A scoping review of videoconferencing systems in higher education:Learning paradigms, opportunities, and challenges

Videoconferencing as a learning tool has been widely used among educators and learners in order to induce effective communication between learners and teachers or learners and their peers, especially when face-to-face means are not possible. Different types of videoconferencing platforms or systems have emerged for use in today’s higher education institutions. Previous research has focused on examining the potential of three different forms of videoconferencing systems: desktop videoconferencing (DVC), interactive videoconferencing (IVC), and Web videoconferencing (WVC). In this study, a review of the literature was conducted to increase the current knowledge regarding the use of these videoconferencing systems. A classification of the videoconferencing paradigms from the constructivism and cognitivism perspectives was provided. The summary of the results for these videoconferencing systems revealed specific learning opportunities, outcomes, and challenges for both learners and instructors. The results suggest that current policy and teaching strategies are not ready to provide an accessible and comprehensive learning experience in DVC and IVC. Relative to previously conducted studies regarding the use of videoconferencing in higher education, this study offers a broader consideration of relevant challenges that emerge when using certain videoconferencing systems in both learning and teaching situations

Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches

Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users’ sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts