Edubot: A framework for chatbot in education

Recently, chatbots have been used in various domains including health care and entertainment. Despite the impact of using chatbots on student engagement, there is little investment in how to develop and use chatbots in education. Such use of advanced technologies supports student learning, both individually and collaboratively. The effective use of chatbots in education depends on different factors, including the learning process, teaching methods, communications, etc. In this paper, the authors focus on the systematic utilization of chatbots in education. A proof of concept has been developed and tested using two MSc module, i.e., cloud computing and software engineering. The authors have used AWS Services to build the backend of Chatbot and integrate it with Facebook Messenger to allow students to learn via an additional venue, i.e., social media. The use of EDUBOT proved that chatbot can improve student learning and engagement especially at the time of COVID-19 where higher education is moving towards online teaching. Extending EDUBOT framework will help to support students ' admin and other queries. © 2023, IGI Global. All rights reserved.

Convolutional neural network based SARS-CoV-2 patients detection model using CT images

The COVID-19 disease caused by the SARS-CoV-2 infection has widely spread round the globe. Due to the large number of infected cases and rapid spread, it has been declared a global pandemic by World Health Organization on March 2020. There are several methods that identify and detect the COVID patient. However, detection using these methods can be confirmed after up to 10 days of the infection. This research presents a convolutional neural network (CNN) based classification model for detecting a COVID patient using CT image of patient. The dataset, used for the study, consists of CT images of variable sizes. It is a challenge for building a CNN model for variable sizes of the input image. This research uses a hybrid technique to overcome this challenge. It employs and analyses three different methods (such as Adam optimiser, Stochastic gradient descent with momentum optimiser, and RMSProp optimiser) for building the CNN model. Among the three CNN models, for CT image-based classification for infected or non-infected patient, adam performs better than RMSprop and sgdm. The classification accuracy achieved using adam is 94.9%, while RMSprop achieved an accuracy of 91.8% and sgdm reached 93.1%.

Capturing Public Concerns About Coronavirus Using Arabic Tweets: An NLP-Driven Approach

This In order to analyze the people reactions and opinions about Coronavirus (COVID-19), there is a need for computational framework, which leverages machine learning (ML) and natural language processing (NLP) techniques to identify COVID tweets and further categorize these in to disease specific feelings to address societal concerns related to Safety, Worriedness, and Irony of COVID. This is an ongoing study, and the purpose of this paper is to demonstrate the initial results of determining the relevancy of the tweets and what Arabic speaking people were tweeting about the three disease related feelings/emotions about COVID: Safety, Worry, and Irony. A combination of ML and NLP techniques are used for determining what Arabic speaking people are tweeting about COVID. A two-stage classifier system was built to find relevant tweets about COVID, and then the tweets were categorized into three categories. Results indicated that the number of tweets by males and females were similar. The classification performance was high for relevancy (F=0.85), categorization (F=0.79). Our study has demonstrated how categories of discussion on Twitter about an epidemic can be discovered so that officials can understand specific societal concerns related to the emotions and feelings related to the epidemic.

A User-Centric Framework for Educational Chatbots Design and Development

Increasing frequency of epidemics, such as SARS-CoV, MERS-CoV, Ebola, and the recent COVID-19, have affected various sectors, especially education. As a result, emphasis on e-learning and distance learning has been increasing in recent years. The growing numbers of mobile users and access to the internet across the world has created more favorable conditions for adopting distance learning on a wider scale. However, lessons learnt from current experiments have highlighted poor student engagement with learning processes, hence a user-centric approach to design and develop educational chatbots is presented. A User-centric approach enables developers to consider the following: learners’ and teachers’ technological skills and competencies, attitudes, and perceptions and behaviour;conceptual concerns, such as pedagogical integration on online platforms, assessment procedures, varying learning culture and lifestyles;technical concerns, such as privacy, security, performance, ubiquity;and regulatory concerns, such as policies, frameworks, standards, ethics, roles and responsibilities have been identified in this study. To address these concerns, there is the need for user-centric design and collaborative approaches to the development of distance learning tools. Considering the abovementioned challenges and the growing emphasis on distance learning, we propose chatbot learning as an effective and efficient tool for delivering such learning. In this regard, a user-centric framework for designing chatbot learning applications and a collaborative user-centric design methodology for developing chatbot learning applications is proposed and discussed. © 2020, Springer Nature Switzerland AG.