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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-20242502


The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy. © 2022 IEEE.

4th International Conference on HCI in Games, HCI in Games 2022 Held as Part of the 24th HCI International Conference, HCII 2022 ; 13334 LNCS:427-443, 2022.
Article in English | Scopus | ID: covidwho-1919640


Switching from face-to-face learning to online learning due to the COVID-19 pandemic is challenging for students especially for children with autism spectrum disorder (ASD). Parents and caregivers of children with ASD struggle when adjusting to the new situation due to the lack of online supportive educational resources. This paper explores two different mediums for online learning resources;which are mobile applications and videos. The strengths and limitations of 11 mobile applications and two websites on educational videos were highlighted and compared using customized rubrics adapted from existing studies. Findings show that the existing applications require content enhancements, dynamic children-directed layout design and free access to a number of the contents. The reviewed videos on two different websites have quality content but lack explicit subject categorization as well as filtering features to help parents and caregivers access desired videos. Evaluation of online learning experience for children with ASD is still in its infancy and requires further research, especially in content development. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.