Intelligent diagnostic feedback for online multiple-choice questions

When students attempt multiple-choice questions (MCQs) they generate invaluable information which can form the basis for understanding their learning behaviours. In this research, the information is collected and automatically analysed to provide customized, diagnostic feedback to support students’ learning. This is achieved within a web-based system, incorporating the snap-drift neural network based analysis of students’ responses to MCQs. This paper presents the results of a large trial of the method and the system which demonstrates the effectiveness of the feedback in guiding students towards a better understanding of particular concepts

Treatment Satisfaction, Expectations, Patient Preferences, and Characteristics in Patients with Rheumatoid Arthritis (Ra): Turkish Cohort Results of the Sense Study

Annual European Congress of Rheumatology (EULAR) -- JUN 02-05, 2021 -- ELECTR NETWORK[No Abstract Available]AbbVieAbbVieThe design, study conduct, and financial support for the study were provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the publication. All authors have received research funding for this study. The authors wish to thank B. Murat Ozdemir of Monitor CRO for medical editing and reviewing services of this manuscript. AbbVie provided funding to Monitor CRO for this work