Browsing by Author "Siyam, Nur"

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    A Pilot Study Investigating the Use of Mobile Technology for Coordinating Educational Plans in Inclusive Settings
    (SAGE journals, 2021) Siyam, Nur; Abdallah, Sherief
    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.
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    Mining government tweets to identify and predict citizens engagement
    (ScienceDirect, 2019) Siyam, Nur; Alqaryouti, Omar; Abdallah, Sherief
    The rise of social media offered new channels of communication between a government and its citizens. The social media channels are interactive, inclusive, low-cost, and unconstrained by time or place. This two-way communication between governments and citizens is referred to as electronic citizen participation, or e-participation. E-participation in the age of technology is considered as a mean for citizens to express their opinions and as a new input to be integrated by policy makers to take decisions. Governments and policy makers always aim to increase such participation not only to utilize public expertise and experience, but also to increase the transparency, trust, and acceptability of government decisions. In this research we investigate how governments can increase citizens e-participation on social media. We collected 55,809 tweets over a period of one year from Twitter accounts of a progressive government in the Arab world. This was followed by statistical analysis of posts characteristics (Type, Day, Time) and their impact on citizens’ engagement. Then, we evaluated how well can different machine learning techniques predict user engagement. Results of the statistical analysis confirmed that post type (video, image, link, and status) impacted citizens’ engagement, with videos and images having the highest positive impact on engagement. Furthermore, posting government tweets on weekdays obtained higher citizens’ engagement than weekends. Conversely, time of post had a weak effect on engagement. The results from the machine learning experiments show that two techniques (Random Forest and Adaboost) produced more accurate predictions, particularly when tweet textual contents were also used in the prediction. These results can help governments increase the engagement of their citizens.
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    Special Education Teachers’ Perceptions on Using Technology for Communication Practices
    (The British University in Dubai (BUiD), 2018) Siyam, Nur
    An important aspect to the effective education of students with special educational needs and disabilities (SEND) is the successful coordination between all stakeholders (parents, educators and therapists). The present study employed qualitative methods to investigate teachers’ perceptions on the current communication practices and how they can be improved using technology. Nine special education teachers participated in semi-structured interviews. Teachers in the study used both traditional and, to a lesser degree, technological means of communication. Technological methods included instant messaging, the school’s information system, and electronic Individualized Education Programs (IEPs) shared on the cloud. The data revealed that some special education teachers were content with the current methods of communication, while others perceived the introduction of new technology useful for the improvement of the communication process. Results can be used to identify the requirements needed before developing and launching new innovations for communication between parties involved with the intervention of SEND students.
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    Toward automatic motivator selection for autism behavior intervention therapy
    (ProQuest Central, 2022) Siyam, Nur; Abdallah, Sherief
    Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interven tions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most infuential factors impacting the efectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the efectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.
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    Using Mobile Technology for Coordinating Educational Plans and Supporting Decision Making Through Reinforcement Learning in Inclusive Settings
    (The British University in Dubai (BUiD), 2021-07) Siyam, Nur
    Learners with special education needs and disabilities (SEND) require attention from a large set of a care team that includes parents, teachers, specialists, therapists, and doctors. Good coordination among these stakeholders leads to increased behavioural and academic progress for the learners. However, achieving good coordination in such setting is a challenging task. This is due to the different tasks each stakeholder is attempting, the different backgrounds of the stakeholders, and the lack of face-to-face interaction among them. I call this the intervention coordination problem (ICP). Furthermore, learners with SEND, and specially learners with autism spectrum disorder (ASD), usually show little interest in academic activities and may display disruptive behaviour when assigned certain tasks. Research indicates that selecting a good motivational variable during interventions improves behavioural and academic performance. I refer to this problem as the motivator selection problem (MSP). This work aims to exploit mobile and artificial intelligence (AI) technologies in order to address the above two problems. Toward this aim, this study follows a design science research approach to develop the IEP-Connect app. This mobile app uses the Individualized Education Program (IEP) as the foundation for coordinating the efforts and supporting the decision-making process of the different personnel who are involved in the IEP of a child with special needs. The proposed work presents four significant contributions, namely identifying the key design principles to inform the design of a coordination mobile app for special education, developing and implementing the IEP-Connect mobile app, modelling the selection of a motivator as a Markov Decision Process (MDP), and proposing a Reinforcement Learning (RL) framework to recommend a motivator to be used with students with SEND in a given learning setting. To evaluate the effectiveness of the proposed mobile app and RL framework, a series of studies based on participatory design research, mixed-methods usability evaluation, and pre-test/post-test quasi-experimental research methodology were conducted. The evaluation of the app focused on students with ASD as their learning requires sharing information from different distributed sources. Results from the usability questionnaires, interviews, and log data revealed that the app has good usability and that participants were satisfied with the use of the app for recording and sharing IEP information. Moreover, evaluations and data analysis have shown the validity of the proposed RL framework through improving the intervention effectiveness and users’ satisfaction. The implementation of this work provides insights into the future development of technology tools that facilitate information sharing between special education teachers and other stakeholders involved in the intervention of children with special education needs. Moreover, this work expands the interdisciplinary research of machine learning and special education by presenting promising preliminary results for therapy decision-making support.