Online Evaluation Research Articles

국내 간호대학생의 핵심간호술 교육 중재의 효과: 체계적 고찰

Objectives This study systematically reviewed the effectiveness of core nursing skill training intervention programs for nursing students in Korea. Methods Literature searches were conducted using the databases RISS, KISS, DBpia, and KCI for papers published up to June 2024, with the keyword ‘core nursing skills’. A total of 130 papers were identified, and ultimately, 14 papers were selected for the literature review. Results The literature review revealed that among the research designs, 1 was randomized and 13 were nonrandomized. Core nursing skill training interventions included studies using various methods of retraining and evaluation (10 papers) and studies using various methods of self-practice and evaluation (4 papers). Retraining and evaluation methods included traditional retraining, STEPS Model retraining, senior mentoring retraining, multimedia retraining, and online learning retraining, while self-practice and evaluation methods included traditional self-practice, scenario-based self-practice, Virtual Reality-based self-practice, and self-observation methods. The most frequently applied core nursing skill was subcutaneous injection, appearing in 10 studies, followed by vital signs measurement, intramuscular injection, intermittent gastric tube feeding, and foley catheterization, each appearing in 8 studies. Outcome variables of core nursing skill training programs showed significant effects on confidence in performance (9 papers), clinical performance ability (4 papers), self-efficacy (3 papers), and knowledge (3 papers). Conclusions Korean nursing colleges have researched various intervention programs to achieve core nursing skill competency levels in accordance with the Nursing Education Evaluation Criteria. These programs have shown significant effects on performance confidence, clinical performance ability, and self-efficacy. Therefore, there is a need to develop and utilize diverse intervention programs to enhance learning motivation and satisfaction among nursing students.

Robotic MAG welding defects and quality assessment with a defect threshold decision model-driven method

The use of machine vision and deep learning methods for online monitoring and evaluation of welding quality during the welding process and timely detection and correction of welding defects is essential for intelligent welding manufacturing. In this paper, with the goal of online monitoring of the welding quality of the robotic arc welding for the excavator boom, we established a multi-source sensing system for robotic arc welding processes. We have developed various feature extraction algorithms for extracting information about welding pools, arc sounds, currents, voltages, and other features. We proposed to extract welding pool image features using Res2-MobileNetV3 to obtain a 12-dimensional welding pool feature, which enabled good inter-class discrimination among different welding defects. We designed a welding defect threshold decision (DD) strategy model using the responsive machine learning and lightweight network Res2-MobileNetV3 method, achieving a welding defect recognition accuracy of 96.59%. Finally, we tested the accuracy and reliability of the welding defect recognition model on an actual welding scene for the robotic welding of the excavator boom. It provided valuable scientific methods and technological approaches for intelligent welding in complex scenes.

Online Evaluation for the POI-Level Inertial Support to the Grid via Ambient Measurements

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5%.

Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping

To mitigate unforeseen incidents, such as key block failure or tunnel collapse during excavation, an appropriate support pattern that correlates with the geological conditions of the rock mass at the tunnel face should be designed. Rock mass evaluations should be conducted through geological face mapping during the construction phase, alongside predictions based on field investigations during the design phase. When marked discrepancies are identified, it is customary to convene an on-site evaluation involving a committee of experts. This study develops a digital tunnel face mapping system that utilises mobile devices to facilitate online evaluations during the construction phase. This system effectively replaces the traditional on-site field evaluation method. Tunnel face mapping can be promptly accomplished using images captured at the excavation face, enabling rapid analysis. In conjunction with the mapping capabilities, the developed system was designed to digitally store geological information, which includes parameters such as rock strength distribution, the spacing and length of discontinuities observed during the mapping process, as well as data pertaining to weathering and the groundwater conditions of those discontinuities. This information was then correlated with the rock mass rating sheet to automate the determination of ratings for each parameter, ultimately leading to a conclusive classification of the rock mass quality. By employing this system for tunnel face mapping and rock quality evaluation, we significantly reduced the discrepancies in the evaluation results that often arise due to the subjective judgement of geologists, as well as human errors that can occur throughout the rating process.

A Meta-Analysis of the Association Between the Hold Me Tight Program and Couples’ Relationship Adjustment

In this meta-analysis, the authors tested the overall effect of Hold Me Tight (HMT), a relationship education program for couples based in emotionally focused therapy, on couples’ relationship adjustment. Using a detailed search process, we identified and analyzed eight quantitative, single-group comparison studies. Studies included predominantly cisgender, straight couples (N = 310) in dyadic, monogamous relationships from the United States, Canada, and the Netherlands. The eight studies also included evaluations of online and in-person delivery methods, as well as eight-week groups and one- and two-day workshops. Across the studies, we found that participation in HMT was associated with a small, statistically significant effect (d = 0.377) on couples’ self-reported relationship adjustment, lending support that HMT may be an effective program for couples. Few available studies limited our ability to test additional outcomes or explore moderators of the effect, which are key areas for future research.

Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems

We consider parametrized linear-quadratic optimal control problems and provide their online-efficient solutions by combining greedy reduced basis methods and machine learning algorithms. To this end, we first extend the greedy control algorithm, which builds a reduced basis for the manifold of optimal final time adjoint states, to the setting where the objective functional consists of a penalty term measuring the deviation from a desired state and a term describing the control energy. Afterwards, we apply machine learning surrogates to accelerate the online evaluation of the reduced model. The error estimates proven for the greedy procedure are further transferred to the machine learning models and thus allow for efficient a posteriori error certification. We discuss the computational costs of all considered methods in detail and show by means of two numerical examples the tremendous potential of the proposed methodology.

Uncovering the impacts of technology literacies and acceptance on emotion regulation, resilience, willingness to communicate, and enjoyment in Intelligent Computer-Assisted Language Assessment (ICALA): an experimental study

Acquiring technological literacy and acceptance has a significant influence on academic emotion regulation (AER), academic resilience (AR), willingness to communicate (WTC), and academic enjoyment (AE), which are crucial for the success of university students. However, this area has not been adequately explored in research, particularly in the context of Intelligent Computer-Assisted Language Assessment (ICALA). This study aimed to bridge the existing information gap by examining the impact of technology literacies and acceptance on the AER, AR, WTC, and AE levels among Uzbek university students enrolled in virtual English as a foreign language (EFL) programs with online evaluation. Quasi-experimental research was carried out with 67 university students pursuing a degree in Applied linguistics to assess the potential underlying connections. Throughout the semester, students in the experimental group were provided with instructional feedback to foster technology literacies and acceptance through a private group on Telegram. This feedback assisted them in acquiring pertinent information to improve the application of technology in language learning and assessment. Based on the results, students in EG fared better than their counterparts in the control group in terms of AER, AR, WTC, and AE in ICALA. The data indicates that in the ICALA setting, EFL learners experience unpleasant emotions, a lack of resilience, unenjoyment, and an unwillingness to communicate due to a lack of knowledge in the application of technology. The implications of the study are examined in further detail.

A deep learning-powered intelligent microdroplet analysis workflow for in-situ monitoring and evaluation of a dynamic emulsion

Accurately determining microdroplet size and uniformity in dispersed phase systems is key for assessing emulsion stability, and essential for quality monitoring and control of emulsion products. Microscopic imaging probes have provided intuitive and advanced insights for characterizing particle microscopic morphology and dispersion in multiphase flows. However, wide size distributions, edge-cutting, overlapping, and high-density target droplets can compromise the image processing accuracy, thereby affecting the precise assessment of emulsion status. In this study, we present a deep learning-based intelligent workflow for microdroplet characterization using an in-situ high-speed particle imaging probe combined with advanced image processing techniques. With the assistance of progressive automatic annotation, a droplet image database was constructed for training the Mask R-CNN model. By incorporating image patching and supervised data augmentation strategies, our well-trained model with accuracy of 95.5% can precisely detect, localize, and segment microscale droplets with various patterns. Additionally, the shape reconstruction module visualizes true shapes of overlapping and edge-cutting droplets, facilitating precise quantification of droplet size information. To validate the feasibility and generalizability of the proposed workflow, we obtained sufficient droplet images through offline sampling during emulsion processes while employing back and front illumination for in-situ monitoring. The automated method achieves precise droplet detection and size measurement, demonstrating its immense potential in online monitoring, evaluation, and control of emulsion quality.

COVID-19 pandemic and women transitioning from incarceration: a study of online health information seeking among underserved and marginalized women

ABSTRACT Based on interviews with 118 women recently released from incarceration in the U.S. Midwest, this study examines changes in their online health information consumptions and evaluations throughout the COVID-19 pandemic (2020–2022). Findings show that women transitioning from incarceration, who are often marginalized at the intersection of gender, socioeconomic status, and race, faced challenges navigating and assessing online health information during the pandemic. We use a feminist framework in analyzing online health information seeking and assessments among women recently released from incarceration while acknowledging intersectional marginality experienced by this population. Given the limited existing research on reentry programs focusing specifically on justice-involved women, our study contributes to developing and improving health and information literacy programs for this underserved and marginalized population.

A non-absorption-loss immune TDLAS sensor for online Mach number evaluation in supersonic flows

Tunable diode laser absorption spectroscopy (TDLAS) techniques are widely used in combustion monitoring by using direct absorption spectroscopy (DAS). However, the DAS method is sensitive to laser intensity fluctuations and fails to work in harsh application cases. A non-absorption-loss immune TDLAS sensor was proposed for robust online evaluation of Mach number from temperature and speed in supersonic flows and in situ validated on a scramjet. Spectral lines of H2O at 7185.58 and 7444.35 cm−1 were selected to perform evaluations of target molecules, i.e., of water vapor. In highly turbulent supersonic flows of Mach 2.8, the proposed TDLAS sensor has improved the signal-to-noise ratio (SNR) of the transmitted laser intensity by about 6.46 dB, compared to the conventional single-ended TDLAS sensor. When the ramjet was running stably, the average Mach number was 2.82, and standard deviations of the Mach number were less than 0.09. Relative errors of measured Mach numbers were less than 1.39 %. The proposed sensor monitors the entire ramjet engine process from ignition to propellant cut-off at a rate of 5 kHz and yields detailed insights for ramjet performance evaluation.

Midwifery student engagement with digital interactive books: A cross sectional survey

AimTo explore the benefit and engagement of undergraduate students’ use of H5P interactive books for student learning. DesignAn evaluation study of technology enabled learning for first year undergraduate Bachelor of Midwifery students in Australia. MethodsStudents were invited to complete an online evaluation survey of their use and engagement with H5P interactive books. The survey included the long form User Engagement Scale which has four subscales of focused attention, aesthetic appeal, reward factor and perceived usability scored on a 5-point Likert scale. Content analysis was used to analyse the text comments given to five open text questions. ResultsThere were 21 students who completed the survey. There was a high overall User Engagement Scale score of 73.1 % with aesthetic appeal and reward factor being the highest scoring subscales. The content analysis showed students found the interactive books engaging and easy to navigate. Areas for improvement identified were not including a large amount of content and providing downloadable content. ConclusionsThis study demonstrates the valuable and engaging use of H5P Interactive Books for undergraduate students in higher education. Students who used H5P Interactive Books identified their ease of use, organised layout and engaging format.

Transforming researchers into writers through a series of semi-structured writing retreats: a mixed methods study

AimTo evaluate the experience and effectiveness of six semi-structured writing retreats on research publication quantity and quality for nursing and midwifery academics and research students.BackgroundResearch publications are necessary to develop a track record to gain competitive funding and for promotion. Publications also improve the standing of universities because their performance is measured in-part by research outputs. However, there are challenges to writing for publication, especially for new nursing and midwifery academics and research students. Therefore, four of the authors initiated semi-structured writing retreats to support nursing and midwifery academics and research students to overcome these challenges.MethodsA mixed methods exploratory sequential design consisting of two distinct phases and data collection methods. In phase one, an online evaluation was administered to collect participant experiences which were then analysed using content analysis. In phase two, data about the quantity and quality of publications arising from each retreat was collected, and descriptive statistics performed.ResultsA total of 70 participants responded to the online evaluation. Qualitative analysis of their responses demonstrated that the writing retreats were highly valued as they offered a collaborative environment with dedicated time to focus on writing for publication. Quantitative analysis identified 81 publications were planned over the six writing retreats. Of these, 60 have been published, 5 are under review, 5 have not yet been submitted, and 11 were abandoned.ConclusionsFindings demonstrated that our six semi-structured writing retreats enabled and developed nursing and midwifery academics and research students writing for publication. Semi-structured writing retreats are a research investment that enabled preparation of high-quality publications by offering protected time to write, expert peer review and collaboration and networking opportunities.

Student's Priorities When Selecting Web Conferencing Tools in Sri Lankan Tertiary Education

A properly selected web conferencing tool can significantly enhance student engagement, motivation, and learning outcomes in online education. Hence, this study aims to produce a framework ordering the key features and associated sub-factors of web conferencing tools that Sri Lankan tertiary sector students prioritize for their online learning experience. A quantitative research method is deployed to get the student's opinions on the factors discovered in a previous study on “Expert opinion on selecting a web conferencing tool for synchronous online tertiary education in Sri Lanka”. The research hypothesis was tested using the Pearson Chi-square test, followed by exploratory and second-order factor analysis to rank the importance of sub-factors and main characteristics. The findings revealed that students prioritize collaboration features the most, followed by online event features, pricing, user-friendliness, customer support, security, online evaluation, performance, and screen-sharing. This framework offers valuable insights for Sri Lankan tertiary institutions to select web conferencing tools that truly align with student preferences. By incorporating these student-centric considerations, institutions can optimize the online learning experience, leading to improved engagement, learning outcomes, and ultimately, greater student satisfaction.

Going virtual: mixed methods evaluation of online versus in-person learning in the NIH mixed methods research training program retreat

BackgroundDespite the central role of mixed methods in health research, studies evaluating online methods training in the health sciences are nonexistent. The focused goal was to evaluate online training by comparing the self-rated skills of scholars who experienced an in-person retreat to scholars in an online retreat in specific domains of mixed methods research for the health sciences from 2015–2023.MethodsThe authors administered a scholar Mixed Methods Skills Self-Assessment instrument based on an educational competency scale that included domains on: “research questions,” “design/approach,” “sampling,” “analysis,” and “dissemination” to participants of the Mixed Methods Research Training Program for the Health Sciences (MMRTP). Self-ratings on confidence on domains were compared before and after retreat participation within cohorts who attended in person (n = 73) or online (n = 57) as well as comparing across in-person to online cohorts. Responses to open-ended questions about experiences with the retreat were analyzed.ResultsScholars in an interactive program to improve mixed methods skills reported significantly increased confidence in ability to define or explain concepts and in ability to apply the concepts to practical problems, whether the program was attended in-person or synchronously online. Scholars in the online retreat had self-rated skill improvements as good or better than scholars who participated in person. With the possible exception of networking, scholars found the online format was associated with advantages such as accessibility and reduced burden of travel and finding childcare. No differences in difficulty of learning concepts was described.ConclusionsKeeping in mind that the retreat is only one component of the MMRTP, this study provides evidence that mixed methods training online was associated with the same increases in self-rated skills as persons attending online and can be a key component to increasing the capacity for mixed methods research in the health sciences.

Assessing the Alignment Between Online Word-Of-Mouth and Schools’ Actual Academic Performance: A Deep Neural Network Analysis

ABSTRACT Many parents use social media to gather word-of-mouth regarding schools’ performance, subsequently influencing their school choice decisions. Yet, it’s unclear how well online opinions reflect schools’ actual academic performance. To address this gap, this study uses machine learning to examine whether the online evaluations of students’ learning can match schools’ test scores and their improvements. Findings indicate that while online evaluations don’t perfectly align with test scores, the association between online word-of-mouth and test scores remains stronger than that of test score gains. Consequently, online word-of-mouth tends to recommend schools with higher test scores over higher value-added scores.

Investigating the effects of online assessment on students’ learning experience

During the pandemic outbreak teaching and learning process has changed into an online mode and thus online evaluation became a requirement. Therefore, the current paper attempts to investigate the effects of online assessment on students’ learning behavior and teachers' views. It also includes the results of a questionnaire from 60 students followed by an interview with 07 EFL teachers from the Department of English in the University. The results from the questionnaire indicated that students have positive attitudes towards the administration of online assessment. Furthermore, the results from the interviews showed that there are four factors for valuable effects which are showing flexibility in assessment, enhancing evaluation adaptability, and increasing teachers' and students' awareness of the importance of evaluation by enlightening students’ autonomous learning abilities. Keywords: Online assessment; online learning experience; student attitudes; teachers’ views.

Research on the online evaluation of the straightness error of hydrostatic guideways based on deep learning

Research on the online evaluation of the straightness error of hydrostatic guideways based on deep learning

Cross-sectional evaluation of online direct-to-public calprotectin testing

Objective Why about a quarter of patients with inflammatory bowel disease (IBD) suffer symptoms for more than a year before their diagnosis made is unclear. Low public awareness, embarrassment and...

#GlobalJustice?: Social Media, Pedagogy, and Activism

Abstract This article addresses the evolvement of pedagogical content on global social injustices across social media during the COVID-19 pandemic. With increasing constraints on how we teach about injustices within formal educational settings, young people are increasingly turning to social media to obtain information on global challenges. Scholars have sought to evaluate particular political knowledge gained through social media and/or debate its effectiveness in mobilizing collective action. This research shifts our focus from such top-down evaluations of political knowledge and activism online to understand how those most active across social media engage with pedagogical content on social injustices. Drawing on focus groups with students aged 13–16 from four schools across the United Kingdom, the article will argue four key points: First, young people are extremely cautious about trusting information on social media; second, they are more likely to trust content from those they feel personally connected to; third, exposure to lived experience through social media creates affective learning and reflexive opportunities; and fourth, social media can provide a catalyst for further learning and activism.

Evaluation of a group-based online informed consent conversation (eConsent) in participants from a low-risk vaccination clinical trial

BackgroundElectronic informed consent (eConsent) usage has expanded in recent years in Europe, especially during the pandemic. Slow recruitment rate and limitations in participant outreach are the challenges often faced in clinical research. Given the benefits of eConsent and group counselling reported in the literature, group eConsent was implemented in recruitment for the SWITCH-ON study. We aim to explore the experience of participants who attended group eConsent for the SWITCH-ON study and evaluate its potential for future use.MethodsSWITCH-ON study aims to analyse the immunogenicity of a healthy population following bivalent COVID-19 booster vaccination. Four hundred thirty-four healthcare workers aged 18–65 were successfully recruited and sent a questionnaire about their experience with group eConsent. Out of 399 completed questionnaires (response rate 92%), 39 participants did not join group eConsent. The remaining 360 responses were included in the final analysis. Quantitative and qualitative data were reported using descriptive statistical analysis and thematic analysis respectively.ResultsParticipants found that group eConsent was an efficient method that it allowed them to hear each other’s questions and concerns and created a sense of togetherness. However, limited privacy, barriers to asking questions in a group, and peer pressure can limit the use of group eConsent. One hundred sixty-five (46%) participants thought that group eConsent was suitable to recruit participants with diseases or conditions, while 87 (24%) reported limitations with this method. The remaining participants suggested that applicability of group eConsent depended on the diseases or conditions of the study population, and one-to-one conversation should always be available. Participants who had experienced both one-to-one and group eConsent shared different preferred consent formats for future studies.ConclusionGroup eConsent was positively evaluated by the participants of a low-risk vaccination study. Participants advised using webinars to provide general information about the study, followed by an individual session for each participant, would retain the benefits of group eConsent and minimise the limitations it posed. This proposed setting addresses privacy questions and makes group eConsent easier to implement.Trial registrationClinicalTrials.gov NCT05471440 (registered on 22nd of July, 2022).