Relationship between self-directed learning readiness, learning attitude, and self-efficacy of nursing undergraduates

Objective: The purposes of this study were to analyze the influencing factors of self-directed learning readiness(SDLR) of nursing undergraduates and explore the impacts of learning attitude and self-efficacy on nursing undergraduates.Methods: A total of 500 nursing undergraduates were investigated in Tianjin, with the Chinese version of SDLR scale, learning attitude questionnaire of nursing college students, academic self-efficacy scale, and the general information questionnaire.Result: The score of SDLR was 149.99±15.73. Multiple stepwise regressions indicated that academic self-efficacy, learning attitude, attitudes to major of nursing, and level of learning difficulties were major influential factors and explained 48.1% of the variance in SDLR of nursing interns.Conclusions: The score of SDLR of nursing undergraduates is not promising. It is imperative to correct students' learning attitude, improve self-efficacy, and adopt appropriate teaching model to improve SDLR.

Effectiveness of the flipped classroom on the development of self-directed learning in nursing education:a meta-analysis

Objectives: To examine the best practice evidence of the effectiveness of the flipped classroom（FC） as a burgeoning teaching model on the development of self-directed learning in nursing education.Data sources: The relevant randomized controlled trial（RCT） and non-RCT comparative studies were searched from multiple electronic databases including PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature（CINAHL）, Cochrane Central Register of Controlled Trials（CENTRAL）, Wanfang Data, China National Knowledge Infrastructure（CNKI）, and Chinese Science and Technology Periodical Database（VIP） from inception to June 2017.Review methods: The data were independently assessed and extracted for eligibility by two reviewers. The quality of included studies was assessed by another two reviewers using a standardized form and evaluated by using the Cochrane Collaboration’s risk of bias tool. The self-directed learning scores（continuous outcomes） were analyzed by using the 95% confidence intervals（Cls） with the standard deviation average（SMD） or weighted mean difference（WMD）. The heterogeneity was assessed using Cochran’s I;statistic.Results: A total of 12 studies, which encompassed 1440 nursing students（intervention group = 685, control group = 755）, were eligible for inclusion in this review. Of 12 included studies, the quality level of one included study was A and of the others was B. The pooled effect size showed that compared with traditional teaching models, the FC could improve nursing students’ selfdirected learning skill, as measured by the Self-Directed Learning Readiness Scale（SDLRS）, Self-Directed Learning Readiness Scale for Nursing Education（SDLRSNE）, Self-Regulated Learning Scale（SRL）, Autonomous Learning Competencies scale（ALC）, and Competencies of Autonomous Learning of Nursing Students（CALNS）. Overall scores and subgroup analyses with the SRL were all in favor of the FC.Conclusions: The result of this meta-analysis indicated that FCs could improve the effect of self-directed learning in nursing education.Future studies with more RCTs using the same measurement tools are needed to draw more authoritative conclusions.

The effect of self-observation on the self-directed learning ability of nursing students: An experimental study

Background: Patients expect nurses to be both technically competent and psychosocially skilled. Enhancing the quality of patient care and patient safety in healthcare settings has increased, resulting in limited opportunities for students to practice clinical skills in healthcare settings. Achieving competence in these skills is viewed as an essential task to be completed during the school curriculum. Objective: The purpose of this study was to evaluate the use of self-observation through cellular recordings as an adjunct to the clinical skills teaching of a blood sugar test to undergraduate nursing. Design: The research design consisted of pre- and post-test consecutive experimental design through a control group. Settings: This study targeted nursing students enrolled in baccalaureate programs running in Korea. Participant: The participants were 64 students including 34 for the experimental group and 30 for the control group. Methods: Those in the control group received standard teaching methods using lectures and skills classes and facilitated the use of self-study methods. Those in the experimental group received standard teaching using lectures and skills classes and facilitated use of cell phone recorded self-observation. The self-confidence of practicing a blood sugar test, satisfaction with the learning method, self-study participation, level of interest in nursing practice, and self-directed learning ability were measured using questionnaires. Results: Significant between-groups differences were detected in self-confidence of practicing a blood sugar test (t = 2.067, p = 0.043), satisfaction with the learning method (t = 2.818, p = 0.044), self-study participation (χ2 = 7.635, p = 0.022), and average self-directed learning ability (t = 3.202, p = 0.002). Conclusions: Self-observation through cellular phone recordings is an effective learning method as an adjunct to teach clinical skills.

Impact of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students

Objective: To explore the effects of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students. Methods: A convenience sampling of 460 undergraduate nursing students was surveyed in Tianjin, China. Students who participated in the study completed a questionnaire that included social demographic questionnaire, Self-directed Learning Readiness Scale, Attitude to Learning Scale, and Social Problem-Solving Inventory. Pearson’s correlation analysis was performed to test the correlations among problem-solving ability, self-directed learning readiness, and learning attitude. Hierarchical linear regression analyses were performed to explore the mediating role of learning attitude. Results: The results showed that learning attitude (r=0.338, P<0.01) and self-directed learning readiness (r=0.493, P<0.01) were positively correlated with problem-solving ability. Learning attitude played a partial intermediary role between self-directed learning readiness and problem-solving ability (F=74.227, P<0.01). Conclusions: It is concluded that nursing educators should pay attention on students’ individual differences and take proper actions to inspire students’ self-directed learning readiness and learning attitude.

Self-directed learning readiness and social problem solving of nursing students in Macao

Objective The aims of this study were to describe nursing students′self-directed learning readiness and social problem solving and test their correlations in Macao.Methods This descriptive cross-sectional study was conducted on 140baccalaureate nursing students.A stratified random sampling was performed.The Self-directed Learning Readiness(SDLR)Scale and Chinese Social Problem-Solving Inventory-Revised(C-SPSI-R)were used.Results The response rate was 79.3%.Students possessed readiness for self-directed learning(mean 149.09±12.53,51.4%at high level,48.6%at low level).Regarding to social problem solving,the mean scores of each subscale were 9.35±3.25(Rational Problem Solving,RPS),10.26±3.23(Positive Problem Orientation,PPO),8.14±4.06(Negative Problem Orientation,NPO),5.67±4.44(Avoidance Style,AS),and 4.84±3.03(Impulsivity/Carelessness Style,ICS).SDLR was positively related to RPS and PPO,but was negatively related to AS.Conclusion Half of students possessed stronger readiness for self-directed learning.Students had a belief in the ability to solve problems,and adopted relevant strategies in solving problems.However,students still had negative and dysfunctional orientation and defective attempts in solving problems.Self-directed learning was positively related to positive and constructive orientation,but was negatively related to defective problem-solving pattern.Nurse educators should create educational climates for promoting student confidence and mutual responsibility for learning and their thinking process for problem solving.

Nursing Students’ Readiness for Self-Directed Learning and Its Effect on Learning Outcome in South-West Nigeria

Self-directed learning (SDL) uses diverse learning resources to solve identified problems in learning. Nursing is a lifelong learning profession and SDL is a valuable skill to remain relevant and productive professionals. Nursing students are expected to embrace SDL and develop these skills. However, there has been no evidence of this innovative process in South-West Nigeria. This study seeks to evaluate nursing students’ readiness for SDL and its effect on learning outcome. This quasi-experimental study purposively utilized 229 nursing students as participants. Baseline (P1) data was collected using Gugliemino’s SDL readiness scale (SDLRS) and a validated-structured questionnaire. Participants had a pre-test to assess knowledge at P1 followed by 6 weeks interaction using SDL on selected topics in Medical-surgical nursing and the same test at post-intervention (P2). Using a 50-point scale, knowledge was categorized as good ≥ 25 and poor < 25 and SDLRS on a 290-point scale was categorized as below average 5 - 201, average 202 - 226 and above average 227 - 290. Descriptive statistics, Chi-square test, t-test and linear regression analysis were used for analysis at p = 0.05. Nursing students’ SDLRS was average;mean = 203 ± 23.0. A significant difference exists between nursing students with good knowledge at P1 and P2. At P1, 39.2% had good knowledge, mean = 22.2 ± 6.3, and 90.1% at P2, mean = 30.6 ± 5.4, p < 0.05 also a significant relationship exist between SDLR and learning outcome at P2;p < 0.05. With the nursing students’ average SDL readiness level having a significant effect on learning outcome. Nursing training institutions should provide necessary resources to embrace SDL as a main-line teaching method to ensure competent life-long professionals.

Role of academic self-efficacy in the relationship between self-directed learning readiness and problem-solving ability among nursing students

Objective: Problem-solving should be a fundamental component of nursing education because It is a core ability for professional nurses. For more effective learning, nursing students must understand the relationship between self-directed learning readiness and problem-solving ability. The aim of this study was to investigate the relationships among self-directed learning readiness, problemsolving ability, and academic self-efficacy among undergraduate nursing students.Methods: From November to December 2016, research was conducted among 500 nursing undergraduate students in Tianjin, China,using a self-directed learning readiness scale, an academic self-efficacy scale, a questionnaire related to problem-solving, and selfdesigned demographics. The response rate was 85.8%.Results: For Chinese nursing students, self-directed learning readiness and academic self-efficacy reached a medium-to-high level,while problem-solving abilities were at a low level. There were significant positive correlations among the students' self-directed learning readiness, academic self-efficacy, and problem-solving ability. Furthermore, academic self-efficacy demonstrated a mediating effect on the relationship between the students' self-directed learning readiness and problem-solving ability.Conclusions: To enhance students' problem-solving ability, nursing educators should pay more attention to the positive impact of self-directed learning readiness and self-efficacy in nursing students' education.

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.

On establishment of novel constitutive model for directionally solidified nickel-based superalloys utilizing machine learning methods

To enhance the accuracy of mechanical simulation in the directional solidification process of turbine blades for heavy-duty gas turbines,a new constitutive model that employs machine learning methods was developed.This model incorporates incremental learning and transfer learning,thus improves the predictive accuracy and generalization performance.To account for the anisotropy of the directionally solidified alloy,a deformation direction parameter is added to the model,enabling prediction of the stress-strain relationship of the alloy under different deformation directions.The predictive capabilities of both models are evaluated using correlation coefficient(R),average relative error(δ),and value of relative error(RE).Compared to the traditional model,the machine learning constitutive model achieves higher prediction accuracy and better generalization performance.This offers a new approach for the establishment of flow constitutive models for other directionally solidified and single-crystal superalloys.

A Graph Theory Based Self-Learning Honeypot to Detect Persistent Threats

Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they fails.These APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses.Hence,there is a need for an effective defense system that can achieve a complete reliance of security.To address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats.The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks.The proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and modelling.The proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.

Sample-Efficient Deep Reinforcement Learning with Directed Associative Graph

Reinforcement learning can be modeled as markov decision process mathematically.In consequence,the interaction samples as well as the connection relation between them are two main types of information for learning.However,most of recent works on deep reinforcement learning treat samples independently either in their own episode or between episodes.In this paper,in order to utilize more sample information,we propose another learning system based on directed associative graph(DAG).The DAG is built on all trajectories in real time,which includes the whole connection relation of all samples among all episodes.Through planning with directed edges on DAG,we offer another perspective to estimate stateaction pair,especially for the unknowns to deep neural network(DNN)as well as episodic memory(EM).Mixed loss function is generated by the three learning systems(DNN,EM and DAG)to improve the efficiency of the parameter update in the proposed algorithm.We show that our algorithm is significantly better than the state-of-the-art algorithm in performance and sample efficiency on testing environments.Furthermore,the convergence of our algorithm is proved in the appendix and its long-term performance as well as the effects of DAG are verified.

Distributed Cooperative Learning for Discrete-Time Strict-Feedback Multi Agent Systems Over Directed Graphs

This paper focuses on the distributed cooperative learning(DCL)problem for a class of discrete-time strict-feedback multi-agent systems under directed graphs.Compared with the previous DCL works based on undirected graphs,two main challenges lie in that the Laplacian matrix of directed graphs is nonsymmetric,and the derived weight error systems exist n-step delays.Two novel lemmas are developed in this paper to show the exponential convergence for two kinds of linear time-varying(LTV)systems with different phenomena including the nonsymmetric Laplacian matrix and time delays.Subsequently,an adaptive neural network(NN)control scheme is proposed by establishing a directed communication graph along with n-step delays weight updating law.Then,by using two novel lemmas on the extended exponential convergence of LTV systems,estimated NN weights of all agents are verified to exponentially converge to small neighbourhoods of their common optimal values if directed communication graphs are strongly connected and balanced.The stored NN weights are reused to structure learning controllers for the improved control performance of similar control tasks by the“mod”function and proper time series.A simulation comparison is shown to demonstrate the validity of the proposed DCL method.

离散四水库问题基准下基于n步Q-learning的水库群优化调度

水库优化调度问题是一个具有马尔可夫性的优化问题。强化学习是目前解决马尔可夫决策过程问题的研究热点,其在解决单个水库优化调度问题上表现优异,但水库群系统的复杂性为强化学习的应用带来困难。针对复杂的水库群优化调度问题,提出一种离散四水库问题基准下基于n步Q-learning的水库群优化调度方法。该算法基于n步Q-learning算法,对离散四水库问题基准构建一种水库群优化调度的强化学习模型,通过探索经验优化,最终生成水库群最优调度方案。试验分析结果表明,当有足够的探索经验进行学习时,结合惩罚函数的一步Q-learning算法能够达到理论上的最优解。用可行方向法取代惩罚函数实现约束,依据离散四水库问题基准约束建立时刻可行状态表和时刻状态可选动作哈希表,有效的对状态动作空间进行降维,使算法大幅度缩短优化时间。不同的探索策略决定探索经验的有效性,从而决定优化效率,尤其对于复杂的水库群优化调度问题,提出了一种改进的ε-greedy策略,并与传统的ε-greedy、置信区间上限UCB、Boltzmann探索三种策略进行对比,验证了其有效性,在其基础上引入n步回报改进为n步Q-learning,确定合适的n步和学习率等超参数,进一步改进算法优化效率。

The Effectiveness of Self-regulated Learning Strategies on Chinese College Students' English Learning

The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated learning can help improve Chinese college students' English learning, and help them perform better in the National English test-CET-4 (College English Test Level-4,).

Research on Application of Metacognitive Strategy in English Listening in the Web-based Self-access Learning Environment

Metacognitive strategies are regarded as advanced strategies in all the learning strategies.This study focuses on the application of metacognitive strategies in English listening in the web-based self-access learning environment(WSLE) and tries to provide some references for those students and teachers in the vocational colleges.

An Experimental Study on the Development of Self-access Language Learning by Non-English Majors

Self-access language learning has attracted much attention in second language teaching and researching. This paper aims to do some researches on developing self-access language learning in the self-access center and test its effect on developing learner autonomy. Data,collected in the form of questionnaires and interview,were analyzed. Results show the development of self-access language learning by non-English majors.

Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.

Understanding the effects of structured self-assessment in directed,self-regulated simulation-based training of mastoidectomy:A mixed methods study

Objective:Self-directed training represents a challenge in simulation-based training as low cognitive effort can occur when learners overrate their own level of performance.This study aims to explore the mechanisms underlying the positive effects of a structured self-assessment intervention during simulation-based training of mastoidectomy.Methods:A prospective,educational cohort study of a novice training program consisting of directed,self-regulated learning with distributed practice(5x3 procedures)in a virtual reality temporal bone simulator.The intervention consisted of structured self-assessment after each procedure using a rating form supported by small videos.Semi-structured telephone interviews upon completion of training were conducted with 13 out of 15 participants.Interviews were analysed using directed content analysis and triangulated with quantitative data on secondary task reaction time for cognitive load estimation and participants’self-assessment scores.Results:Six major themes were identified in the interviews:goal-directed behaviour,use of learning supports for scaffolding of the training,cognitive engagement,motivation from self-assessment,selfassessment bias,and feedback on self-assessment(validation).Participants seemed to self-regulate their learning by forming individual sub-goals and strategies within the overall goal of the procedure.They scaffolded their learning through the available learning supports.Finally,structured self-assessment was reported to increase the participants’cognitive engagement,which was further supported by a quantitative increase in cognitive load.Conclusions:Structured self-assessment in simulation-based surgical training of mastoidectomy seems to promote cognitive engagement and motivation in the learning task and to facilitate self-regulated learning.