Examining the use of Robots as Teacher Assistants in Uae Classrooms: Teacher and Student Perspectives

Aim/Purpose This study sought to understand the views of both teachers and students on the usage of humanoid robots as teaching assistants in a specifically Arab context. Background Social robots have in recent times penetrated the educational space. Although prevalent in Asia and some Western regions, the uptake, perception and ac-ceptance of educational robots in the Arab or Emirati region is not known. Methodology A total of 20 children and 5 teachers were randomly selected to comprise the sample for this study, which was a qualitative exploration executed using fo-cus groups after an NAO robot (pronounced now) was deployed in their school for a day of revision sessions. Contribution Where other papers on this topic have largely been based in other countries, this paper, to our knowledge, is the first to examine the potential for the inte-gration of educational robots in the Arab context. Findings The students were generally appreciative of the incorporation of humanoid robots as co-teachers, whereas the teachers were more circumspect, express-ing some concerns and noting a desire to better streamline the process of bringing robots to the classroom. Recommendations for Practitioners We found that the malleability of the robot’s voice played a pivotal role in the acceptability of the robot, and that generally students did well in smaller groups with the robot; teachers expressed concern that the children would become easily distracted should too many children be privy to one robot. Recommendations for Researchers Our results provide valuable recommendations for researchers in the area. We believe, there needs to be continued efforts in devising suitable methodo-logical assessment tools to evaluate student and teacher attitudes in the class-room particularly in the Arab world. We also advise researchers to focus on providing adaptive behavior in the context of educational robots. There are different distinct areas that need further clarifications and study based on our review. Impact on Society On a wider scale, the findings of this paper have a huge implication for the educational technology as the integration of robotics in education is one of the emerging trends in the area, particularly in the UAE. This study allows to answer questions related to attitudes and perceptions of both teachers and students toward educational robots in the UAE. Future Research Possible avenues of research in the area include focusing on the adaptive and natural behavior of robots in disciplines other than Mathematics as a means of successfully integrating robots in the classroom

Examining the use of robots as teacher assistants in UAE classrooms : teacher and student perspectives

Aim/Purpose - This study sought to understand the views of both teachers and students on the usage of humanoid robots as teaching assistants in a specifically Arab context. Background - Social robots have in recent times penetrated the educational space. Although prevalent in Asia and some Western regions, the uptake, perception and acceptance of educational robots in the Arab or Emirati region is not known. Methodology - A total of 20 children and 5 teachers were randomly selected to comprise the sample for this study, which was a qualitative exploration executed using focus groups after an NAO robot (pronounced now) was deployed in their school for a day of revision sessions. Contribution - Where other papers on this topic have largely been based in other countries, this paper, to our knowledge, is the first to examine the potential for the integration of educational robots in the Arab context. Findings - The students were generally appreciative of the incorporation of humanoid robots as co-teachers, whereas the teachers were more circumspect, expressing some concerns and noting a desire to better streamline the process of bringing robots to the classroom. Recommendations for Practitioners - We found that the malleability of the robot’s voice played a pivotal role in the acceptability of the robot, and that generally students did well in smaller groups with the robot; teachers expressed concern that the children would become easily distracted should too many children be privy to one robot. Recommendations for Researchers - Our results provide valuable recommendations for researchers in the area. We believe, there needs to be continued efforts in devising suitable methodological assessment tools to evaluate student and teacher attitudes in the classroom particularly in the Arab world. We also advise researchers to focus on providing adaptive behavior in the context of educational robots. There are different distinct areas that need further clarifications and study based on our review. Impact on Society - On a wider scale, the findings of this paper have a huge implication for the educational technology as the integration of robotics in education is one of the emerging trends in the area, particularly in the UAE. This study allows to answer questions related to attitudes and perceptions of both teachers and students toward educational robots in the UAE. Future Research - Possible avenues of research in the area include focusing on the adaptive and natural behavior of robots in disciplines other than Mathematics as a means of successfully integrating robots in the classroom

Use of YouTube as a source of information for quitting or cutting down alcohol

Background: Although research has been done on considering YouTube for dissuading and encouraging unhealthy behaviours such as smoking, less focus has been placed on its role in quitting or cutting down alcohol. This study aims to analyse the alcohol cessation videos available and accessible on YouTube to gain a more in-depth insight into the ways that YouTube as a platform is being used to persuade with relation to alcohol cessation. Methods: We systematically searched for content on YouTube related to alcohol cessation and these videos were analysed and evaluated for the format, themes, specific alcohol cessation advice, and uploader. Results: The results demonstrated that the collected alcohol cessation videos included a fairly even presence of the themes of discussing the negative impacts of alcohol and the benefits of quitting or staying away from it. At the same time, however, we found the videos were not sourced from professional institutions, such as government or anti-alcohol misuse non-government organisations. Conclusion: More research is needed to investigate utilising YouTube to support those looking to quit or cut down alcohol

Humanoid robots as teaching assistants in an arab school

© 2019 Association for Computing Machinery. The proliferation of educational robots has led to an investigation of suitable roles that humanoids robots can take in the classroom. In the recent past, the focus has been on humanoids being used in student focused roles or as peer learners. Coupled with the seemingly absence of any case studies of educational robots in the United Arab Emirates (UAE) or the Arab world, we present a study where we employed the Nao robot as a teaching assistant in a local primary school in Abu Dhabi, UAE. The Nao robot was used to revise a topic in Mathematics and its efficacy in comparison to a human teaching assistant was measured through pre and post test scores, facial expressions and indirect verbal responses. Our results showed that while there no significant differences in test scores, the children were much more engaged when interacting with the Nao robot. We conclude with a positive outlook towards the implementation of humanoid robots in UAE classrooms

Quranic education and technology : reinforcement learning system for non-native Arabic children

We built a simulator based on reinforcement learning to improve teaching experience in Quranic and Islamic education for non-native Arabic speakers to evaluate their strength and weaknesses and allow the system to help improving the child in one hand, and provide an accurate actual report for each child on the other hand

COVID-19 global risk : expectation vs. reality

Background and Objective: COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. Method: A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. Results: The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. Conclusion: In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators

You just do not understand me! Speech Recognition in Human Robot Interaction

Abstract — Speech Recognition has not fully permeated in our interaction with devices. Therefore we advocate a speech recognition friendly artificial language (ROILA) that initially was shown to outperform English, however under constraints. ROILA is intended to be used to talk to robots and therefore in this paper we present an experimental study where the recognition of ROILA is compared to English when speech is input using a robot’s microphones and both when the robot’s head is moving and stationary. Our results show that there was no significant difference between ROILA and English but that the type of microphone and robot’s head movement had a significant effect. In conclusion we suggest implications for Human Robot (Speech) Interaction. I

ReflectWorld: A Distributed Architecture for Meetings and Groups Evolution Analysis

In this paper, we describe the efforts in taking advantage of the latest IT developments, (especially of the development of mobile computing devices such as tablets or phones) to create a comprehensive architecture for face-to-face meetings support and groups evolution analysis. The main purpose of the architecture is to mitigate frequent meetings problems by providing state-of-the-art technological support to groups or teams. This paper introduces ReflectWorld, the distributed architecture created on top of the principles of Reflect Table, a meeting support and analysis system centered on individuals’ participation and interactions

An explainable machine learning framework for lung cancer hospital length of stay prediction

This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3–100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3–100%, and 97%, CI 95%: 93.7–100% SMOTE-Tomek, and SMOTE-ENN respectively). Under-sampling methods reported the least important AUC results (50%, with CI 95%: 40.2–59.8%) for both (ENN and Tomek- Links). Using ML explainable technique called SHAP, we explained the outcome of the predictive model (RF) with SMOTE class balancing technique to understand the most significant clinical features that contributed to predicting lung cancer LOS with the RF model. Our promising framework allows us to employ ML techniques in-hospital clinical information systems to predict lung cancer admissions into ICU

Personalized robot interventions for autistic children : an automated methodology for attention assessment

We propose a robot-mediated therapy and assessment system for children with autism spectrum disorder (ASD) of mild to moderate severity and minimal verbal capabilities. The objectives of the robot interaction sessions is to improve the academic capabilities of ASD patients by increasing the length and the quality of their attention. The system uses a NAO robot and an added mobile display to present emotional cues and solicit appropriate emotional responses. The interaction is semi-autonomous with minimal human intervention. Interaction occurs within an adaptive dynamic scenario composed of 13 sections. The scenario allows adaptive customization based on the attention score history of each patient. The attention score is autonomously generated by the system and depends on face attention and joint attention cues and sound responses. The scoring system allows us to prove that the customized interaction system increases the engagement and attention capabilities of ASD patients. After performing a pilot study, involving 6 ASD children, out of a total of 11 considered in the clinical setup, we conducted a long-term study. This study empirically proves that the proposed assessment system represents the attention state of the patient with 82.4% accuracy