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In the field of aerospace, real-time and accurate monitoring of the milling state is of great significance for improving processing quality and processing efficiency. Relying on milling force signals to monitor tool wear state has problems such as high cost and limited size of machined parts. Aiming at the above problem, a milling cutter wear condition monitoring method based on spindle current is proposed. Firstly, based on the linear correlation between the milling force and the current increment theory, the milling experiment was carried out. The characteristic variation trend and spectral characteristics of the milling force signal and spindle current signal under the change of spindle speed, feed rate, and radial cutting width are analyzed. The cosine similarity demonstrates that the similarity of the root mean square values of the two is 0.9865, 0.9943, and 0.9421, respectively, and the changing trend of the spectral amplitude is consistent. In the iSESOL platform, a self-learning feature boundary model is built from learning data, signal interception, root mean square feature extraction, drawing boundary, and threshold judgment to monitor the wear state of the milling cutter online. Finally, the data in each wear stage are selected to verify the model, and the validity of the model is proved by setting the threshold beyond the boundary and the alarm trigger condition, in real-time, and accurately monitoring the wear state of the milling cutter online. [ABSTRACT FROM AUTHOR]

This study explores the affordance of Artificial Intelligence (AI) to English language learning and teaching, focusing on its alignment with the United Nations' Sustainable Development Goals (SDGs). It aims to investigate the role of AI in enhancing language education and fostering student-centered learning. Data for this study were collected through semi-structured interviews with 18 English teachers to gather qualitative insights into their experiences with AI-powered language learning tools. The findings reveal that the teachers have positive appraisals of AI that its use has six major impacts: i) enhancing the personalization of learning; ii) contributing to improved learning outcomes by advancing students' speaking, listening, reading, and writing skills; iii) playing a fundamental role in bridging educational gaps; iv) enhancing students' engagement and motivation; v) empowering educators with professional development opportunities; vi) and encouraging self-directed learning. This study argues that, if implemented thoughtfully, AI can enhance language learning outcomes and create an environment conducive to student engagement and success. [ABSTRACT FROM AUTHOR]

In order to ensure that students have a deeper and more thorough understanding of knowledge and skills, the author takes deep learning as the direction and integrates it into the real-time evaluation of basketball classrooms, organically combining the two. Deep learning provides a new theoretical perspective and driving force for the optimization of real-time evaluation, and points out the direction for promoting evaluation effectiveness; The optimization of real-time evaluation provides a feasible path for achieving deep learning for students. After the implementation of the real-time evaluation plan between the experimental group and the control group, the comparison results of basketball skills tests were obtained: before and after the experiment, the experimental group's 60 second self throwing and self grabbing, vigorous dribbling, comprehensive dribbling, teaching competitions, and teaching lectures all showed significant differences in test indicators; Before and after the experiment, the control group showed significant differences in the four test indicators of 60 second self throwing and self grabbing, vigorous dribbling, comprehensive dribbling, and teaching lectures. The teaching competition indicators also showed significant differences, all of which were improved. Comparison results of deep learning abilities: Before and after the experiment, there were significant differences in the dimensions of experimental combination ability and learning perseverance, while there were significant differences in the dimensions of communication ability, self-learning ability, and total score of deep learning ability; Before and after the experiment, there was a significant difference in the dimensions of control group work ability, while there was no significant difference in the dimensions of self-directed learning ability, learning perseverance, and communication ability. Overall, there was a very significant difference in deep learning ability. Overall, the experimental group showed a more significant improvement in deep learning ability compared to the control group. The real-time evaluation plan for sports education professional basketball classrooms based on deep learning is more effective in improving students' basketball skills and deep learning abilities than the conventional real-time evaluation plan for sports education professional basketball classrooms. The design of the plan has certain effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to examine the mechanisms influencing metacognitive awareness among nursing students, encompassing educational environment, learning motivation, emotional intelligence, and self-directed learning ability. These findings could be a theoretical reference for formulating metacognitive awareness intervention strategies specifically designed for nursing students. Method: Through stratified sampling, 600 nursing students were recruited from two universities in China. Data collection involved five self-report instruments: the Metacognitive Awareness Inventory, Medical Education Environment Evaluation Scale, Learning Motivation Scale, Emotional Intelligence Scale, and Self-Directed Learning Ability Scale. Confirmatory factor analysis was utilized to test the measurement model, while structural equation modeling utilized multiple indices to assess model fit. Bootstrapping was conducted to analyze total, direct, and indirect effects. Results: All study variables differed significantly according to age and year level (p < 0.01). Metacognitive awareness positively correlated with the educational environment, learning motivation, emotional intelligence, and self-directed learning ability (r = 0.493–0.729; all p < 0.01). The test of the measurement model shows good reliability and validity. The model depicting factors influencing metacognitive awareness demonstrated a good fit (χ2/df = 2.42, SRMR = 0.048, RMSEA = 0.049, CFI = 0.947, IFI = 0.935). Path analyses revealed that educational environment (EE), learning motivation (LM), and emotional intelligence (EI) directly impacted self-directed learning ability (SDLA) (βEE = 0.189, βLM = 0.436; βEI = 0.215; all p < 0.05). Moreover, educational environment and self-directed learning ability directly influenced metacognitive awareness (βEE = 0.224, βSDLA = 0.520; all p < 0.05). Lastly, the educational environment, learning motivation, and emotional intelligence influenced metacognitive awareness by self-directed learning ability. Conclusion: Metacognitive awareness in nursing students rises with age and year level. Females exhibit more potent self-directed learning abilities than males. Educational environment, motivation, and emotional intelligence indirectly influence metacognitive awareness via self-directed learning ability. This finding has enriched the ways and perspectives of metacognitive awareness training for nursing students. Highlights: This study profoundly explores the characteristics and interconnections between metacognitive awareness, self-directed learning ability, educational environment, emotional intelligence, and learning motivation in undergraduate nursing students in China. By examining the role of self-directed learning ability as a mediator, the research offers theoretical and practical insights that could guide the design of intervention strategies to enhance metacognitive awareness in nursing education. [ABSTRACT FROM AUTHOR]

Background: Traditionally, ultrasound skills have been taught through a one-on-one approach, where instructors physically guide learners' hands at the bedside or in the workshop. However, this method is frequently challenged by scheduling and cost limitations. Our objective was to create a tele-education model for point-of-care ultrasound training and evaluate its effectiveness and its impact on learners' perceived workload compared to conventional education and self-directed learning methods. Methods: We conducted a 3-arm randomized trial, comparing tele-education (TE), conventional education (CE) and self-directed learning (SL) methods. All subjects underwent online didactic lectures prior to a hands-on ultrasound workshop. The TE group utilized an ultrasound machine equipped with a speakerphone, a webcam for direct visualization of learner's hand maneuvers, and an analog-to-video converter for the real-time streaming of ultrasound images. This configuration enabled remote instructors to provide immediate verbal feedback to learners. In contrast, the CE group received in-person coaching, while the SL group had no instructors present. Following the coaching session, subjects completed a scenario-based skill test and a survey on the National Aeronautics and Space Administration task load index (NASA-TLX) to measure their ultrasound competency and perceived workload, respectively. Results: Twenty-seven ultrasound novices were randomly allocated into 3 groups. The median skill test score of TE, CE, and SL was 22 [interquartile range (IQR): 18–28], 24 [IQR: 21–31], and 16 [IQR: 15–18], respectively (p < 0.01). Pairwise comparisons of median test scores of 3 groups demonstrated a statistical significance in comparisons of TE vs. SL (22 vs. 16, p = 0.01) and CE vs. SL (24 vs. 16, p < 0.01), but not in TE vs. CE (22 vs. 24, p = 0.56). There was no statistical significance observed in the median NASA-TLX scores among the 3 groups; 54 [IQR:47–61] in TE, 57 [IQR:22–64] in CE, and 66 [IQR: 66–72] in SL (p = 0.05). Conclusions: Our tele-education model was more effective than self-directed learning. There was no statistically significant difference in effectiveness between the tele-education and the conventional education groups. Importantly, tele-education did not impose a significantly higher workload on learners compared to conventional education or self-directed learning. Tele-education has a substantial potential as an alternative to conventional ultrasound training. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) uses in healthcare changed dramatically with large language models. While work is underway to integrate generative AI safely and effectively into medical practice, little is known about how resident physicians are already using these tools in their work. Objectives: To characterize the current usage patterns and attitudes towards large language models among internal medicine residents in North Carolina. Methods: Cross-sectional survey-based study of Internal medicine residents in North Carolina. Results: 152 of 523 total residents completed the survey (29% response rate). Many identified a role for LLMs in both clinical (56%) and non-clinical medicine (72%) today, although only 26% reported current use. More perceived a future role in clinical (71%) and non-clinical (80%) medicine. None had received formal training, but 94% expressed interest in learning more and 87% thought residencies maybe or definitely should have formal curriculum. Top clinical uses include forming a differential diagnosis, researching treatments, and creating educational materials. Top non-clinical uses included self-directed learning, scientific writing, and summarizing literature. Conclusions: Medical educators and administrators should be aware that residents are using generative AI in professional settings. Residents believe in a current and future role for AI in medicine and desire curriculum and learning opportunities during training. [ABSTRACT FROM AUTHOR]

Introduction: The ability to self-assess is a crucial skill in identifying one's own strengths and weaknesses and in coordinating self-directed learning. The Dunning-Kruger effect occurs when limited knowledge causes individuals to overestimate their competence and underestimate others', leading to poor self-assessment and unrecognized incompetence. To serve as a foundation for developing strategies to improve self-assessment, the self-assessment abilities of first-semester students were assessed. Methods: In the final weeks of the summer 2021, winter 2021/22, and summer 2022 semesters, the academic performance (oral anatomy exam) of first semester students was assessed (0–15 points). Before the exam results were announced, students were asked to self-assess their performance. Results: Exam scores (M = 10.64, SD = 2.95) and self-assessed scores (M = 10.38, SD = 2.54) were comparable. The absolute difference between them, as a measure of self-assessment ability ranged from − 9 to + 9 points (M = -0.26, SD = 2.59). Among participants (N = 426), 18.5% assessed themselves accurately, 35.5% overestimated, and 46.0% underestimated their performance. The correlation between actual score and self-assessment was ρ = -0.590 (p < 0.001), reflecting the Dunning-Kruger effect. When separated by gender, correlation for females was ρ = -0.591 (p < 0.001), and for males ρ = -0.580 (p < 0.001). Conclusions: Realistic self-assessment is a challenge for first-semester students. The data indicate that females tend to overestimate their performance while males underestimate theirs. A pronounced Dunning-Kruger effect is evident in both genders, with significant negative correlations between self-assessment and actual performance. There are several reasons for the occurrence of the Dunning-Kruger effect. Considering that the COVID-19 pandemic influenced learning environments, collaborative learning was significantly restricted. The lack of opportunities for comparison could potentially lead to unrealistic self-assessment. [ABSTRACT FROM AUTHOR]

Echolocation is a vital method of spatial orientation for many visually impaired individuals who are willing to and able to learn it. Blind echolocators use a variety of sounds, such as mouth clicks, cane taps, or specialized sound-emitting devices, to perceive their surroundings. In our study, we examined the effectiveness of several different sounds used in echolocation by conducting trials with 12 blind and 14 sighted volunteers. None of the participants had received formal training in echolocation, though a number identified as self-taught experts. The sounds tested included those played from a loudspeaker, generated by a mechanical clicker, or made by the participants themselves. The task given to the participants was to identify the direction and distance to an obstacle measuring 1x2 meters in an outdoor environment, with the obstacle placed in one of nine possible positions. Our findings indicated that the blind participants displayed significantly better echolocation skills when compared to the sighted participants. The results of the blind participants were also strongly divided into two distinct subgroups—totally blind participants performed much better than those which were legally blind, but had some residual vision. In terms of sound comparisons, we found that sounds with a center frequency near 3-4kHz and a wide spectrum provided higher accuracy rates than those with lower frequency peaks. Sighted participants performed best with 3kHz and 4kHz percussion sounds, while the blind group performed best with blue and pink noise. The loudspeaker generated tones generally yielded better results than those generated by the participant (using a mechanical clicker, mouth clicks or hand claps). These results may be useful in developing training programs that teach echolocation as well as artificial sounds to improve echolocation effectiveness. [ABSTRACT FROM AUTHOR]

Background: Small group teaching is one of the components of competency-based medical education which was implemented in 2019 by NMC. Seminar is one of the methods of small group teaching which increases the engagement of students, self-directed learning, communication skills, and peer discussion. Aims and Objectives: The present idea is to study the effect of students' seminar as a revision strategy in Biochemistry among I MBBS students. Materials and Methods: After obtaining the IEC approval, I MBBS students who are 250 in number will be divided into small groups. Each group will be given a particular seminar topic. All the students in the particular group will be instructed to present the topic on random basis. A total of 15 seminars will be conducted on particular topics. Pre-tests and post-tests (immediately after the seminar) will be conducted after the seminars along with pre-test and post-test questionnaires will be obtained and collated to understand how far the student's acceptance of students' seminar and its effectiveness. Results: Our study results showed that that there is an increase in the percentage of marks in the post-evaluation and also more than 70% of the students responded positively to most of the queries in the feedback questionnaire. Conclusion: Our study results showed that there is an increase in the percentage of marks in the post-evaluation and also more than 70% of the students responded positively to most of the queries in the feedback questionnaire. [ABSTRACT FROM AUTHOR]

The automatic video recognition of depression is becoming increasingly important in clinical applications. However, traditional depression recognition models still face challenges in practical applications, such as high computational costs, the poor application effectiveness of facial movement features, and spatial feature degradation due to model stitching. To overcome these challenges, this work proposes a lightweight Time-Context Enhanced Depression Detection Network (TCEDN). We first use attention-weighted blocks to aggregate and enhance video frame-level features, easing the model's computational workload. Next, by integrating the temporal and spatial changes of video raw features and facial movement features in a self-learning weight manner, we enhance the precision of depression detection. Finally, a fusion network of 3-Dimensional Convolutional Neural Network (3D-CNN) and Convolutional Long Short-Term Memory Network (ConvLSTM) is constructed to minimize spatial feature loss by avoiding feature flattening and to achieve depression score prediction. Tests on the AVEC2013 and AVEC2014 datasets reveal that our approach yields results on par with state-of-the-art techniques for detecting depression using video analysis. Additionally, our method has significantly lower computational complexity than mainstream methods. [ABSTRACT FROM AUTHOR]

Nowadays, with the common use of smartphones in daily lives, mobile applications have become popular around the world, which will lead to a rise in Flutter framework. Developed by Google, Flutter with Dart programming provides a cross-platform development environment to create visually appealing and responsive user interfaces across mobile, web, and desktop platforms using a single codebase. However, due to time and staff limitations, the Flutter/Dart programming course is not included in curricula, even in IT departments in universities. Therefore, independent learning environments for students are essential to meet this growing popularity. Previously, we have developed programming learning assistant system (PLAS) as a web-browser-based self-learning platform for novice students. PLAS offers various types of exercise problems designed to cultivate programming skills step-by-step through a lot of code reading and code writing practices. Among them, one particular type is the code modification problem (CMP), which asks to modify the given source code to satisfy the new specifications. CMP is expected to be solved by novices with little effort if they have knowledge of other programming languages. Thus, PLAS with CMP will be an excellent platform for independent learning. In this paper, we present PLAS with CMP for the independent learning of Flutter/Dart programming. To improve the readability of the source code by students, we provided rich comments on grammar or behaviors. Besides, the code can be downloaded so that students can check and run it on an IDE. For evaluations, we generated 38 CMP instances for basic and multimedia/storage topics in Flutter/Dart programming and assigned them to 21 master students at Okayama University, Japan, who have never studied it. The results confirm the validity of the proposal. [ABSTRACT FROM AUTHOR]

The vehicle-routing problem (VRP) is a popular area of research. This popularity springs from its wide application in many real-world problems, such as logistics, network routing, E-commerce, and various other fields. The VRP is simple to formulate, but very difficult to solve and requires a great deal of time. In these cases, researchers use approximate solutions offered by metaheuristics. This work involved the design of a new metaheuristic called Open Competency Optimization (OCO), which was inspired by human behavior during the learning process and based on the competency approach. The aim is the construction of solutions that represent learners' ideas in the context of an open problem. The candidate solutions in OCO evolve over three steps. Concerning the first step, each learner builds a path of learning (finding the solution to the problem) through self-learning, which depends on their abilities. In the second step, each learner responds positively to the best ideas in their group (the construction of each group is based on the competency of the learners or the neighbor principle). In the last step, the learners interact with the best one in the group and with the leader. For the sake of proving the relevance of the proposed algorithm, OCO was tested in dynamic vehicle-routing problems along with the Generalized Dynamic Benchmark Generator (GDBG). [ABSTRACT FROM AUTHOR]

The article discusses the importance of incorporating informal learning experiences into science and mathematics education. Informal learning experiences, which occur outside of traditional classrooms, can make these subjects more engaging and relevant to students' lives. Examples of informal learning experiences include interactive exhibits at museums and science centers, after-school STEM programs, everyday activities at home, and community-based learning. These experiences can increase student engagement, provide real-world relevance to abstract concepts, develop critical thinking and problem-solving skills, and support personalized learning. However, challenges such as unequal access to resources and the need for teacher training must be addressed to effectively integrate informal learning into formal education. Overall, integrating informal learning experiences can create a more comprehensive and effective educational experience for students, preparing them for lifelong learning and success in a rapidly changing world. [Extracted from the article]

Students benefit from a nonlinear learning path, also known as a nonsequential learning path, because it allows them to control the pace and sequence of their learning. However, the dynamics of a nonlinear learning path, particularly within an open learning environment like MOOCs, remain underexplored. The current study aims to map out various nonlinear learning paths and explore the potential for personalisation within these environments. Guided by the PRISMA 2020 Statement, we conducted a comprehensive review of 3,418 articles from three databases, focusing on 30 that were relevant to nonlinear learning paths in open learning environments. We discovered that a nonlinear learning path in MOOCs involves path selection, cyclical paths, or skipped paths, all influenced by the design of the MOOC learning materials. In classrooms utilizing an open learning environment, a nonlinear learning path is facilitated by activities such as face-toface or online discussions, self-study materials, student-created content, project mentoring or coaching, peer feedback, and co-learning activities. Additionally, personalisation, tailored by educators or technology, is key to preventing students from becoming disoriented within these open learning environments. Our findings highlight the importance of promoting nonlinear learning paths in both classrooms and MOOCs, developing learning path recommender systems, and creating supportive MOOC learning materials. Future research should explore students' perceptions of nonlinear learning paths within an open learning environment, particularly focusing on the integration and impact of MOOC learning materials. [ABSTRACT FROM AUTHOR]

This study explores how podcasts' accessibility fosters learning, examining listener engagement, content preferences, and the unique educational opportunities they offer. With the rise of digital audio platforms, podcasts have emerged as a significant medium for acquiring knowledge outside formal educational settings. Using an online survey, data were collected from 605 respondents, who were predominantly young and well educated, to understand their podcast listening habits and the extent of informal learning. The study categorizes informal learning into self-directed, incidental, and tacit learning. Results show that more than half of respondents engage in self-directed learning, while a third participate in incidental learning, indicating that podcasts serve as effective tools for intentional and unintentional knowledge acquisition. ANOVA analysis reveals that self-directed learners exhibit higher information retention and critical evaluation skills compared to tacit learners. Additionally, trust in podcast content is significantly higher among educated listeners. The study concludes that while podcasts are valuable for learning and professional development, there is a need for enhanced digital literacy to improve critical appraisal skills among listeners. These findings provide insights into the cognitive processes involved in podcast-based learning and suggest practical strategies for educators and content creators to enhance the educational impact of podcasts. [ABSTRACT FROM AUTHOR]

This study investigates the impact of active learning on the acquisition of competencies and learning outcomes in the context of organic chemistry education. Specifically, this study focuses on the implementation of active learning in the extraction of an unknown mixture of organic compounds using acidic and basic solutions. This research is based on an "ex post facto" study involving 40 first-year undergraduate students who are pre-service chemistry teachers at a Slovak public university. This study aims to analyse students' performance, identify common problems encountered, and assess the advantages and disadvantages of the active learning approach. The data collection instruments included a structured report on best practices in university education and a questionnaire to evaluate the experiences and assessment systems used. This study compares the effectiveness of online and face-to-face teaching methods for practical chemistry coursework. The key findings from the comparison of these methods are the differences in learning outcomes achieved, e.g., answers to tasks 2–6 from the questionnaire. Group B respondents had a higher number of correct responses and lower variability compared to Group A respondents. This difference may indicate an improvement in comprehension and effectiveness of instruction over the period. Differences in scores between the groups may be due to random variability in the composition of the groups, which we found through statistical analysis. Full-time students felt more engaged and more satisfied. More than half of the students said that they preferred face-to-face interactions to help them better understand the material. While online instruction provided greater flexibility and accessibility, students felt that they lacked hands-on interaction, which negatively impacted their acquisition of practical skills. The results indicate that active learning, particularly hands-on laboratory exercises, had a positive impact on the acquisition of professional competencies and students' learning outcomes. This study also highlights the advantages of active learning in practical chemistry education. [ABSTRACT FROM AUTHOR]

The technological revolution has transformed the area of labor with reference to automation and robotization in various domains. The employment of robots automates these disciplines, rendering beneficial impacts as robots are cost-effective, reliable, accurate, productive, flexible, and safe. Usually, single robots are deployed to accomplish specific tasks. The purpose of this study is to focus on the next step in robot research, collaborative multi-robot systems, through flocking control in particular, improving their self-adaptive and self-learning abilities. This review is conducted to gain extensive knowledge related to swarming, or cluster flocking. The evolution of flocking laws from inception is delineated, swarming/cluster flocking is conceptualized, and the flocking phenomenon in multi-robots is evaluated. The taxonomy of flocking control based on different schemes, structures, and strategies is presented. Flocking control based on traditional and trending approaches, as well as hybrid control paradigms, is observed to elevate the robustness and performance of multi-robot systems for collective motion. Opportunities for deploying robots with flocking control in various domains are also discussed. Some challenges are also explored, requiring future considerations. Finally, the flocking problem is defined and an abstraction of flocking control-based multiple UAVs is presented by leveraging the potentials of various methods. The significance of this review is to inspire academics and practitioners to adopt multi-robot systems with flocking control for swiftly performing tasks and saving energy. [ABSTRACT FROM AUTHOR]

Presently, numerous universities in Saudi Arabia have embraced online learning following the experience gained during the COVID-19 pandemic. While several studies have assessed the quality of online learning and student satisfaction during that period, limited research has explored students' satisfaction post-pandemic control despite several universities planning to offer some courses online. Investigating student satisfaction post-pandemic is crucial for predicting the quality of online learning and assisting universities in enhancing the outcomes of online learning. Consequently, this study aims to examine student satisfaction with online learning by proposing a model derived from the updated Information System Success Model. The updated model factors include system quality, service quality, and information quality, supplemented by student–student interaction quality and self-directed learning. Data were collected from a sample of 150 undergraduates at King Saud University in the second semester of the 2023–2024 academic year. The research model was validated using the PLS approach. The findings indicated that only self-directed learning significantly affects students' satisfaction with online learning. This study contributes theoretically by expanding the existing literature and enhancing the understanding of the factors that drive student satisfaction. Moreover, it provides practical contributions to decision-makers and educators developing online learning strategies focusing on enhancing self-directed learning abilities. [ABSTRACT FROM AUTHOR]

As has been said on many occasions, students’ interest in specific topics is among the factors that determine autonomous and self-directed learning. This paper shows the results obtained from the experience of 60 accounting students from a Peruvian technological institute who recorded videos on topics of their interest. Mixed methods research was carried out. The quantitative stage consisted of two parts. The first one measured the difference between the pre-and post-tests, before and after the project. Second, they proposed a validated model, considering three constructs: oral skills, language difficulties, and video types; for this reason, a confirmatory factor analysis (CFA) was applied from a survey completed by students at the end of the project. The qualitative stage consisted of coding the responses given by students to the survey's open-ended questions. This research highlighted the improvement of oral skills and an acceptable factor structure (RMSEA 0.058; CFI 0.965; and TLI 0.958) concerning students' opinions. The outcomes of this research are promising, with a significant enhancement in students' oral skills, increased confidence in public speaking, and a positive learning experience that can inspire and shape future educational practices. [ABSTRACT FROM AUTHOR]

Background: Previous studies have reported varying levels of self-directed learning readiness and time management skills among nursing students, and the relationship between these two skills has not been extensively explored. Consequently, this study aimed to assess self-directed learning readiness and its influencing factors, with a specific emphasis on the role of time management skills among nursing students. Methods: This cross-sectional study involved the participation of 110 undergraduate nursing students who were conveniently recruited for the research. The data collection tools included a demographic information form, Fisher's Self-Directed Learning Readiness questionnaire, and the Time Management Inventory developed by Britton and Tesser. The collected data were analyzed using STATA-14 statistical software. Results: The findings revealed that 96.4% of the nursing students demonstrated readiness for self-directed learning. The mean overall score for self-directed learning readiness was 162.3 ± 6.1 out of 200, indicating a relatively high level of readiness. The mean score for time management skills was 98.1 ± 5.0 out of 135, suggesting satisfactory proficiency in this area. In terms of the relationship between self-directed learning readiness and its associated factors, time management skills, academic probation history, grade point average, place of residence, and level of interest in the field of study collectively accounted for 9.2% of the variance in self-directed learning readiness. Among these factors, academic probation history, grade point average, and place of residence emerged as statistically significant predictors (P < 0.05). Conclusions: The study findings indicate that the level of self-directed learning readiness and time management skills among the nursing students were considered acceptable. Academic probation history, grade point average, and place of residence emerged as significant predictors of self-directed learning readiness. These results highlight the importance of considering students' academic background and living conditions to effectively enhance their level of self-directed learning readiness. Further research is recommended to explore additional factors that may influence self-directed learning readiness among nursing students. [ABSTRACT FROM AUTHOR]

Background: Lifelong learning is essential for cultivating conscientious nurses. Because self-directed learning is significantly correlated with lifelong learning, it is necessary to enhance nursing students' self-directed learning skills after graduation. This study examined the effectiveness of an integrated team-based learning and empathy mapping teaching strategy in a Psychiatric Nursing course for enhancing students' self-directed learning skills and academic performance. Methods: A quasi-experimental design was used. The subjects of the study were 89 third-year nursing students who enrolled in a course on Psychiatric Nursing. Data were collected from September 22, 2021 to January 31, 2022. The participants were recruited using purposive sampling and divided into experimental group and control group. The pre- and post-test learning outcomes of nursing students were evaluated with the Self-Directed Learning Instrument (SDLI). Results: The experimental group performed significantly better than the control group on the dimensions of "identifying learning resources," "monitoring learning progress," and "interpersonal communication," (p <.05) as revealed by the results. Additionally, the students enhanced their self-directed learning skills (p =.038) and academic performance (p <.001). Conclusions: The integrated team-based learning and empathy mapping teaching strategy significantly enhanced the self-directed learning skills and academic performance of nursing students. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic has compelled a transition to distance and online education, requiring instructors and students to adjust swiftly. During this transition period, it became crucial to prioritize comprehending students' perspectives and decision-making within virtual educational environments. The present study investigates the strategies a GPCOM (Purposive Communication) instructor employs at a premier university to enhance student autonomy and decision-making through student voice and choice in the online learning environment. Data were collected via a survey comprising Likert scale items and open-ended questions from the 335 participants who participated in the study. The study results indicate that educators significantly facilitate student engagement in expressing their thoughts and ideas, promoting self-discovery, critical thinking, and collaborative problem-solving. Student preferences noted that active learning, utilization of technology, and engagement in class discussions were prominent options. Several strategies were employed to promote student engagement, such as fostering cooperation, implementing interactive gamification techniques, establishing real-life linkages, facilitating class debates, and encouraging self-directed learning. This study emphasizes the crucial role of instructor assistance in virtual learning while recognizing and integrating student preferences. Doing so aims to create a well-rounded and captivating virtual learning experience. [ABSTRACT FROM AUTHOR]

Background: Psychological capital has become a prominent focus in positive psychology, highlighting the positive influence of higher psychological capital on individuals. Self-directed learning ability is a fundamental skill for students, vital for enhancing academic performance and professional development, and is integral to the continuous learning process of nursing students. Recognizing the relationship between psychological capital and self-directed learning ability is crucial for the progress and development of undergraduate nursing students. Objective: This study aims to investigate the correlation between psychological capital and self-directed learning ability in undergraduate nursing students, as well as to explore the factors that influence these variables. Methods: A cross-sectional survey was conducted with 667 full-time undergraduate nursing students from a nursing school in Taizhou, China. Psychological capital and self-directed learning ability were assessed using the Psychological Capital Questionnaire and Self-Directed Learning Scale, respectively. Correlation and stepwise multiple regression analyses were then carried out to evaluate the relationship between psychological capital and selfdirected learning ability among the participants. Results: The study revealed that the psychological capital score averaged at 103.24 ± 15.51, while the self-directed learning scale score averaged at 230.67 ± 27.66. Variations in psychological capital scores were noted based on factors including grade level, being an only child, growth environment, monthly living expenses, parental education level, voluntary selection of nursing major, and club experience. Similarly, differences in self-directed learning scores were associated with factors such as grade level, gender, parental education level, and voluntary selection of nursing major. Moreover, a positive correlation was identified between the overall psychological capital scores and the total selfdirected learning ability scores among nursing students. Notably, the multiple regression analysis highlighted that optimism and resilience played significant roles as predictors of self-directed learning ability. Conclusion: Psychological capital is positively correlated with the self-directed learning ability of nursing students, with optimism and resilience identified as crucial predictors. Nursing educators can utilize strategies rooted in positive psychology and perseverance to improve the self-directed learning ability of nursing students. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders—such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury—by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot. [ABSTRACT FROM AUTHOR]

In this work, we enhance the FNet , a 1D convolutional neural network (CNN) with a residual neural network (ResNet) architecture, to perform spectral classification of quasars, galaxies, stars, and broad absorption line (BAL)-quasars in the SDSS-IV catalogue from DR17 of eBOSS. Leveraging its convolutional layers and the ResNet structure with different kernel sizes, FNet autonomously identifies various patterns within the entire sample of spectra. Since FNet does not require the intermediate step of identifying specific lines, a simple modification enabled our current network to classify all SDSS spectra. This modification involves changing the final output layer from a single value (redshift) to multiple values (probabilities of all classes), and accordingly adjusting the loss function from mean squared error to cross-entropy. FNet achieves a completeness of 99.00  per cent |$\pm$| 0.20 for galaxies, 98.50  per cent |$\pm$| 0.30 for quasars, 99.00  per cent |$\pm$| 0.18 for BAL-quasars, and 98.80 per cent |$\pm$| 0.20 for stars. These results are comparable to those obtained using QuasarNET , a standard CNN employed in the SDSS routine, comprises convolutional layers without the ResNet structure with equal kernel sizes, and is utilized for redshift measurement and classification by identifying seven emission lines. QuasarNET , in order to overcome the problem of finding a C  iv emission line with broad absorption which is slightly more challenging than that of detecting emission lines requires to add BAL C  iv line to the list of lines that the network learns to identify. However, this procedure is not necessary in FNet as it learns the features through a self-learning procedure. [ABSTRACT FROM AUTHOR]

This study examines the impact of digital learning platforms--Moodle, Microsoft Teams, and Zoom--on student engagement and academic achievement at the University of Jordan. The study explores students' familiarity with these e-learning tools and their effect on self-directed learning and performance. Over three months, data was collected from 450 students through an online questionnaire comprising closed questions. The analysis employed multiple regression models to consider variables such as gender, age, prior computer literacy, attitude towards emerging technology, learning preferences, and the implementation of e-learning within the university. Additionally, a qualitative content analysis identified the advantages and disadvantages of e-learning from the students' perspectives. The findings indicate that strategic implementation of e-learning platforms significantly influences student perceptions more than individual contextual factors. Students appreciated the connectivity and accessibility provided by these platforms. Those with prior computer knowledge and those studying emerging technologies showed a particularly positive attitude towards e-learning. The study concludes that engagement with e-learning tools markedly enhances self-study habits and academic performance, underscoring the importance of integrating digital platforms into educational strategies. [ABSTRACT FROM AUTHOR]

This study investigates the intricate causal mechanisms of university students' sustained use of artificial intelligence (AI) tools for self-directed learning (SDL) within the theoretical framework of self-determination theory (SDT). Employing a convenience sampling strategy, 387 university students from China were included in the study. Methodologically, we employed structural equation modeling (SEM) for the measurement and causal analysis, and we employed fuzzy-set qualitative comparative analysis (fsQCA) for the configurational analysis. The research results emphasize several important insights. Perceived usefulness, intrinsic motivation, and satisfaction play important roles in encouraging university students' continuance intentions. Satisfaction emerges as a pivotal mediator, bridging the connection between perceived usefulness, intrinsic motivation, and continuance intention. The system quality, the information quality, and social interaction have significant positive influences on perceived usefulness. Perceived autonomy and perceived competence display strong correlations with both intrinsic motivation and satisfaction. Moreover, the results from the fsQCA show five configurations, in which the key factors collectively shape students' continuance intentions through complex interactions through various configurations. The findings reveal diverse configurations by which university students form continuance intentions towards using AI tools for SDL, providing detailed insights into the profound and indirect impacts on forming continuance intention behaviors. This enriches and advances the current theoretical understanding. [ABSTRACT FROM AUTHOR]

Background: Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective: To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods: Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results: AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion: While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. [ABSTRACT FROM AUTHOR]

Background: Point-of-care ultrasound is rapidly gaining traction in clinical practice, including primary care. Yet, logistical challenges and geographical isolation hinder skill acquisition. Concurrently, an evidentiary gap exists concerning such guidance's effectiveness and optimal implementation in these settings. Methods: We developed a lung point-of-care ultrasound (POCUS) curriculum for primary care physicians in a rural, medically underserved region of the south of Israel. The course included recorded lectures, pre-course assessments, hands-on training, post-workshop lectures, and individual practice. To evaluate our course, we measured learning outcomes and physicians' proficiency in different lung POCUS domains using hands-on technique assessment and gathered feedback on the course with a multi-modal perception approach: an original written pre- and post-perception and usage questionnaire. Results: Fifty primary care physicians (PCPs) showed significant improvement in hands-on skills, increasing from 6 to 76% proficiency (p < 0.001), and in identifying normal versus abnormal views, improving from 54 to 74% accuracy (p < 0.001). Ten weeks after training, primary care physicians reported greater comfort using lung ultrasound, rising from 10 to 54% (p < 0.001), and improved grasp of its potential and limits, increasing from 27.5% to 84% (p < 0.001). Weekly usage increased from none to 50%, and the number of primary care physicians not using at all decreased from 72 to 26% (p < 0.001). Conclusions: A two-day focused in-person and remote self-learning lung-POCUS training significantly improved primary care physicians' lung ultrasound skills, comfort, and implementation. [ABSTRACT FROM AUTHOR]

Student academic achievement is an important indicator for evaluating the quality of education, especially, the achievement prediction empowers educators in tailoring their instructional approaches, thereby fostering advancements in both student performance and the overall educational quality. However, extracting valuable insights from vast educational data to develop effective strategies for evaluating student performance remains a significant challenge for higher education institutions. Traditional machine learning (ML) algorithms often struggle to clearly delineate the interplay between the factors that influence academic success and the resulting grades. To address these challenges, this paper introduces the XGB-SHAP model, a novel approach for predicting student achievement that combines Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The model was applied to a dataset from a public university in Wuhan, encompassing the academic records of 87 students who were enrolled in a Japanese course between September 2021 and June 2023. The findings indicate the model excels in accuracy, achieving a Mean absolute error (MAE) of approximately 6 and an R-squared value near 0.82, surpassing three other ML models. The model further uncovers how different instructional modes influence the factors that contribute to student achievement. This insight supports the need for a customized approach to feature selection that aligns with the specific characteristics of each teaching mode. Furthermore, the model highlights the importance of incorporating self-directed learning skills into student-related indicators when predicting academic performance. [ABSTRACT FROM AUTHOR]

Background: Despite the numerous advantages of the nursing process, nursing students often struggle with utilizing this model. Therefore, studies suggest innovative teaching methods to address this issue. Teaching based on real clinical cases is considered a collaborative learning method that enhances students' active learning for the development of critical thinking and problem-solving skills. In this method, students can acquire sufficient knowledge about patient care by accessing authentic information. Objective: The aim of the present study was to investigate the experiences of nursing students and faculty members regarding the implementation of nursing process educational workshops, based on real case studies. Design: A qualitative descriptive study. Participants: 9 Nursing students and 7 faculty members from the Isfahan School of Nursing and Midwifery who attended the workshops. Methods: This qualitative descriptive study was conducted from 2021 to 2023. Data was collected through semi-structured individual and focus group interviews using a qualitative content analysis approach for data analysis. Results: After analyzing the data, a theme titled "Breaking Taboos in the Nursing Process" was identified. This theme consists of four categories: "Strengthening the Cognitive Infrastructure for Accepting the Nursing Process," "Enhancing the Applicability of the Nursing Process," "Assisting in Positive Professional Identity," and "Facilitating a Self-Directed Learning Platform." Additionally, thirteen subcategories were obtained. Conclusion: The data obtained from the present study showed that conducting nursing process educational workshops, where real clinical cases are discussed, analyzed, and criticized, increases critical thinking, learning motivation, and understanding of the necessity and importance of implementing the nursing process. Therefore, it is recommended that instructors utilize this innovative and effective teaching method for instructing the nursing process. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of eLearning in enhancing healthcare professionals' capacity has received substantial attention globally. This review sought to synthesis evidence on the effectiveness of various types of eLearning programmes, and the facilitators and barriers to its use. Methods: The review was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Four main databases (PubMed, Web of Science, JSTOR, and Scopus) in July 2023 and 44 articles met the eligibility criteria and were included in the review. The JBI critical appraisal checklist was used to appraise the methodological quality of the studies. The data were examined using narrative review to determine the effectiveness of the intervention as well as the barriers and facilitators to its use. Results: This review found that asynchronous, synchronous, blended, and self-learning methodologies are effective eLearning approaches for continuous professional development. Previous positive experiences, user-friendly interfaces and relevance of the eLearning content to daily practice are critical elements that facilitate eLearning usage. Poor computer competence and literacy, lack of personal computers and high family duties were the main personal factors that hindered eLearning use. Some systemic barriers included; heavy workloads, shortage of specialised eLearning facilitators poor management involvement, and technical inadequacies within the ICT departments. Environmental issues such as poor infrastructure, including limited internet and frequent power outages acted as barriers. Conclusion: The review highlights the effectiveness of various eLearning approaches among health professionals and presents the disparities between developing and developed economies in relation to the facilitators and barriers. [ABSTRACT FROM AUTHOR]

This study aimed to explore the application effect of problem-based learning and situational simulation teaching in cardiopulmonary resuscitation (ECPR) education. A retrospective analysis was conducted on 128 trainees specializing in ECPR at our institution. The participants were divided into two groups: a study group and a control group. The study group consisted of 64 students who received problem-based learning with situational simulation, and a control group of 64 students who adopted traditional teaching methods. Comparative analysis was performed between the two groups in terms of theoretical and practical skills scores, Cardiopulmonary resuscitation (CPR) compression quality, self-directed learning ability and satisfaction with the teaching. There was no statistically significant difference between the two groups in the following pre-training measures: self-management ability score, passion for learning score, self-control ability score, compression depth, compression depth efficiency, chest rebound ratio and compression rate (p > 0.05). After undergoing training, the study group outperformed the control group in all aspects, including theory, practical skills, compression depth, compression depth efficiency, chest rebound ratio, compression rate, self-management ability, passion for learning and self-control ability. The study group also reported higher satisfaction with the teaching, with a statistically significant difference (p < 0.05). We conclude that problem-based learning used in conjunction with situational simulation in CPR training can help trainees enhance their learning abilities, better grasp theoretical and practical knowledge, and improve the quality of CPR practical skills. [ABSTRACT FROM AUTHOR]

Background: Indian medical graduates are expected to be lifelong learners, and medical teachers need to find newer and more engaging ways to help them hone their lifelong learning skills. Online learning can be a stepping stone toward this goal. Methods: We conducted a pre-post design interventional study in which 22 phase two MBBS students rated their self-directedness in learning using the Self-Rating Scale of Self-directed Learning (SRSSDL) at two points in time—before and after completing a 10-week online basic psychopharmacology module. Participant feedback was obtained regarding various aspects of the online modular learning experience. Results: The participants' mean SRSSDL scores before and after the intervention were 231.31 ± 26.64 and 227.31 ± 31.33. The difference is not significant. The students' scores at both points in time placed them in the "high" range of level of self-directed learning, leaving less realistic scope for a large change in the given time frame. Participant feedback was encouraging, with 19 out of 22 reporting that the overall learning experience was a "positive" one. Conclusions: Second-year medical students' self-reported self-directedness is high. Online modular learning may be a feasible addition to medical education, even though it may not increase self-directedness in the short term. A limited sample size can lead to type 2 errors. [ABSTRACT FROM AUTHOR]

Self-supervised learning (SSL) has gained significant attention in the past decade for its capacity to utilize non-annotated datasets to learn meaningful data representations. In the medical domain, the challenge of constructing large annotated datasets presents a significant limitation, rendering SSL an ideal approach to address this constraint. In this study, we introduce a novel pretext task tailored to stimulus-driven eye movement data, along with a denoising task to improve the robustness against simulated eye tracking failures. Our proposed task aims to capture both the characteristics of the pilot (brain) and the motor (eye) by learning to reconstruct the eye movement position signal using up to 12.5% of the unmasked eye movement signal patches, along with the entire REMOBI target signal. Thus, the encoder learns a high-dimensional representation using a multivariate time series of length 8192 points, corresponding to approximately 40 s. We evaluate the learned representation on screening eight distinct groups of pathologies, including dyslexia, reading disorder, and attention deficit disorder, across four datasets of varying complexity and size. Furthermore, we explore various head architecture designs along with different transfer learning methods, demonstrating promising results with improvements of up to approximately 15%, leading to an overall macro F1 score of 61% and 61.5% on the Saccade and the Vergence datasets, respectively. Notably, our method achieves macro F1 scores of 64.7%, 66.1%, and 61.1% for screening dyslexia, reading disorder, and attention deficit disorder, respectively, on clinical data. These findings underscore the potential of self-learning algorithms in pathology screening, particularly in domains involving complex data such as stimulus-driven eye movement analysis. [ABSTRACT FROM AUTHOR]

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%. [ABSTRACT FROM AUTHOR]

The precise segmentation of different regions of the prostate is crucial in the diagnosis and treatment of prostate-related diseases. However, the scarcity of labeled prostate data poses a challenge for the accurate segmentation of its different regions. We perform the segmentation of different regions of the prostate using U-Net- and Vision Transformer (ViT)-based architectures. We use five semi-supervised learning methods, including entropy minimization, cross pseudo-supervision, mean teacher, uncertainty-aware mean teacher (UAMT), and interpolation consistency training (ICT) to compare the results with the state-of-the-art prostate semi-supervised segmentation network uncertainty-aware temporal self-learning (UATS). The UAMT method improves the prostate segmentation accuracy and provides stable prostate region segmentation results. ICT plays a more stable role in the prostate region segmentation results, which provides strong support for the medical image segmentation task, and demonstrates the robustness of U-Net for medical image segmentation. UATS is still more applicable to the U-Net backbone and has a very significant effect on a positive prediction rate. However, the performance of ViT in combination with semi-supervision still requires further optimization. This comparative analysis applies various semi-supervised learning methods to prostate zonal segmentation. It guides future prostate segmentation developments and offers insights into utilizing limited labeled data in medical imaging. [ABSTRACT FROM AUTHOR]

The classrooms in Israel are very diverse, with students differing in learning styles, their handling of literacy tasks, personal and socioeconomic backgrounds, and more. These differences significantly impact the curriculum aimed at promoting literacy resilience, explicit teaching processes in the classroom, and imparting metacognitive strategies and actions to overcome learning difficulties. This qualitative-interpretative study reveals the pedagogical perceptions, challenges, and coping strategies of fourteen Hebrew teachers in five elementary schools in central Israel regarding integrating home literacy in language lessons and cultivating literacy resilience among their students. The research data were collected through in-depth interviews with the teachers. The analysis of the teachers' reports reveals two main perceptions regarding literacy resilience: (1) Literacy resilience is a tool for life; (2) Home literacy significantly contributes to fostering literacy resilience. Furthermore, cultivating literacy resilience presents three significant challenges for the teachers: (1) Teaching in a heterogeneous classroom, (2) Encouraging parental involvement, and (3) Fostering independent learners. To cultivate literacy resilience in a heterogeneous classroom, teachers must be sensitive to each student's unique needs and plan teaching-learning processes based on principles of self-directed learning and peer dialogue. They must establish a personal-emotional connection that is a significant anchor for the students and outlines a path for integrating and strengthening the sense of competence in handling literacy tasks. It was also found that parental involvement is a significant factor influencing the cultivation of literacy resilience, and teachers undertake various actions to increase their level of involvement. This study adds an essential layer to the body of knowledge regarding the understanding of the factors affecting the development of pedagogical literacy perceptions that promote the integration of home literacy in the classroom. These perceptions may promote the nurturing process of literacy resilience among students from various cultures, accepting and understanding them. In this way, we can attempt to address literacy and language challenges in Israel. [ABSTRACT FROM AUTHOR]

Introduction: Blended learning is a combination of online and face-to-face learning. With the introduction of a competencybased curriculum in India and Early Clinical Exposure (ECE) as a part of it, there is a need for innovative learning methods like the Blended Learning Method (BLM). Aim: To analyse the effectiveness of BLM in implementing ECE compared to the Non BLM or traditional method. Materials and Methods: This educational interventional study was conducted at the National Institute of Medical Sciences and Research, NIMS University, Jaipur, Rajasthan, India from April 2022 to October 2022. The sample population included Phase 1 MBBS students (n=110). Students were randomly assigned to two groups: the BLM group and the Non BLM group, each consisting of 55 students. The BLM group received ECE through a combination of digital and face-to-face methods, while the Non BLM group received traditional face-to-face ECE. Students' performance was evaluated through standardised assessments. Students' and faculty perceptions of the BLM method were assessed using a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Results: The comparison of pretest scores between both groups was statistically not significant. However, the post-test score comparison was statistically significant (p-value<0.05) for a few ECE sessions (sessions 1 and 3), with better performance observed in the BLM group. The paired t-test between pre and post-test scores was highly significant in both the BLM and Non BLM groups (p-value<0.0001). The student perception of the use of BLM was positive, while faculty perception indicated that they had difficulty using the BLM method to implement ECE. Conclusion: BLM can be an effective tool in the implementation of ECE sessions in physiology. It can enhance the self-directed learning ability of students. [ABSTRACT FROM AUTHOR]

Introduction: Early Clinical Exposure (ECE) bridges the gap between theory and practice for medical students. The Oneminute Preceptor (OMP) model, a widely used clinical teaching framework, promotes effective feedback and critical thinking efficiently. It employs five microskills: commitment, evidence probing, teaching rules, reinforcing correct actions, and correcting mistakes. This structured approach enhances clinical teaching in limited time, particularly in outpatient and ambulatory settings. Aim: To evaluate the perceptions of first-year MBBS students regarding the effectiveness of the five microskills of the OMP model during ECE. Materials and Methods: A quasi-experimental study was conducted at Vydehi Institute of Medical Sciences and Research Centre, Bengaluru, Karnataka, India, from September 2019 to June 2020, involving 188 first-year MBBS students. Data were collected through questionnaires focusing on the OMP's five microskills. Pretest and post-test scores were compared, and session feedback was obtained using a validated 10-item questionnaire. Paired t-tests and Analysis of Variance (ANOVA) were used for statistical analysis. Results: The study sample comprised 102 females and 86 males students (mean age 19.2 years). Results showed a significant increase from pretest (10.55±4.4558) to post-test (22.29±1.687). Students reported improvements in diagnostic capabilities (67.6%), understanding of clinical history (64.9%), self-directed learning (60.6%), critical thinking (58%), clinical reasoning (57.5%), and decision-making confidence (52.1%). The OMP also promoted motivation for rectifying mistakes. Conclusion: ECE using the OMP model proved to be effective in enhancing various aspects of students' clinical skills and reasoning abilities. The study recommends including the OMP in preclinical competency-based curricula to prepare students for busy clinical practice and outpatient settings. [ABSTRACT FROM AUTHOR]

Embedding clinically relevant learning experience in basic science subjects is desired for the preclinical phase of undergraduate medical education. The present study aimed to modify case-based learning (CBL) with a role-playing situational teaching method and assess the student feedback and learning effect. One hundred seventy-six sophomore students majoring in clinical medicine from Harbin Medical University were randomly divided into two groups: the control group (n = 90), who received traditional hybrid teaching, and the experimental group (n = 86), who received the role-playing situational teaching. Students in the experimental group were given a 1-wk preclass preparation to dramatize a hyperthyroidism scenario through online autonomous learning of thyroid physiology and performed the patient's consultation process in class, followed by a student presentation about key points of lecture content and a question-driven discussion. A posttest and questionnaire survey were conducted after class. The test scores of the two groups had no statistical differences, whereas the rate of excellence (high scores) of the experimental group was significantly higher than that of the control group. Furthermore, the record of online self-directed learning engagements was significantly improved in the experimental group. In the questionnaire, >70% of the students showed positive attitudes toward the role-playing situational teaching method and were willing to participate in other chapters of the physiology course. Such results show that CBL supported by a role-playing situational teaching method encourages active learning and improves the application of basic knowledge of physiology, which can be incorporated in the preclinical curricula to bridge the gap between theory and practice. NEW & NOTEWORTHY: Formal application through structured role-play is often overlooked in physiology education. In traditional case-based learning (CBL), clinical cases are the subject and unfocused discussion often occurs. The present study aimed to modify CBL with a role-playing situational teaching method and assess the student feedback and learning effect. The results show that the new teaching model encourages active learning and improves the application of basic knowledge of physiology. [ABSTRACT FROM AUTHOR]

In the field of automatic optical inspection (AOI), this study presents innovative strategies to enhance object detection accuracy while minimizing dependence on large annotated datasets. We initially developed a defect detection model using a dataset of 3579 images across 32 categories, created in collaboration with a major Taiwanese panel manufacturer. This model was evaluated using 12,000 ambiguously labeled images, with improvements achieved through data augmentation and annotation refinement. To address the challenges of limited labeled data, we proposed the Adaptive Fused Semi-Supervised Self-Learning (AFSL) method. This approach, designed for anchor-based object detection models, leverages a small set of labeled data alongside a larger pool of unlabeled data to enable continuous model optimization. Key components of AFSL include the Bounding Box Assigner, Adaptive Training Scheduler, and Data Allocator, which together facilitate dynamic threshold adjustments and balanced training, significantly enhancing the model's performance on AOI datasets. The AFSL method improved the mean average precision (mAP) from 43.5% to 57.1% on the COCO dataset and by 2.6% on the AOI dataset, demonstrating its effectiveness in achieving high levels of precision and efficiency in AOI with minimal labeled data. [ABSTRACT FROM AUTHOR]

Elearning in a blended learning approach provides enormous opportunities for working nurses to further their studies and enhance their professional development and life-long learning. To date, the impact of self-directed learning (SDL) abilities among working nurses in elearning education in Malaysia remains uncertain. This study aims to investigate the relationship between SDL abilities and elearning outcomes, as well as the mediating effect of student satisfaction between the variables. This is a cross-sectional quantitative correlational study using a predictive design and multivariate analysis method. A stratified random sampling technique was applied to recruit 241 nursing students through an online survey. The Self-Directed Learning Instrument (SDLI) and an elearning course evaluation survey were used to measure the independent and dependent variables of the study. The variance-based Partial Least Squares Structural Modeling (PLS-SEM) method was employed to analyse the possible effects on the relationships among the variables studied. The findings revealed statistically significant relationships among SDL abilities, student satisfaction, and elearning outcome. A total effect of 62.4% is predicted for SDL abilities on elearning outcomes through student satisfaction. This study highlights the vital role of SDL abilities for online learners’ success. A collaborative effort between universities and faculty and support personnel is essential to ensure successful, continuous elearning education for student nurses. We recom)mend that future studies explore other possible factors in the model that could help to enhance successful elearning in nursing education. [ABSTRACT FROM AUTHOR]

As generative AI tools are increasingly popular in today’s teaching and learning process, challenges and opportunities occur at the same time. Self-directed learning has been regarded as a powerful learning ability that supports learners in informal learning contexts and its importance rises in salience when incorporating AI into learning. This study employed a mixed-method design to understand how people self-direct their online language learning through the utilization of ChatGPT. Analyzing survey data from 276 survey respondents and 11 one-to-one interviews with language learners in the United States, we found that learners are motivated to use generative AI for its high flexibility and personalization which enables learners to access learning materials that align with their knowledge levels, personal interests, and learning goals. We also found self-monitoring skills that are inherent to learners help them to use ChatGPT more effectively and achieve their learning goals efficiently. This study contributes to the emerging field of AI in education and explores possibilities to use ChatGPT to foster self-directed language learning and provide educators, instructional designers, and researchers with insights to design learning integrated with AI to best fulfill learners’ diverse needs and expand learning opportunities to more people. [ABSTRACT FROM AUTHOR]

Students in higher education are using mobile devices, which challenges our understanding of what it means to learn in modern environments. By using mobile devices as instructional aids, students gain a variety of social, cognitive, and technical abilities. Mobile devices may therefore be seen as supporting the growth of employability skills and broader lifelong learning, both of which may be helpful in a variety of circumstances. The quickly evolving landscape of technology and education presented teachers with a number of challenges, and they had to retrain and refresh their skills in order to provide practical training. As a result, heutagogical methods are ideal for enhancing their collective knowledge and skills. Online and blended learning (BL) offer a learning environment that incorporates technological affordances to facilitate learning. This enables the growth of an independent, capable, and self-directed lifelong learner. Heutagogy provides an educational strategy in this context that may link the development of lifelong learning competencies with BL and online learning environments. Using this methodology, we investigate the potential contribution of online and blended higher education to technology-enabled lifelong learning where heutagogical experiences are available. The results support the notion that heutagogy and lifelong learning are linked by some basic concepts that apply to both mixed and online learning environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Research in Education Sciences is the property of National Taiwan Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Research in Education Sciences is the property of National Taiwan Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Research in Education Sciences is the property of National Taiwan Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite the increasing recognition of language learning beyond traditional classrooms, little is known about how language teachers support and guide students in self-directed learning environments. Drawing on qualitative data from a team of experienced English as a Foreign Language teachers, this article reports on the intersections between teachers' cognition, emotions, and agency in a self-directed language learning unit (SDLU) at a university in Japan. Thematic analysis of the data illuminated dialectal relationships between teachers' feelings, beliefs, and agency during the implementation of the SDLU. Although teachers were supportive of SDLU's goals and the overall approach it promoted, they were cautious of intervening in their students' self-directed learning due to concerns about protecting the learner autonomy that SDLU was designed to foster. As well as confusion over how best to meet the needs of their learners in this novel context, they experienced vulnerability in relation to their professional identities and there was considerable variation in ways teachers enacted agency and managed the new learning environment. The findings suggest teachers may need guidance and support to help them recalibrate existing competencies and develop strategies for nurturing their students' self-directed learning in novel classroom contexts. [ABSTRACT FROM AUTHOR]