Nursing lecturers' perception toward E-learning approaches used in nursing colleges: Scoping review.

BACKGROUND: Nursing colleges have traditionally taught students in hospitals and laboratories. COVID-19 compelled most nursing colleges to embrace e-learning without prior experience or preparation after 2020, which may influence nursing educators' views and attitudes toward its use. OBJECTIVE: This scoping review explores the nursing educators' perception of the e-learning approaches used in nursing colleges. DESIGN: A comprehensive review of five databases, Cochrane, Ebsco (Medline), PubMed, Science Direct, and Scopus, was conducted, adhering to the Joanna Brings Institute (JBI) standards full theme, utilizing preset eligibility criteria and adhering to the PRISMA Extension for Scoping review (PRISMA-ScR) recommendations. METHODS: This scoping review examined studies published in English from January 1st, 2017-2022. Three reviewers evaluated the eligibility of the literature and retrieved data to address the research question from prior literature. A content analysis was done. RESULTS: Thirteen articles with various hypotheses and models were reviewed. The review reveals that nursing educators are novices at using e-learning approaches in their classes due to their novelty in most nursing colleges. Nursing educators have a modest positive perception, with an optimistic perspective on e-learning effectiveness in theoretical course teaching, emphasizing that it is inappropriate in teaching clinical courses. The review demonstrates that e-learning faces numerous challenges that negatively impact educators' perceptions. CONCLUSION: Institutional preparedness in terms of personnel through educator training, provision of necessary infrastructure, administrative support, and incentives are critical to improving the perception of the e-learning method and increasing its adoption in nursing colleges.

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques.

Breast cancer must be addressed by a multidisciplinary team aiming at the patient's comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.