artificial Intelligence Works in Ubiquitous Learning Environments and Technologies

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%; I 2 = 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%), I 2 = 99.83%] was considerably more heterogeneous (I 2 = 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.

Dimensionality Reduction of Social Media Application Attributes for Ubiquitous Learning Using Principal Component Analysis

“…In the study by Meyliana et al [11], students' social media preference was analysed to increase student engagement with the university. Data was collected from 1021 students from fifty-eight Indonesian universities using questionnaires [11,12]. Entropy was used to process data and assign criteria weights for social media preference.…”

“…Using the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method, it was established that the implementation of social media was more dependent on information quality as opposed to service quality. However, while comprehensiveness and usefulness of information were highly essential to students, they also valued system availability, efficiency, and fulfilment as they directly impacted their expectation and active learning process [11,12].…”

“…ere exists a behavioural studies' research gap on the reasons why users join and participate in SNSs [12,16]. e 2…”

“…A multicriteria decisionmaking (MCDM) tool and the fuzzy AHP (Analytical Hierarchy Process) were used to evaluate the level of importance of literature-derived educational requirements such as performance expectancy, effort expectancy, social influence, self-efficacy, perceived enjoyment, and attitude toward technology; technology criteria such as facilitating conditions, trust, privacy, and security had the adoption of an SNS. Data was collected from 291 university students in the field of the SNS via questionnaires, and the findings of the study were trust, performance expectancy, and security which were critical influential factors in SNS adoption [12,16]. e study by Woodcock et al [17] investigated the efficiency of an online synchronous platform employed to train preservice teachers using a blended learning approach.…”

“…Classroom technology incorporates interactive learning technology such as e-book technology. In Malaysia, the acceptance of novel technology such as e-book technology by school children is important [12,21]. e study by Elyazgi et al in [21] identified the interface factors of Children Computer Interaction (CCI) and the determinants of usability guiding e-book behavioural acceptance by 417 school learners.…”