This study examines the efficacy of robots as assistive technology (AT) learning tools for children with autism spectrum disorder (ASD). The study attempts to find answers to whether robots as assistive tools can (i) profoundly improve achievement in learning and (ii) provide valuable learning experiences among this group of children. Using LEGO Mindstorms EV3, a robot was built and programmed to teach the basic concept of place value in mathematics. Eight children with ASD, specifically, four females and four males, participated in the single case study, and six special education teachers took part in the interviews. The children participated in both traditional and robotic intervention lessons and were assessed at the end of each session. The results indicate a positive increase in content knowledge and an improved disposition toward learning, thus demonstrating the potential utilization of robots as AT tools for harnessing classroom learning. Data from the interviews with teachers highlighted four valuable learning experiences that occurred in the classrooms as a result of the robotic interventions; namely, with respect to young children with ASD, the AT (1) promoted interest and engagement, (2) increased attention and focus, (3) triggered interactions and communication, and (4) created a happy and fun learning environment.

Game-based learning (GBL) has been widely utilized in various domains such as the military, education, marketing and advertising. Despite its popularity, an assessment of its effectiveness as a learning or a training tool is still unclear. Therefore, the main aim of this research work is to evaluate the effectiveness of using GBL in Higher Education. This paper briefly discusses a proposed research framework, the data analysis framework, some results obtained from a data collection activity, as well as a statistical analysis of the data

Learning disability (LD) is a situation where a person has difficulties in receiving, processing and storing information in their brain. There are many types of learning disabilities including reading (Dyslexia), math (Dyscalculia) and writing (Dysgraphia). The utilization of mobile learning has increased in education practice for LD children. Despite an increased interest in mobile learning, it is surprising that very limited empirical research has actually been conducted on the mobile app design model for Dyscalculia children especially from the perspectives of mobile app designers and developers. The purpose of this paper is to investigate the existing mobile app model that is related to Dyscalculia children, to identify the factors that influence Dyscalculia children in learning and to propose the mobile app design model for Dyscalculia children in Malaysia called Calculic Kids. Hence, this research work starts with rigorous literature review search on the domains related to LD, Dyscalculia and existing mobile app model. Next, all the relevant literatures were consolidated and analyzed. From literature review, two existing mobile app models related to Dyscalculia children were reviewed and analyzed. This has resulted in identifying 3 factors and 19 components in Initial Calculic Model.

Organisations invest in a considerable number of resources towards implementing Enterprise Content Management Systems (ECMS), expecting productive outcomes from the ECMS utilisation. A well-designed ECMS is impressive and deserves admiration, but to the organisations the ECMS limited value is often a major highlight. This motivates this study to understand how sustainable investment in Enterprise Content Management (ECM) technologies could support organisational Agile Business Processes (ABP). In studying how different organisation types utilise ECMS, the research employs a cross-sectional multiple-case studies approach with participation of seven large-scaled organisations. An investigation and an exploration into how these organisations interacted with ECMS are pivotal to the research. Based on the qualitative data analysis, this study proposes a sustainable ECMS-use framework that explains how ABP could benefit from it. Essentially, the framework enlightens researchers and practitioners to leverage ECM technologies for different work practices supporting ABP and organisational structures.

The expeditious propagation of fake news through online social media platforms has cropped up as a captious challenge, undermining the credibility of information sources and affecting public trust. Accurate detection of fake news is imperative to maintain the integrity of online content but is constrained by availability of data. This research aims to detect fake news from online articles by proposing a novel deep learning ensemble network capable of effectively discerning between genuine and fabricated news articles using limited data. We introduce ScrutNet, which leverages the synergistic capabilities of a bidirectional long short-term memory network and a convolutional neural network, which have been meticulously designed and fine-tuned for the task by us. This comprehensive ensemble classifier captures both sequential dependencies and local patterns within the textual data without requiring very large datasets like transformer based models. Through rigorous experimentation, we optimise the individual model parameters and ensemble strategy. The experimental results showcase the remarkable efficacy of ScrutNet in the detection of fake news, with an outstanding precision of 99.56%, 99.43% specificity, and an F1 score of 99.49% achieved on the partition test of the data set. Comparative analysis against state-of-the-art baselines demonstrates the superior performance of ScrutNet, establishing its prominence as a generalised and dependable fake news detection mechanism.