Search

Good coordination among school staff and families leads to increased learning quality and academic success for students with special education needs and disabilities (SEND). This pilot study aims to investigate the use of mobile technology for the coordination of therapy and learning for students with SEND. This study first follows a participatory design methodology to identify the key design principles required to inform the design of a coordination mobile app for special education. Then, a mobile app (IEP-Connect) is designed and implemented with the aim of facilitating information sharing between different parties involved in the intervention of students with SEND. The proposed app uses the Individualized Educational Plan (IEP) as the focal point of coordination. The evaluation of the app focused on students with autism spectrum disorder (ASD) as their learning requires sharing information from different distributed sources. Results from the usability study revealed that the app has "good" usability and that participants were satisfied with the use of the app for recording and sharing IEP information. The results of this study provide an understanding of the ways in which a coordination app for special education could be made easy and rewarding to use.

Curriculum mapping is a method in which teachers record the operational curriculum to be used for ongoing curriculum review and development. Therefore, schools aim to establish a fluid system for maintaining and updating the curriculum. As teachers are the main practitioners of curriculum mapping, this study aimed to investigate the factors that impact teachers' attitudes towards an online curriculum mapping tool. In this study we employed the Technology Acceptance Model (TAM) to examine the attitudes of the academic staff in a private school in Dubai towards the use of a curriculum mapping tool and whether the staff role impacted their perceptions. Also, we examined the factors that impact perceived ease of use and perceived usefulness of the curriculum mapping tool. The external factor considered as a determinant of perceived usefulness is relevance, which is studied in terms of long-term planning, short-term planning, curriculum alignment, teachers' collaboration, and students' achievement. The factors considered as determinants of perceived usefulness are training and time. The results indicated that the staff did not have significant positive attitudes towards the tool. Also, while perceived ease of use and actual usage were significantly high, perceived usefulness was significantly low. The results also showed that there were no differences in perceptions according to the staff role except for perceived usefulness, which was significantly higher for leaders compared to teachers. Results also indicated that perceived short-term planning and teacher collaboration were good predictors of perceived usefulness, while perceived training and time were good predictors of perceived ease of use. The results reflect a shallow understanding of the theory that underlies the use of the curriculum mapping tool. School leaders should dedicate time and resources to extend communication about the purpose of curriculum mapping and how the mapping tool can help the process.

With the increased level of technology usage in schools and the move to online learning, many schools had to re-evaluate the content of their cyber-safety policy and review it to ensure it works within and beyond the schools' premises. This study aimed to analyse the cyber-safety policies of twenty private schools in Dubai, an emirate in the United Arab Emirates (UAE). Five main categories were considered for the content analysis of the policy documents including definitions, preventive measures, reporting and responding to incidents, connection to other policies and mention of existing legislation. Upon the analysis of the policy documents, it was found that while some addressed cyber-safety issues, the focus remained more on cyberbullying incidents. Besides, the development of the cyber-safety policies is lacking the input from the concerned authorities whose ultimate responsibility is to develop the major policies and guidelines to be adopted by schools.

This study uses the Technology Acceptance Model (TAM) to explore the factors that impact special education teachers' acceptance and actual use of technology. TAM is used as the foundation for generating the hypothesis and developing the conceptual framework for the study. Twenty-four (n=24) special education teachers in a private school in the United Arab Emirates (UAE) participated in this study by answering an electronic questionnaire that included items related to Perceived Usefulness, Perceived Ease of Use, Attitudes Towards Usage, Behavioural Intention to Use, Access to Technology, Job Relevance, Self-Efficacy, Time, and Actual Usage. Preliminary findings indicate that special education teachers have positive attitudes towards the use of technology. Self-efficacy, time and access to technology were found to significantly impact actual use of technology. The research results provide initial insights on special education teachers attitudes towards using technology in their practice as well as the factors that may facilitate or hinder their actual use. Implications for practice and future research are discussed.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers' feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models. [ABSTRACT FROM AUTHOR]