Learning Tool Research Articles

Empirical approaches for non-linear site response: results for the ESG6-blind test

As a contribution to step 3 of the ESG6 blind prediction exercise, we present an application of two different, purely empirical approaches to estimate the strong ground motion at a soft site ("KUMA") from the observed ground motion at a reference rock site ("SEVO") for the two largest shocks of the Kumamoto 2016 sequence. The two methods estimate the non-linear transfer function between a reference rock and a sedimentary site by modifying the linear transfer function derived from weak motion recordings. The modification is based either on a machine learning tool based on a wide collection of Japanese weak and strong motion recordings and the associated site metadata (method 1), or on an estimate of a site-specific parameter related to an average non-linear site response (method 2). The acceleration time series are then derived at the sedimentary site of interest using an estimation of the time delay between wave arrivals at the rock and site stations, and a minimum phase assumption for the site transfer function. These predictions were made blindly, but after the ESG6 conference they could be compared both with the actual ground motion recorded at KUMA during the two shocks, and the average and range of all other predictions preformed for this benchmark. Both of these purely empirical methods provide an honorable prediction of usual engineering ground motion parameters of the two target events. The performance of these two purely empirical approaches is at least comparable to those of the numerical simulation methods for the foreshock—if not better—and slightly worse for the (largest) mainshock. As the methods required only recordings of weak motions at the target and a referent sites and very simple description of the soil profile. The use of moderate motions to constrain the frequency shift prediction for the second method and the consideration of an alternative phase modification are possible ways to improvement.Graphical

Empowering vocational students: Exploring mobile learning for sustainable high-level cognition in authentic contexts

Early studies show that learning with mobile devices, also known as mobile learning, improves students’ learning in authentic contextual learning–i.e., learning connected to the real world. However, no empirical evidence has yet to firmly prove the effects of mobile technology on specific student skillsets such as learning scalability which means learning can be applied in various scenarios and learning sustainability which means learning can be sustained in real-world environments. Therefore, this study aims to explore the effect of learning using a mobile app called mobile Smart-Physics on learning cognitive levels, learning scalability (e.g., number of learning locations and number of experimental data), and learning sustainability (e.g., number of completed assignments). Eleventh-grade vocational high school students volunteered for this quasi-experiment and were divided into an experimental group (EG), which used Smart-Physics, and a control group (CG), which used a mobile Ubiquitous-Physics (U-Physics) app. The findings show that the EG significantly outperformed the CG concerning learning cognitive levels, learning scalability and learning sustainability. Smart-Physics features enabled the students to tackle technical and pedagogical difficulties during physical investigations in real-world environments and, in some cases, improved their task accomplishment and sustained their motivation to learn. Location awareness promoted the students’ authentic experiential learning, which sharpened their ability to apply learning in real-world environments and upload more experimental data. Feedback helped the students consolidate their physics theories and practical experiences, thereby generating more learning records with meaningful multimedia content like experimental graphs, tables, and notes in various learning locations. Therefore, we encourage practitioners to use smart learning environment features in their learning tools and activity designs.

Pelaksanaan Kurikulum Merdeka di SMP Negeri 1 Banuhampu

The purpose of this study is to identify the implementation of the Kurikulum Merdeka and obstacles in the implementation of the Kurikulum Merdeka at SMP Negeri 1 Banuhampu. The study of the implementation of the Kurikulum Merdeka at SMP Negeri 1 Banuhampu can be carried out using a qualitative approach with a descriptive research method. The data collection technique uses observation, interview, and documentary research techniques to collect data from participants (including principals, curriculum representatives and teachers) involved in the implementation of the self-directed learning program at SMP Negeri 1 Remaja. Data analysis is carried out qualitatively consisting of data reduction, data presentation, and conclusion drawing. The results of the study show that the Kurikulum Merdeka has been implemented as expected. However, in its implementation, teachers need guidance, including attending training sessions at schools to learn more about the Kurikulum Merdeka and continuing to activate the learning community to identify shortcomings and things that need to be improved. In learning, the teachers have carried out the learning process according to the Kurikulum Merdeka and prepared learning tools starting from Learning Outcomes (CP), Learning Objectives Flow (ATP), Learning Objectives (TP), and teaching modules. Obstacles in the implementation of the Kurikulum Merdeka are human resources who do not understand the implementation of the Kurikulum Merdeka and inadequate facilities and infrastructure at SMP Negri 1 Banuhampu so that it slows down the learning process in the classroom.

Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.

The Use of a Novel Virtual Reality Training Tool for Peritoneal Dialysis: Qualitative Assessment Among Health Care Professionals.

Effective peritoneal dialysis (PD) training is essential for performing dialysis at home and reducing the risk of peritonitis and other PD-related infections. Virtual reality (VR) is an innovative learning tool that is able to combine theoretical information, interactivity, and behavioral instructions while offering a playful learning environment. To improve patient training for PD, Fresenius Medical Care launched the stay•safe MyTraining VR, a novel educational program based on the use of a VR headset and a handheld controller. This qualitative assessment aims to investigate opinions toward the new tool among the health care professionals (HCPs) who were responsible for implementing the VR application. We recruited nursing staff and nephrologists who have gained practical experience with the stay•safe MyTraining VR within pilot dialysis centers. Predetermined open-ended questions were administered during individual and group video interviews. We interviewed 7 HCPs who have 2 to 20 years of experience in PD training. The number of patients trained with the stay•safe MyTraining VR ranged from 2 to 5 for each professional. The stay•safe MyTraining VR was well accepted and perceived as a valuable supplementary tool for PD training. From the respondents' perspective, the technology improved patients' learning experience by facilitating the internalization of both medical information and procedural skills. HCPs highlighted that the opportunity offered by VR to reiterate training activities in a positive and safe learning environment, according to each patient's needs, can facilitate error correction and implement a standardized training curriculum. However, VR had limited use in the final phase of the patient PD training program, where learners need to get familiar with the handling of the materials. Moreover, the traditional PD training was still considered essential to manage the emotional and motivational aspects and address any patient-specific application-oriented questions. In addition to its use within PD training, VR was perceived as a useful tool to support the decision-making process of patients and train other HCPs. Moreover, VR introduction was associated with increased efficiency and productivity of HCPs because it enabled them to perform other activities while the patient was practicing with the device. As for patients' acceptance of the new tool, interviewees reported positive feedback, including that of older adults. Limited use with patients experiencing dementia or severe visual impairment or lacking sensomotoric competence was mentioned. The stay•safe MyTraining VR is suggested to improve training efficiency and efficacy and thus could have a positive impact in the PD training scenario. Our study offers a process proposal that can serve as a guide to the implementation of a VR-based PD training program within other dialysis centers. Dedicated research is needed to assess the operational benefits and the consequences on patient management.

Measuring attitude change in nursing students after completion of a First Nations peoples’ health unit: Embedding a validated tool in learning and assessment practices

The health inequities experienced by Aboriginal and Torres Strait Islander peoples, rooted in the historical and ongoing negative impacts of colonisation and disrupted traditional lifestyles, sees higher rates of illness and hospitalisations, increased morbidity and higher premature death rates than other Australians. Nurses represent the majority of Australia’s health workforce and nursing students must have knowledge to provide culturally safe care to Australia’s Aboriginal and Torres Strait Islander peoples. An Aboriginal and Torres Strait Islander Peoples’ Health and Wellbeing unit was developed and introduced into an undergraduate nursing curriculum. This project aimed to determine the effectiveness of the unit content in changing the attitude of nursing students towards care of First Nations peoples. A quasi-experimental design was used and included all undergraduate nursing students commencing this unit in 2020 at one university in Western Australia. Students completed a validated questionnaire prior to engaging with unit learning materials and again at completion of the unit. Complete paired data was available for 339 participants. Results demonstrated statistically significant change in attitude following completion of the unit. Embedding targeted education concerning Aboriginal and Torres Strait Islander peoples’ health and wellbeing positively influences attitudes in caring for this population of patients.

Comparative effectiveness of interventions for cancer treatment–related cognitive impairment in adult cancer survivors: protocol for a systematic review

BackgroundCancer treatment–related cognitive impairment (CTRCI) can substantially reduce the quality of life of cancer survivors. Many treatments of CTRCI have been evaluated in randomized controlled trials (RCTs), including psychological interventions, pharmacologic interventions, and other therapies. There is a pressing need to establish the benefits and harms of previously studied CTRCI treatments. The proposed systematic review and network meta-analyses will assess the relative efficacy and safety of competing interventions for the management of CTRCI.MethodsIn consultation with the review team, an experienced medical information specialist will draft electronic search strategies for MEDLINE®, Embase, CINAHL, PsycINFO, and the Cochrane Trials Registry. We will seek RCTs of interventions for the treatment of CTRCI in adults with any cancer, except cancers/metastases of the central nervous system. Due to the anticipated high search yields, dual independent screening of citations will be expedited by use of an artificial intelligence/machine learning tool. The co-primary outcomes of interest will be subjective and objective cognitive function. Secondary outcomes of interest will include measures of quality of life, mental and physical health symptoms, adherence to treatment, and harms (overall and treatment-related harms and harms associated with study withdrawal), where feasible, random-effects meta-analyses and network meta-analyses will be pursued. We will address the anticipated high clinical and methodological heterogeneity through meta-regressions, subgroup analyses, and/or sensitivity analyses.DiscussionThe proposed systematic review will deliver a robust comparative evaluation of the efficacy and safety of existing therapies for the management of CTRCI. These findings will inform clinical decisions, identify evidence gaps, and identify promising therapies for future evaluation in RCTs.

Editorial: Digitalization in education: developing tools for effective learning and personalisation of education

Editorial: Digitalization in education: developing tools for effective learning and personalisation of education

Gender differences in online education

AbstractOnline learning resources have become extremely popular, particularly after the restrictions caused by the Covid-19 outbreak. In this paper, we use data for Spain at the individual level from an online math learning platform which is used by children from over 100 countries, to document the gender differences in the context of online learning. We quantify the gender gaps in effort and relative performance outcomes and analyze whether the gaps differ by the gender of the parent who mainly supervises the children. Our main results point toward significant gender gaps in the relative performance outcomes in favor of boys, while the evidence for the effort gender gaps is only significant when we compare the siblings of the opposite gender (controlling for parent fixed effects). Further, we find that living in municipalities with more egalitarian gender norms is associated with narrower or positive gender gaps in effort outcomes, while we do not find such differences in the relative performance outcomes. Taking into account the increase in the use of online learning tools and their progressive integration into the regular educational system, our results provide important information to minimize gender biases in these new settings.

DepoScope: Accurate phage depolymerase annotation and domain delineation using large language models.

Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases.

Personalized approach to malignant struma ovarii: Insights from a web-based machine learning tool.

Malignant struma ovarii (MSO) is a rare ovarian tumor characterized by mature thyroid tissue. The diverse symptoms and uncommon nature of MSO can create difficulties in its diagnosis and treatment. This study aimed to analyze data and use machine learning methods to understand the prognostic factors and potential management strategies for MSO. In this retrospective cohort, the Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five machine learning algorithms to predict the 5-year survival. A validation method incorporating the area under the curve of the receiver operating characteristic curve was used to validate the accuracy and reliability of the machine learning models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 329 patients. Multivariate Cox regression analysis revealed that older age, unmarried status, chemotherapy, and the total number of tumors in patients were poor prognostic factors. Machine learning models revealed that the multilayer perceptron accurately predicted outcomes, followed by the random forest classifier, gradient boosting classifier, K-nearest neighbors, and logistic regression models. The factors that contributed the most were age, marital status, and the total number of tumors in the patients. The present study offers a comprehensive approach for the treatment and prognosis assessment of patients with MSO. The machine learning models we have developed serve as a practical, personalized tool to aid in clinical decision-making processes.

Listening Beyond: Collaborative Reflections on Learning about Activism Through an Undergraduate Oral History and Podcasting Project

ABSTRACT This article, written collaboratively by a professor and four undergraduate students, explores the pedagogical challenges and opportunities of learning oral history and creating an oral history podcast in one thirteen-week undergraduate course about activism. The article highlights four main themes that emerged: the use of oral history as a pedagogical tool and way of learning about activism, the challenges and opportunities of collaborative work, the particularities of conducting a digital project for public dissemination, and the significance of positionality and the ethical imperative of avoiding transactional and extractive approaches. This article emphasizes the inherent messiness and complexity of striving for equality in oral history pedagogy, acknowledging the tension between theory and practice, institutional constraints, and the transient nature of the classroom. The transformative potential of oral history pedagogy lies in its ability to bring diverse voices into the classroom while striking a balance between utopian aspirations and pragmatic considerations. Despite various challenges, the authors express collective effort and pride in the outcomes, reinforcing the value of teaching through oral history for meaningful engagement and personal growth.

Opportunities and challenges for using artificial intelligence in academic continuity: Case of Georgia

This article examined how higher education institutions in the Republic of Georgia responded to the challenges of the COVID-19 pandemic. Focusing on the context of the digital revolution and centering upon the utilization of artificial intelligence (AI), it aimed to discern how these institutions sustained the continuity of the learning process and implemented innovative measures. Based on the research findings, the solutions proposed in this article present AI tools for personalized learning, adaptive assessment, and intelligent tutoring. As institutions navigated the post-pandemic era, the integration of AI into education proved viable. This research provided tangible insights into the digital revolution affecting education and informing strategic decision-making in Georgia's evolving higher education landscape. Recognizing the difficulties caused by the pandemic and the inherent challenges associated with strategic decision-making, a qualitative research approach was used to gain nuanced insights. It relied on in-depth interviews, recognizing the spontaneous and time-sensitive nature of strategic decisions made by universities during the pandemic, often precluding extensive pre-planning. The authors provided critical findings in terms of the pros and cons of distance learning and proposed AI solutions for each challenge that Universities faced during and after this significant disruption, giving real successful examples.

Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy

To accurately capture the dynamic behavior of small nanoparticles in scanning transmission electron microscopy, high-quality data and advanced data processing is needed. The fast scan rate required to observe structural dynamics inherently leads to very noisy data where machine learning tools are essential for unbiased analysis. In this study, we develop a workflow based on two U-Net architectures to automatically localize and classify atomic columns at particle-support interfaces. The model is trained on non-physical image simulations, achieves sub-pixel localization precision, high classification accuracy, and generalizes well to experimental data. We test our model on both in situ and ex situ experimental time series recorded at 5 frames per second of small Pt nanoparticles supported on CeO2(111). The processed movies show sub-second dynamics of the nanoparticles and reveal site-specific movement patterns of individual atomic columns.

I reflect, therefore I am!, Exploring the use of a Voluntary Online Reflective Journal as a learning tool among postgraduate dental students.

Reflection is widely used in all aspects of teaching and learning in dental education and makes a fundamental part of all learning activities for dental students. However, reflective tasks are often used with a clear purpose; for example, in completing e-portfolios or dealing with critical incidences. This study explores the use of optional online journals that Postgraduate (PG) dental students were encouraged to use as part of their own development. To explore how PG dental students perceive the use of optional online journals. Data were collected via an anonymous questionnaire that included a word pool, Likert-scale statements and free text comment sections. Overall, 31 students (93%) responded to the questionnaire with high focus on the usefulness of the journal, with 58% selecting 'connecting with tutors' and 41% selecting 'keeping track' of their own learning and progress. The word 'reflection' was selected by 87% of participants when describing the use of the journal. Some participants, 29%, considered the journal as 'added pressure', and 41% felt it was 'extra work' as the journal, although voluntary, presented an added task to complete. All students made at least one entry in the online journal. The use of an optional online journal can be a useful tool in establishing connection between dental students and their tutors. Some postgraduate dental students valued the benefits of reflective journal without it being linked to assessments. Some concerns were reported around the time constraints as well as the added work related to taking part in such activity.

Aberrant migration features in primary skin fibroblasts of Huntington's disease patients hold potential for unraveling disease progression using an image based machine learning tool

Huntington's disease (HD) is a complex neurodegenerative disorder with considerable heterogeneity in clinical manifestations. While CAG repeat length is a known predictor of disease severity, this heterogeneity suggests the involvement of additional genetic and environmental factors. Previously we revealed that HD primary fibroblasts exhibit unique features, including distinct nuclear morphology and perturbed actin cap, resembling characteristics seen in Hutchinson-Gilford Progeria Syndrome (HGPS). This study establishes a link between actin cap deficiency and cell motility in HD, which correlates with the HD patient disease severity. Here, we examined single-cell motility imaging features in HD primary fibroblasts to explore in depth the relationship between cell migration patterns and their respective HD patients' clinical severity status (premanifest, mild and severe). The single-cell analysis revealed a decline in overall cell motility in correlation with HD severity, being most prominent in severe HD subgroup and HGPS. Moreover, we identified seven distinct spatial clusters of cell migration in all groups, which their proportion varies within each group becoming a significant HD severity classifier between HD subgroups. Next, we investigated the relationship between Lamin B1 expression, serving as nuclear envelope morphology marker, and cell motility finding that changes in Lamin B1 levels are associated with specific motility patterns within HD subgroups. Based on these data we present an accurate machine learning classifier offering comprehensive exploration of cellular migration patterns and disease severity markers for future accurate drug evaluation opening new opportunities for personalized treatment approaches in this challenging disorder.

A Dynamic Tool to Describe Lamb Growth and Its Use as a Decision Support System

A dynamic model has been developed to simulate aspects of feedlot lamb growth and body composition, including energy and protein requirements, growth rate, composition of gain, and body mass. Model inputs include initial body mass (kg), standard final mass (kg), age (days), and dietary energy concentration (Mcal·kg−1). The model was assessed as a decision support tool using a dataset of 564 individual measures of final body mass and diet energy. The simulations provide graphical and numerical descriptions of nutrient requirements, composition of gain, and estimates of animal performance over time. The model is accurate and precise, with a root mean squared error of 7.79% of the observed final body mass and a coefficient of determination of 0.89 when simulating the same variable. The model can be used as a reliable decision support tool to estimate final body mass and the days on feed required to reach a certain final mass with precision and accuracy. Moreover, the dynamic model can also serve as a learning tool to illustrate practical principles of animal nutrition, nutrient requirement relationships, and body composition changes. This model holds the potential to enhance livestock management practices and assist producers in making informed decisions about feedlot lamb production.

Media Disc Triage Assessment on Improving Triage Skills Using the Australasian Triage Scale (ATS) on Students of Kusuma Husada University Surakarta

ABSTRACT The Australasian Triage Scale (ATS) is a tool used by triage officers in the emergency department to assess patient severity and prioritize services based on the severity of the patient's condition. To evaluate the effectiveness of using learning tools in the form of Disc Triage Assessment as a learning medium in improving understanding and accuracy in determining triage by nursing students in using ATS triage. The Community Service method used is quasi-experimental with a pre-post test without control design. Community Service will be carried out on Nursing Study Program Students, Diploma Three Program, Faculty of Health Sciences, Kusuma Husada University. Data analysis was performed using the Wilcoxon test. The pretest results showed that the moderate category was 1 respondent (3.1%) and the poor category was 31 respondents (96.9%). The posttest results with the Good category were 3 respondents (9.4%) and the Fair category were 5 respondents (75.0%). The results of the use of Disc Triage Assessment media show that the p-value is 0.016 (<0.05) which means that there is an effect of Disc Triage Assessment Media on Skills to Determine Australasian Triage Scale (ATS) on Students of Kusuma Husada University Surakarta. Keywords: Media Disc Triage Assessment, Triage skills, Australasian Triage Scale (ATS)

Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction.

The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for the acceleration and undersampling of imaging data. Resulting reduced acquisition times would lead to greater machine utility and to greater cost-efficiency of machine operations. Our case shows images from magnetic resonance arthrography under traction of the right hip joint from a 30-year-old, otherwise healthy, male patient. The undersampled image data when reconstructed by a deep learning tool can contain false-positive cartilage delamination and false-positive diffuse cartilage defects. In the future, precision of this novel technology will have to be put to thorough testing. Bias of systems, in particular created by the choice of training data, will have to be part of those assessments.

The Diverse Development and Clinical Application of Game-Based Learning

The effectiveness of game-based learning strategies lies in the ability of these strategies to engage learners and enhance their motivation to learn. This is particularly important for today's younger generations, which are known to respond better to visual rather than textual information. Gamified education provides stimulating, realistic, and enjoyable learning experiences, helping students understand complex nursing knowledge and skills. The diversity of game-based learning tools, including based board games, escape room games, digital games, simulation games, mobile serious games, and virtual reality games, not only enhances students' learning effectiveness and skills but also improves their problem-solving abilities, communication skills, and ability to cope with various challenges in clinical care. In general, game-based learning is a strategy with great potential and importance. This strategy not only has profound implications for modern nursing education and clinical practice but also, through its promotion of innovative thinking and diversified applications, can effectively promote the learning motivation of nursing professionals, improve teaching effectiveness, and enhance professional abilities and self-directed learning capabilities. In an era in which medical knowledge is constantly evolving, game-based learning should be promoted and utilized to cultivate nursing professionals' capabilities effectively.