Application of Portfolio in Nursing Education and Assessment of Learning

Introduction: Portfolio is one of the active learning strategies for clinical education. By making portfolio, students present their own projects including clinical learning activities at or near the end of a clinical course. The purpose of this study was to investigate the application of this tool in nursing education in order to know the advantage and limitation of the tool. Methods: This study reviews the literature published in Farsi and English with the possibility of accessing the full text of the article over the past five years related to the use of portfolio in nursing education. A literature review was done by searching the keywords portfolio and nursing in the databases including; Web of Science, and ProQuest and scientific search engine such as Google Scholar. After removing repetitive and non-related items, 17 articles were selected accordingly. Result: The review of literature suggests that the portfolio is used in different schools and courses of nursing students with different goals such as assessment, evaluation, training; and performance improvement. Using portfolios has some advantages and limitations that need to be determinate for designing and implementing portfolios. Some benefits of portfolios were development of skills, fostering active learning, improvement of clinical competencies, and satisfaction of students from assessment and academic achievement. Its limitations include the lack of clarity and time constraints for completing it. Conclusion: The portfolio facilitates the monitoring of nurses' professional development and facilitates knowledge management. Therefore, designing and using this tool is recommended to improve the clinical competence of nursing students in undergraduate, graduate and postgraduate studies in Iran. However, the scope and purpose of using the portfolio should be specified and potential users should be well aware of the issue and its importance, and to learn the skills necessary to use it.

The Effects of Feature Optimization on High-Dimensional Essay Data

Current machine learning (ML) based automated essay scoring (AES) systems have employed various and vast numbers of features, which have been proven to be useful, in improving the performance of the AES. However, the high-dimensional feature space is not properly represented, due to the large volume of features extracted from the limited training data. As a result, this problem gives rise to poor performance and increased training time for the system. In this paper, we experiment and analyze the effects of feature optimization, including normalization, discretization, and feature selection techniques for different ML algorithms, while taking into consideration the size of the feature space and the performance of the AES. Accordingly, we show that the appropriate feature optimization techniques can reduce the dimensions of features, thus, contributing to the efficient training and performance improvement of AES.