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Introduction: Self-learning is a learning process in which students harvest the enterprise, to express their learning goals, choose assets for learning, practice learning strategies, and assess the outcomes achieved. Many forms of self-learning were introduced in integrative medical curricula such as Team-based learning (TBL) and Problem-based learning (PBL). This study aims to evaluate self-learning in the otolaryngology module and determine the type of self-learning that students prefer and which of these types has a stronger impact on achieving the educational objectives of the module. Material and methods: A cross-sectional study was done on the 270 students of studied the otolaryngology module in three consecutive years representing the whole class of the fifth-year medical students along three consecutive years. A Likert scale questionnaire was distributed to measure the students' satisfaction with the current teaching and learning. Results: The obtained results revealed higher students 'satisfaction with TBL than other modalities supported by high achievement in TBL-related questions. In addition, there is a significant difference between TBL and PBL (p = .00044). No significant differences were obtained either between TBL and CBL (p = .16570) or between TBL and Seminar presentation (p = .16570). In addition, no significant correlations were obtained between PBL and CBL (p = .34677), between PBL and seminar presentation (p = .46496), and between CBL and seminar (p = .99967). Conclusion: The results showed that the highest students' satisfaction was towards TBL compared to other educational methods. These results encourage clinical educators to insert and implement TBL in most of the integrative curriculum modules, especially that of the clinical years.

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The health monitoring of CRF (circulation water) pumps is essential for prognostics and management in nuclear power plants. However, the operational status of CRF pumps can vary due to environmental factors and human intervention, and the interrelationships between monitoring parameters are often complex. Consequently, the existing methods face challenges in effectively assessing the health status of CRF pumps. In this study, we propose a health monitoring model for CRF pumps utilizing a meta graph transformer (MGT) observer. Initially, the meta graph transformer, a temporal-spatial graph learning model, is employed to predict trends across the various monitoring parameters of the CRF pump. Subsequently, a fault observer is constructed to generate early warnings of potential faults. The proposed model was validated using real data from CRF pumps in a nuclear power plant. The results demonstrate that the average Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) of normal predictions were reduced to 1.2385, 0.5614, and 2.6554, respectively. These findings indicate that our model achieves higher prediction accuracy compared to the existing methods and can provide fault warnings at least one week in advance.

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This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

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To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation. To this end, three different datasets were employed used for brain tumor and hippocampus delineation from MR images (BraTS and Decathlon datasets, respectively) and kidney segmentation from CT images (Decathlon dataset). Since data augmentation techniques are also aimed at enhancing the performance of deep learning methods, a deformable data augmentation technique was proposed and compared with contrastive learning and self-learning frameworks. The segmentation accuracy for the three datasets was assessed with and without applying data augmentation, contrastive learning, and self-learning to individually investigate the impact of these techniques. The self-learning and deformable data augmentation techniques exhibited comparable performance with Dice indices of 0.913 ± 0.030 and 0.920 ± 0.022 for kidney segmentation, 0.890 ± 0.035 and 0.898 ± 0.027 for hippocampus segmentation, and 0.891 ± 0.045 and 0.897 ± 0.040 for lesion segmentation, respectively. These two approaches significantly outperformed the contrastive learning and the original model with Dice indices of 0.871 ± 0.039 and 0.868 ± 0.042 for kidney segmentation, 0.872 ± 0.045 and 0.865 ± 0.048 for hippocampus segmentation, and 0.870 ± 0.049 and 0.860 ± 0.058 for lesion segmentation, respectively. The combination of self-learning with deformable data augmentation led to a robust segmentation model with no outliers in the outcomes. This work demonstrated the beneficial impact of self-learning and deformable data augmentation on organ and lesion segmentation, where no additional training datasets are needed.

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Background: Student-centered learning strategy increases the likelihood of graduation of competent, self-dependent, and problem-solving physicians. The University of Bisha, College of Medicine (UBCOM) adopted self-directed learning (SDL) represented by problem-based learning (PBL), and directed self-learning (DSL) represented by team-based learning (TBL). Aim: To compare the students' performance in SDL and DSL among UBCOM students. Methodology: A total of 502 multiple choice questions (MCQs) from the mid-course and final exams were collected by the relevant subject experts from nine courses during the period from September 2020 till June 2023 that adopted PBL and TBL; 247 MCQs related to PBL and 255 related to TBL. Psychometric analysis was used to determine difficult, easy, and optimum questions (≤25%, ≥90%, and 26-89%, respectively). Point biserial as <0.19, 0.20-0.29, 0.30-0.39, and >0.40 which indicate poor, marginal, good, and excellent point biserial, respectively. Finally, the number of functional distractors was attempted by >5% of the candidates. Results: No significant differences were noted for the students' performance in MCQs related to PBL (representing self-directed, small group learning tool), and TBL (representing directed-self, large group learning tool) regarding difficulty index (DI), point biserial, and distractors functionality. Conclusion: It has been observed that there is no difference in students' performance whether PBL or TBL is used for learning Basic Medical Science courses. Small group learning such as PBL needs more resources in comparison to large group learning as in TBL, therefore any institute can decide on the adopted learning strategy depending on its resources and the number of students.

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PURPOSE: Ultrasound is becoming an essential tool for hand surgeons, but most of them are trained on the job, without any diploma or dedicated training. The aim of this study was to assess the ability of hand surgeons new to ultrasound to identify hand and wrist anatomical structures. METHODS: A monocentric study was conducted from January 2022 to April 2022. Ten residents and five attending hand surgeons, ultrasound novices, were involved in this study. The participants underwent two tests, wherein they were required to identify 17 anatomical structures using ultrasound, on the same subject. The second test was similar and carried out 2 to 6 weeks later by all participants. The number of structures successfully identified and if it was the case, the detection time per structure, were recorded. The correlations between participants age, years of surgical experience, surgical background (orthopedic or plastic) and the ability to perform immediately during the first test or to progress between the two tests were also assessed. RESULTS: The average number of structures identified during the first test (T1) was 14.1+/-2.1 (82.9%), versus 16.2+/-0.8 (95.3%) structures during the second test (T2) (p = 0.001). The mean detection time per structure was 53.4 +/- 18.9 s during T1 versus 27.7 +/- 7.2 s during T2 (p < 0.0001). A moderate negative correlation between the progression in the number of anatomical structures identified between the two tests and the years of surgical experience (ρ=-0.56; p = 0.029) was found. The other parameters were neither correlated with the ability to perform at the first test nor with the progression between the two tests. CONCLUSION: Hand surgeons new to ultrasound are most of the time able to identify hand and wrist anatomical structures. Comparison of their first and second tests showed significant potential for improvement in anatomical structure identification and detection time of those, especially in surgeons with limited surgical experience.

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BACKGROUND: Self-directed learning (SDL) is an essential aspect of adult education or andragogy, gaining significance in medical education with the introduction of competency-based medical education. The primary objective of this study is to assess the self-directed learning abilities of second-year medical undergraduates in Chennai, South India, and to identify potential challenges and gaps in their learning process. MATERIALS AND METHODS: A cross-sectional study was conducted among 82 second-year medical students attending self-directed learning sessions at a medical college in Chennai. Data were collected using the self-directed learning instrument (SDLI), a standardized questionnaire, administered through Google Forms. Participants' identities were maintained confidential. Data were analyzed using SPSS version 22.0. Descriptive data were presented as proportions and percentages. Normally distributed quantitative data were expressed as mean and standard deviation. Non-normal continuous data were expressed as median and interquartile range (IQR). RESULTS: The majority of the students (61%) demonstrated a high level of SDL ability, with a median score of 76. Students exhibited strong learning motivation (mean score 4.11) but struggled with planning and implementation (mean score 3.07). The maximum mean score was 4.11 for item 3 (constant improvement and excelling in learning), and the minimum mean score was 3.07 for item 11 (arranging and controlling learning time). The students showed high self-monitoring (mean score 3.76) and interpersonal communication skills (mean score 4.00). CONCLUSIONS: SDL emerges as a boon for medical undergraduates in this study. By providing adequate training to faculty members on SDL implementation and guidance to students on planning and time management, SDL can play a pivotal role in enhancing medical education quality and fostering life-long learning among future medical professionals.

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The grey wolf optimization algorithm (GWO) is a new metaheuristic algorithm. The GWO has the advantages of simple structure, few parameters to adjust, and high efficiency, and has been applied in various optimization problems. However, the orginal GWO search process is guided entirely by the best three wolves, resulting in low population diversity, susceptibility to local optima, slow convergence rate, and imbalance in development and exploration. In order to address these shortcomings, this paper proposes an adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO). First, the convergence factor was segmented and nonlinearized to balance the global search and local search of the algorithm and improve the convergence rate. Second, the wolves in the original GWO approach the leader in a straight line, which is too simple and ignores a lot of information on the path. Therefore, a dynamic logarithmic spiral that nonlinearly decreases with the number of iterations was introduced to expand the search range of the algorithm in the early stage and enhance local development in the later stage. Then, the fixed step size in the original GWO can lead to algorithm oscillations and an inability to escape local optima. A dynamic self-learning step size was designed to help the algorithm escape from local optima and prevent oscillations by reasonably learning the current evolution success rate and iteration count. Finally, the original GWO has low population diversity, which makes the algorithm highly susceptible to becoming trapped in local optima. A novel position update strategy was proposed, using the global optimum and randomly generated positions as learning samples, and dynamically controlling the influence of learning samples to increase population diversity and avoid premature convergence of the algorithm. Through comparison with traditional algorithms, such as GWO, PSO, WOA, and the new variant algorithms EOGWO and SOGWO on 23 classical test functions, ASGWO can effectively improve the convergence accuracy and convergence speed, and has a strong ability to escape from local optima. In addition, ASGWO also has good performance in engineering problems (gear train problem, ressure vessel problem, car crashworthiness problem) and feature selection.

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We conducted a prospective, single arm, multisite, multinational, open label trial assessing the safety and efficacy of a novel amygdala derived neurofeedback treatment, designated Amygdala-Derived-EFP, for chronic PTSD. Participants, including veterans and civilians, underwent screening, training, 15 neurofeedback sessions over 8 weeks and; baseline, termination (8 weeks) and 3 month post treatment assessments with validated measures. The primary endpoint was more than 50 % of the participants demonstrating a Minimally Clinically Important Difference (MCID) defined as a 6-point reduction, on the Clinician Administered PTSD Scale (CAPS-5) total score at 3 months. Secondary measures included the PCL-5, ERQ, PHQ-9, and CGI. Statistical analyses were performed using SAS®V9.4. The primary endpoint was met, with a CAPS-5 MCID response rate of 66.7 %. The average reduction in CAPS-5 total scores at 3 month follow up was 13.5 points, more than twice the MCID. Changes from baseline in CAPS-5, PCL-5, PHQ-9 scores at 8 weeks and the 3 month follow-up demonstrated statistically significant improvements in response and; demonstrated effect sizes ranging from 0.46 to 1.07. Adverse events were mild and resolved after treatment. This study builds on prior research demonstrating similar outcomes using amygdala-derived neurofeedback. Positive attributes of this therapy include monitoring by non-physician personnel, affordability, accessibility, and tolerability.

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OBJECTIVES: This study aimed to elucidate effective methodologies for utilizing the generative artificial intelligence (AI) system, namely the Chat Generative Pre-trained Transformer (ChatGPT), in improving clinical reasoning abilities among clinicians. METHODS: We conducted a comprehensive exploration of the capabilities of ChatGPT, emphasizing two main areas: (1) efficient utilization of ChatGPT, with a focus on application and language selection, input methodology, and output verification; and (2) specific strategies to bolster clinical reasoning using ChatGPT, including self-learning via simulated clinical case creation and engagement with published case reports. RESULTS: Effective AI-based clinical reasoning development requires a clear delineation of both system roles and user needs. All outputs from the system necessitate rigorous verification against credible medical resources. When used in self-learning scenarios, capabilities of ChatGPT in clinical case creation notably enhanced disease comprehension. CONCLUSIONS: The efficient use of generative AIs, as exemplified by ChatGPT, can impressively enhance clinical reasoning among medical professionals. Adopting these cutting-edge tools promises a bright future for continuous advancements in clinicians' diagnostic skills, heralding a transformative era in digital healthcare.

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BACKGROUND: Although low-dose computed tomography (CT) imaging has been more widely adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT images tend to have more noise, which impedes accurate diagnosis. Recently, deep neural networks using convolutional neural networks to reduce noise in the reconstructed low-dose CT images have shown considerable improvement. However, they need a large number of paired normal- and low-dose CT images to fully train the network via supervised learning methods. PURPOSE: To propose an unsupervised two-step training framework for image denoising that uses low-dose CT images of one dataset and unpaired high-dose CT images from another dataset. METHODS: Our proposed framework trains the denoising network in two steps. In the first training step, we train the network using 3D volumes of CT images and predict the center CT slice from them. This pre-trained network is used in the second training step to train the denoising network and is combined with the memory-efficient denoising generative adversarial network (DenoisingGAN), which further enhances both objective and perceptual quality. RESULTS: The experimental results on phantom and clinical datasets show superior performance over the existing traditional machine learning and self-supervised deep learning methods, and the results are comparable to the fully supervised learning methods. CONCLUSIONS: We proposed a new unsupervised learning framework for low-dose CT denoising, convincingly improving noisy CT images from both objective and perceptual quality perspectives. Because our denoising framework does not require physics-based noise models or system-dependent assumptions, our proposed method can be easily reproduced; consequently, it can also be generally applicable to various CT scanners or dose levels.

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Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning fashion. We propose a new online test-time method, called TestFit, to improve results of a given off-the-shelf DL segmentation model in test time by actively fitting the test data distribution. TestFit first creates a supplementary network (SuppNet) from the given trained off-the-shelf segmentation network (this original network is referred to as OGNet) and applies SuppNet together with OGNet for test time inference. OGNet keeps its hypothesis derived from the original training set to prevent the model from collapsing, while SuppNet seeks to fit the test data distribution. Segmentation results and supervision signals (for updating SuppNet) are generated by combining the outputs of OGNet and SuppNet on the fly. TestFit needs only one pass on each test sample - the same as the traditional test model pipeline - and requires no training time preparation. Since it is challenging to look at only one test sample and no manual annotation for model update each time, we develop a series of technical treatments for improving the stability and effectiveness of our proposed online test-time training method. TestFit works in a plug-and-play fashion, requires minimal hyper-parameter tuning, and is easy to use in practice. Experiments on a large collection of 2D and 3D datasets demonstrate the capability of our TestFit method.

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Background: Histology forms an important component of first-year medical education. Unfortunately, it is limited to the practical laboratory due to the need for a microscope and good quality slides. Virtual microscopy is a recent advancement, which uses computers as an alternative to microscopes. The aim of the study was to compare virtual microscopy (VM)-based practical classes with traditional microscopy (TM)-based practical classes for two cohorts of first-year medical students, by comparing learning achieved using two different test scores as well as a qualitative assessment of student and faculty perspectives regarding the feasibility and usefulness of VM. Methods: Each cohort of students was divided into two equal batches and each batch underwent eight histology modules of which, four utilised traditional microscopes and four utilised virtual microscopes. Quantitative analysis was performed using a theory test (which assessed preparation, theory knowledge and understanding) as well as a spotter test (which assessed identification skills, reasoning, and recall). Qualitative analysis was performed using a structured questionnaire and focus group discussions. Results: Modules using VM were better when compared with those using TM, showing statistically significant and better grades. Qualitative analysis performed, yielded important information as to how this technology can serve as a good adjunct to traditional histology classes in the competency-based curriculum by increasing student interest, enabling self-study, and reducing students dependence on the tutor. Conclusions: VM forms a good adjunct as well as a standalone modality of learning to TM, as it improves accessibility to slides and promotes self-learning.

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BACKGROUND: The Richmond Agitation-Sedation Scale - Palliative version (RASS-PAL) tool is a brief observational tool to quantify a patient's level of agitation or sedation. The objective of this study was to implement the RASS-PAL tool on an inpatient palliative care unit and evaluate the implementation process. METHODS: Quality improvement implementation project using a short online RASS-PAL self-learning module and point-of-care tool. Participants were staff working on a 31-bed inpatient palliative care unit who completed the RASS-PAL self-learning module and online evaluation survey. RESULTS: The self-learning module was completed by 49/50 (98%) of regular palliative care unit staff (nurses, physicians, allied health, and other palliative care unit staff). The completion rate of the self-learning module by both regular and casual palliative care unit staff was 63/77 (82%). The follow-up online evaluation survey was completed by 23/50 (46%) of respondents who regularly worked on the palliative care unit. Respondents agreed (14/26; 54%) or strongly agreed (10/26; 38%) that the self-learning module was implemented successfully, with 100% agreement that it was effective for their educational needs. CONCLUSION: Using an online self-learning module is an effective method to engage and educate interprofessional staff on the RASS-PAL tool as part of an implementation strategy.

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BACKGROUND: To develop doctors with appropriate knowledge of health and diseases, reasonable medical abilities, and a positive attitude toward patients and their families, it is important to reexamine the methods used to educate and train medical school students. To establish which is best for both medical students and professors, the various teaching and learning methodologies must be compared and analyzed. This study attempts to determine the preferred medical education techniques among medical students as well as the caliber of the classes they attend. METHODS: This is a before-and-after study conducted among 480 first- (240) and second-year (240) undergraduate students. Students were divided into three groups. Each group was assigned a teacher who was responsible for teaching four short topics according to the common understanding and knowledge level of both year students in four different ways: traditional blackboard method, offline PowerPoint presentation, online PowerPoint presentation, and online annotative. Application-based learning and self-learning were the other two teaching methods conducted in a monitored environment. An MCQ-based pre- and post-test were taken to assess the improvement, and a feedback form was filled out by each student to assess their perception. To assess long-term retention, a surprise follow-up test was conducted after 15 days. RESULTS: For all the teaching methods except for traditional blackboard and online presentation, there was a significant improvement in the post-test scores as compared to the pre-test scores (p<0.05). Retentivity was more remarkable in online application-based and self-learning methods. 77.2% of the study participants preferred offline presentation as the mode of teaching. CONCLUSION: Retention was found to be highest in self-directed and application-based learning. So, students should be encouraged and motivated for self-study after every lecture, whatever the teaching method used by teachers.

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The sports world has many prejudices that have been converted into common sense. Some relate to the idea of athletes being strong or pretty but endowed with little intelligence. There is another view, perhaps a little more accurate, around the reification of consciousness in the name of the automation and maximum outcome of the body. Both views are informed by Cartesian thinking, perpetuating the mind-body dichotomy. Such a dichotomy is spread in several other areas in our society, expressed as binaries. We meet a binary when conducting research as well, disembodying the researcher as someone who is neutral, objective, and highly rational, and someone who, in synthesis, performs good mental work, but who must not let feelings intrude. On the contrary, we argue that we are embodied beings who are often not able to (and maybe should not) become detached from previous experiences and knowledge when conducting research. Even though this can present itself as a challenge, we consider that a fluid non-binary positioning encompasses actions holistically and leads to tasks being performed on a continuum. The purpose of this paper is to explore the reflexive process embedded in carrying out a PhD project committed to studying the production of the embodied subjectivities of a group of women high-level athletes in karate. The researcher inserted in the researched environment was not a high-level athlete; however, she had several experiences competing at the amateur level in different countries and faced experiences that were, to some extent, similar to those of the elite athletes. She used her previous experiences as a karateka, researcher, and woman to inform her research-doing since the intersectional social issues faced by her and lived queer feelings motivated her research questions. She plunged into a process of self-reflection and counted on the guidance of the other authors to organise her learning in order to use it in her scholarship. That was, primarily, an experience of "practice" of subjectivity through examining others' production of subjectivity, besides strengthening a positionality that lacked self-confidence. Thus, we explore issues around the researcher-practitioner theoretical-practical continuum of research-doing, presenting a journey that became empowering.

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To suppress inertial navigation system drift and improve the seamless navigation capability of microelectromechanical system-inertial navigation systems/geomagnetic navigation systems (MEMS-INS/MNS) in geomagnetically unlocked environments, this paper proposes a hybrid seamless MEMS-INS/MNS strategy combining a strongly tracked square-root cubature Kalman filter with deep self-learning (DSL-STSRCKF). The proposed DSL-STSRCKF method consists of two innovative steps: (i) The relationship between the deep Kalman filter gain and the optimal estimation is established. In this paper, combining the two auxiliary methods of strong tracking filtering and square-root filtering based on singular value decomposition, the heading accuracy error of ST-SRCKF can reach 1.29°, which improves the heading accuracy by 90.10% and 9.20% compared to the traditional single INS and the traditional integrated navigation algorithm and greatly improves the robustness and computational efficiency. (ii) Providing deep self-learning capability for the ST-SRCKF by introducing a nonlinear autoregressive neural network (NARX) with exogenous inputs, which means that the heading accuracy can still reach 1.33° even during the MNS lockout period, and the heading accuracy can be improved by 89.80% compared with the single INS, realizing the continuous high-precision navigation estimation.

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BACKGROUND AND PURPOSE: Self-assessment and self-learning are essential skills for student pharmacists. Data demonstrating the association between these skills in pharmacy courses are limited. The aim of this study was to evaluate the impact of providing pre-course review and administering a pre-course assessment on performance in two required integrated pharmacotherapy (IP) courses - IP: Pulmonology and IP: Cardiology. EDUCATIONAL ACTIVITY AND SETTING: This study included second-year student pharmacists enrolled in fall semester IP: Pulmonology and IP: Cardiology from 2019 to 2021. Voluntary pre-course review materials and pre-course assessments were added in fall 2021. Overall course grades and examination scores between each year were analyzed. Student perceptions of the pre-course assessment were also captured. FINDINGS: Of the 454 students analyzed, there was no difference in median overall IP: Pulmonology grades (85.93%, 86.67%, 86.29%; P = .63) or IP: Cardiology grades (80.25%, 78.3%, 79.96%; P = .41) for 2019, 2020, and 2021, respectively. IP: Pulmonology Exam 1 scores were statistically higher in 2021. For IP: Cardiology, Exam 1 and Final Exam scores were statistically higher in 2020 compared to 2019 and Exam 3 scores were significantly higher in 2021 than 2019. Pre-course assessment scores had a statistically significant, positive association with overall course grade. Half of the students surveyed agreed that completing the course prep work was an effective approach to learning. SUMMARY: Although overall course grades did not differ between years, pre-course assessment scores correlated with overall course grade. Thus, voluntary pre-course assessments could provide early identification of poor performance.

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Introducción: El Sistema de Créditos Transferibles fue desarrollado para traducir la carga de trabajo de los estudiantes en créditos académicos que son reconocidos en todos los países, apuntando al logro del aprendizaje. El aprendizaje autónomo es un objetivo que la mayoría de los programas educativos promueven como una opción estratégica para conectar la profesión, el entorno de estudio y las expectativas profesionales. Objetivo: Analizar las horas de trabajo autónomo utilizadas por los estudiantes para lograr los resultados de aprendizaje determinados en los programas de asignaturas, su efectividad en cuanto al rendimiento académico y su correspondencia con lo establecido en el plan de estudio, ajustado al Sistema de Créditos Transferibles. Métodos: Se realizó un análisis descriptivo transversal retrospectivo a partir de datos de un registro manual de estudiantes (n = 54) y docentes (n = 6) respecto a seis asignaturas de primer nivel de la Carrera de Odontología de la Universidad Autónoma de Chile, sede Temuco. Resultados: Los análisis revelan una incoherencia entre las horas de trabajo autónomo utilizadas por los estudiantes con respecto a lo establecido en el plan de estudio y las horas de trabajo extra-aula estimadas por los profesores. Conclusión: Se concluye que la implementación del Sistema de Créditos Transferibles por sí sola no asegura una mejora en el desempeño de los estudiantes, requiriendo revisar el procedimiento institucional para definirlas; por parte de los docentes una mayor apropiación de los resultados de aprendizajes y la didáctica necesaria para orientar a los estudiantes a obtener un mayor rendimiento del trabajo autónomo, por otro lado, los estudiantes deben ser responsables del uso consciente de dichas horas(AU)

Introduction: The Transferable Credit System was developed to translate student workload into academic credits that are recognized in all countries, aiming at learning achievement. Autonomous learning is an objective that most educational programs promote as a strategic option to connect career, study environment and professional expectations. Objective: Analyzing the hours of autonomous work used by students to achieve the learning outcomes determined in the subject programs, their effectiveness in terms of academic performance and their correspondence with what is established in the study plan, adjusted to the Transferable Credit System. Methods: A retrospective cross-sectional descriptive analysis was carried out using data from a manual record of students (n= 54) and teachers (n= 6) regarding six first level subjects of the Dentistry course of the Universidad Autónoma de Chile, Temuco campus. Results: The analysis revealed an incoherence between the hours of autonomous work used by the students with respect to what is established in the study plan and the hours of extra-classroom work estimated by the professors. Conclusion: It is concluded that the implementation of the Transferable Credit System alone does not ensure an improvement in student performance, requiring a review of the institutional procedure to define them; on the part of teachers a greater appropriation of the learning outcomes and the didactics necessary to guide students to obtain a higher yield of autonomous work, on the other hand, students must be responsible for the conscious use of these hours(AU)

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Introducción: Desde hace unos años, tanto en grupos grandes como pequeños, y principalmente en clases en línea, se hapuesto en práctica la metodología ‘SLIDE-4-U’ o ‘una diapositiva para ti’ (2020PID-UB/023), con el objetivo de implicar alestudiante en su propio proceso de aprendizaje y en el de sus compañeros. Se consiguió mediante la participación delalumnado en la explicación en clase de diapositivas específicamente diseñadas para este fin. Métodos: La experiencia se llevó a cabo en el primer semestre del curso 2021-22 en la asignatura Nutrición Molecular delgrado de Nutrición Humana y Dietética (Universitat de Barcelona). Se preparó una sesión de seminario presencial centrada en inmunonutrición. El profesor dirigió la sesión seleccionando de forma aleatoria al estudiante, que debía explicar ladiapositiva sin preparación previa. Las explicaciones del alumnado fueron complementadas o corregidas por el profesordurante el desarrollo de la actividad. Al final del seminario se realizó una encuesta de opinión en la que se constató labuena aceptación de esta iniciativa (puntuaciones medias superiores a 4,2 sobre 5). Resultados: El alumnado consideró que era un reto explicar una diapositiva sin prepararla previamente y que este hecho,asociado a no saber quién haría la explicación, había provocado un cierto clima de nerviosismo. Ahora bien, la mayoríaestaba de acuerdo en que los esquemas/imágenes aportados fueron suficientes para poder desarrollar la actividad y quelas explicaciones hechas por los compañeros eran suficientemente correctas. Asimismo, también valoraban positivamente la participación del profesor a la hora de completar las explicaciones de sus compañeros. En general, la metodologíautilizada hizo que el alumnado fuera más consciente de que las diapositivas tienen una estructura y un objetivo, y de ladificultad de comunicar correctamente...(AU)

Introduction: Lately, both in large and small groups and mainly in online classes, the 'SLIDE-4-U' or 'one slide for you' methodology (2020PID-UB/023) has been put into practice, with the aim of involving the student in their own learning process and that of their classmates. It is achieved through the participation of the students in the explanation of slides in class, specially designed for this purpose. Methods: The experience was carried out in the first semester of the 2021-22 academic year in the subject Molecular Nutrition of the Human Nutrition and Dietetics degree (Universitat de Barcelona). A face-to-face seminar session focused on immunonutrition was prepared with this type of material. The teacher led the session by randomly selecting the student, who had to explain the slide without prior preparation. The explanations of the students were complemented and/or corrected by the teacher, during the development of the activity. At the end of the seminar, an opinion survey was carried out in which the good acceptance of this initiative was verified (average scores higher than 4.2 out of 5). Results: The students considered that it was a challenge to explain a slide without previously preparing it, and that this fact, associated with not knowing who would do the explanation, had caused a certain climate of nervousness. However, the majority agreed that the diagrams/images provided were sufficient to be able to carry out the activity and that the explanations made by the classmates were correct enough. Likewise, they also positively valued the teacher's participation when completing the explanations of their classmates. In general, the methodology used made the students more aware that the slides have a structure and an objective, and of the difficulty of communicating correctly...(AU)

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