Students' engagement in academic-related learning activities is one of the important determinants of students' success. Identifying the best teaching strategies to sustain and promote nursing students' engagement in academic and clinical settings has always been a challenge for nurse educators. Hence, it is essential to provide a set of strategies for maintaining and enhancing the academic engagement of nursing students. The purpose of this review was to explore and summarize the strategies that nurse educators use to sustain and promote nursing students' engagement in academic and clinical settings. A narrative literature review was conducted. CINAHL (nursing content), ProQuest, Medline, the Cochrane, Google Scholar, and Scopus were searched. Of 1,185 retrieved articles, 32 teaching strategies were identified and extracted from the nursing literature. We used thematic analysis approach to organize these strategies into five main categories as follows: technology-based strategies (15 articles), collaborative strategies (10 articles), simulation-based strategies (two articles), research-based strategies (two articles), and miscellanea learning strategies (three articles). As a general comment, these strategies have the potential to promote nursing students' engagement. Among the strategies discussed in this review, the use of technology, particularly the response system and online learning, was more common among nursing educators, which is in line with today's advances in smart technologies. The collection presented in this review can be used as a starting point for future research to evaluate the effectiveness of an educational intervention on the academic engagement of nursing students. Nevertheless, due to the lack of experimental studies, the optimal strategies remain to be elucidated through future high-quality experimental study.
Background and aimAcademic engagement is an important indicator of quality of higher education. This study aimed to explain the experiences of undergraduate nursing students in terms of student-related factors affecting academic engagement.MethodsThis qualitative study was conducted in 2017 at Mashhad University of Medical Sciences in Iran. Data were collected using semi-structured interviews and focus groups with 7 and 16 undergraduate nursing students at Mashhad School of Nursing and Midwifery; respectively. Undergraduate nursing students of both genders who enrolled in different academic semesters with various academic achievements were selected. Data were analyzed using conventional content analysis approach proposed by Graneheim and Lundman, with the support of MAXQDA software.ResultsAfter analyzing the data, 374 initial codes were extracted, which ultimately conceptualized within six main categories including: “learning motivation”, “interest in learning”, “student participation in extracurricular scientific programs”, “self-directedness”, “mental concentration”, and “demonstration of emotions”.ConclusionThe findings of this study indicated that student-related factors such as individual motivation and interest, mental concentration, participation in extracurricular activities, and self-directedness in learning, as well as students’ sense of satisfaction with learning could play important roles in the creation of academic engagement in undergraduate nursing students that need to be of interest to nursing educators and planners.
Recently, there has been a tendency to use machine learning (ML)–based methods, such as artificial neural networks (ANNs), for more accurate estimates. This paper investigates the effectiveness of three different machine learning methods including radial basis function neural network (RBNN), multi-layer perceptron (MLP), and support vector regression (SVR), for predicting the ultimate strength of square and rectangular columns confined by various FRP sheets. So far, in the previous study, several experiments have been conducted on concrete columns confined by fiber reinforced polymer (FRP) sheets with the results suggesting that the use of FRP sheets enhances the compressive strength of concrete columns effectively. Also, a wide range of experimental data (including 463 specimens) has been collected in this study for square and rectangular columns, confined by various FRP sheets. The comparison of ML-derived results with the experimental findings, which were in a very good agreement, demonstrated the ability of ML to estimate the compressive strength of concrete confined by FRP; the correlation coefficient (R2) for MLP, RBFNN, and SVR methods was equal to 0.97, 0.97, and 0.90, respectively. Similar accuracy was obtained by MLP and RBFNN, and they provided better estimates for determining the compressive strength of concrete confined by FRP. Also, the results showed that the difference between statistical indicators for training and testing specimens in the RBFNN method was greater than the MLP method, and this difference indicated the poor performance of RBFNN.
This research has addressed the effects of coarse aggregate (CA) and wavy steel fiber (WSF) volumes on the self‐compacting concrete (SCC) fracture parameters in beam and edge notched disk bend (ENDB) specimens using size effect and work fracture methods (SEM and WFM). Mix designs were based on 30%–60% CA volumes and 0.15%–0.45% WSF volumes, and 144 different‐size notched beam specimens underwent 3‐point bending tests. Results showed that increasing the CA volume and percent WSFs increased the SCC ductility in both SEM and WFM; increasing the WSF volume affected the fracture energy‐compressive strength relationship negatively at 30% and 40% CA volumes, and increasing the WSF affected the mentioned relationship positively at 50 and 60% CA volumes. Finally, we predicted the relationship between the fracture parameters calculated with ENDB specimens and those of beam specimens calculated with SEM and WFM.
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