With the rapid development of computer technology and network technology and the widespread popularity of electronic equipment, communication among people is more dependent on the Internet. The Internet has brought great convenience to people's lives and work, and the Internet data is constantly being recorded. People's data information and behavior information, which provides the basis for data mining and recommendation systems, mining users' information and behaviors, and providing "user portraits" for each user, can provide better services to users and it is also an important part of the recommendation system. In one step, this article takes MOOC education resources as the research goal. In order to improve the effective management of MOOC platform resources based on traditional methods, this article combines relevant data sets and recommendation techniques to initially build a learning platform, implements a deep neural network algorithm, and recommends related services. The request and response data were explained, and through the online learning data set, based on the learner's historical learning records, the learning resources were simulated and recommended to the learners. The resource customization module was elaborated. Through the results of resource recommendation, a personalized learning resource recommendation platform was initially realized, which more intuitively demonstrated the recommendation effect and better realized the teaching management of the MOOC platform.
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http://dx.doi.org/10.1155/2022/8031602 | DOI Listing |
PLoS One
November 2024
School of Psychology, Zhejiang Normal University, Jinhua, China.
This paper seeks to explore the influence of success factors, specifically motivation and course quality, on MOOC retention intention. Going beyond a mere examination of these motivational and quality factors, the study investigates students' motivation, considering needs, interests, course system, content, and service quality. Methodologically, a questionnaire survey was conducted, collecting data from 311 students enrolled in online courses.
View Article and Find Full Text PDFEur J Investig Health Psychol Educ
October 2024
Laboratory for Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte, Natal 59012-300, RN, Brazil.
Virtual Learning Environments have become innovative tools in health professionals education. Through Massive Open Online Courses, they enable different ways of connecting with knowledge, facilitating study autonomy, interaction, and closer alignment with professional practices and the context of course participants. MOOCs comprise an educational strategy for many fields, including health.
View Article and Find Full Text PDFFront Public Health
October 2024
Department of Emergency Medicine, Stanford University, Palo Alto, CA, United States.
Introduction: COVID-19 created a global need for healthcare worker (HCW) training. Initially, mass trainings focused on public health workers and physicians working in intensive care units. However, in resource-constrained settings, nurses and general practitioners provide most patient care, typically lacking the training and equipment to manage critically ill patients.
View Article and Find Full Text PDFBMJ Open Qual
September 2024
Department of Allergy and Immunology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
Background: Over 95% of penicillin allergy labels are inaccurate and may be addressed in low-risk patients using direct oral penicillin challenge (DPC). This study explored the behaviour, attitudes and acceptability of patients, healthcare professionals (HCPs) and managers of using DPC in low-risk patients.
Methods: Mixed-method, investigation involving patient interviews and staff focus groups at three NHS acute hospitals.
PeerJ Comput Sci
April 2024
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Student dropout prediction (SDP) in educational research has gained prominence for its role in analyzing student learning behaviors through time series models. Traditional methods often focus singularly on either prediction accuracy or earliness, leading to sub-optimal interventions for at-risk students. This issue underlines the necessity for methods that effectively manage the trade-off between accuracy and earliness.
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