ABSTRACT
The online learning system during the Covid-19 pandemic is the best choice in the learning process, however, the quality of learning and knowledge transfer remains the top priority. An android-based blended learning system is used at the Faculty of Sharia and Law which presents the features needed in the online teaching and learning process by combining face-to-face learning using an e-learning system and a two-way communication system, so that maximum quality of learning can be achieved. © 2022 IEEE.
ABSTRACT
Because of COVID-19 pandemic, online movies are now extremely popular. While the movie theaters have not serviced and people are staying quarantine, movies are the best choice for relaxing and treating stress. In present, recommender systems are widely integrated into many platforms of movie applications. A hybrid recommender system is one promising technique to improve the system performance, especially for cold-start, data sparsity, and scalability. This paper proposed a hybrid of matrix factorization, biased matrix factorization, and factor wise matrix factorization to solve all mentioned drawback problems. Simulation shows that the proposed hybrid algorithm can decrease approximately 11.91% and 10.70% for RMSE and MAE, respectively, when compared with the traditional methods. In addition, the proposed algorithm is capable of scalability. While the number of datasets is tremendously increased by 10 times, it is still effectively executed. © 2022 IEEE.
ABSTRACT
The unprecedented global pandemic COVID-19 significantly disrupted the higher education sector by forcing educators to rethink modes of content delivery. As COVID-19 restrictions slowly lifts, many institutions are operating a hybrid course delivery structure: online lectures and small groups of in-person, hands-on learning sessions. In this paper, a method to model student cohort learning communities is proposed. This model would limit viral spreading through its small and static nature, while promoting a sense of community and identity-building. A similar learning community model was implemented within a 2nd year Integrated Learning Stream pilot program. The goal of this study is to identify the optimal student cohort configuration, based on an anonymized dataset of 81 electrical engineering students' Fall 2020 semester enrollment records. Three very large scale integrated (VLSI) circuit clustering algorithms (Hyperedge Coarsening, Modified Hyperedge Coarsening, and Best Choice) are implemented. The resulting cohorts are evaluated based on cohort members' number of possible interactions external to their cohort. The Best Choice algorithm yielded more uniform cohorts that are less connected with other clusters, showing the cohort model to be a viable method of grouping students to limit cross-cohort transmission. Post-pandemic, the proposed method can be applied in many cohort-based learning use cases. © American Society for Engineering Education, 2021
ABSTRACT
At the beginning of 2020, the outbreak of COVID-19, a black swan event, brought a huge impact and change to people's normal life. Online consultation has become the best choice for users to consult and seek medical treatment. This paper starts from the problem of the experience of User Consultation of the Medical Mobile Media Platforms under the Backdrop of COVID-19, method based on an online survey from of 167 valid questionnaires, we studied the influence and role of medical APP on the society and the rigid medical demand. Based on the research purposes, the content of the Mobile Medical App User Consultation, the Use Effect of Mobile Medical App User Consultation are studied on field research. Research shows that the consultation design is not perfect, the application effect varies due to the differences in age, region, condition, cost and other aspects of the patients. But we can be predicted that in addition to its unique diagnostic and therapeutic advantages in the face of epidemic diseases such as COVID-19, it can also play a role in the diagnosis and treatment of common diseases and chronic diseases. © 2021, Springer Nature Switzerland AG.