Flipped and Peer-Assisted teaching: a new model in virtual anatomy education.

INTRODUCTION: In response to the COVID-19 crisis, this study aimed to introduce a new virtual teaching model for anatomy education that combines Peer-Assisted Learning (PAL) and flipped classrooms, aligning with constructivist principles. METHOD: The Flipped Peer Assisted (FPA) method was implemented in a virtual neuroanatomy course for second-year medical students at Birjand University of Medical Sciences via a descriptive study. The method involved small groups of PAL, with peer learning serving as educational assistants and the teacher acting as a facilitator. Educational content was uploaded to the university's learning management system (LMS). The opinion of medical students regarding the teaching method were evaluated using a 15-item questionnaire on a five-point Likert scale. RESULTS: A total of 210 students participated in the instruction using the FPA method. The analysis of students' scores revealed an average score of 26.75 ± 3.67 on the 30-point test. According to student feedback, this teaching method effectively motivated students to study, enhanced teamwork and communication skills, transformed their perspective on the anatomy course, provided opportunities for formative assessment and feedback, and demonstrated the teacher's dedication to education. CONCLUSION: The FPA model demonstrates its effectiveness in transforming traditional classroom teaching and fostering teaching and learning in virtual environments, particularly during pandemics like COVID-19. This model holds promise for enhancing anatomy education in challenging circumstances.

Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review.

Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.

An active-learning laboratory on urinary incontinence products for pharmacy students.

BACKGROUND AND PURPOSE: To describe an active-learning laboratory on urinary incontinence (UI) and its effect on students' confidence and comfort in addressing UI. EDUCATIONAL ACTIVITY AND SETTING: Second year pharmacy students (n = 98) participated in an active-learning laboratory focused on UI with four components: catheter lecture and demonstration, UI product overview, hands-on practice with UI absorbent products, and a debrief on the activity focused on difficult conversations. Students completed an optional retrospective pre-post survey at the end of the laboratory including five confidence questions, ranking of activities in the laboratory, and open-ended responses on how to change the activity as well as what was one takeaway from the debrief. Descriptive statistics assessed survey responses. Changes in student confidence were assessed using paired t-tests. Thematic analysis was used for the open-ended debrief question. FINDINGS: Of the 101 students who participated in the laboratory, 98 students completed the pre/post-survey (response rate: 97%). Students demonstrated a significant increase in their confidence in all five areas assessed. The hands-on activity with the absorbent products was rated as the most useful activity. The themes from the debrief on difficult conversations included: self-awareness, expanding viewpoints, cultural sensitivity, and professional duty. Student feedback on the UI active-learning laboratory was largely positive, with most students suggesting no changes (n = 75) to the activity. SUMMARY: An active-learning laboratory on UI helped improve confidence and was well received by pharmacy students.

How Does Active Learning Pedagogy Shape Learner Curiosity? A Multi-Site Mediator Study of Learner Engagement among 45,972 Children.

Curiosity is one of the most fundamental biological drives that stimulates individuals' intense desire to explore, learn, and create. Yet, mechanisms of how curiosity is influenced by instructional pedagogy remain unclear. To shed light on this gap, the present study sets out to investigate the underlying channels linking active learning pedagogy, learner engagement, and learner curiosity, employing a partial least-squares structural equation model leveraging the Social and Emotional Skills Survey dataset across ten sites (N = 45,972). Findings indicate that active learning pedagogy is positively associated with learner engagement (std. ß = 0.016, p = 0.005), but there lacks a significant direct effect on learner curiosity (std. ß = -0.001, p = 0.738). Structural mediation results show that learner engagement is a key mediating channel linking active learning pedagogy and learner curiosity (std. ß = 0.013, p = 0.005).

The use of role-play in the learning of medical terminology for online and face-to-face courses.

Student engagement while learning a new, unfamiliar vocabulary is challenging in health science courses. A group role-play activity was created to teach students medical terminology and learn why its correct usage is important. This activity brought engagement and relevance to a topic traditionally taught through lecture and rote memorization and led to the development of an undergraduate and a stand-alone introductory course to teach students medical terminology. The undergraduate course was designed to be a fully online medical terminology course for health science students and a face-to-face course for first-year dental students founded in active learning and group work. The course's centerpiece learning activity focused on using published case studies with role-play. In this group activity, students are challenged to interpret a published patient case study as one of the members of a healthcare team. This course models the group work inherent in modern health care to practice building community and practicing professional skills. This approach gives students the capacity to work asynchronously in a team-based approach using our learning management system's wiki tool and requires students to take responsibility for their learning and group dynamics. Students practice identification, writing, analyzing, and speaking medical terms while rotating through the roles. Students in both classes self-reported a 92% to 99% strong or somewhat agreement using a five-point Likert scale that the course pedagogy was valued and helpful in their learning of medical terminology. Overall, this method has proven to be an engaging way for students to learn medical terminology.NEW & NOTEWORTHY Role-play can engage students and encourage learning in identification, pronouncing, writing, and understanding medical terminology in multiple course formats.

Developing an Evidence-Based Ethics Education Program for Surgical Residents in Guatemala.

OBJECTIVE: This study assessed 2 modalities for teaching responsible conduct of research and human subjects protection (RCR/HSP) to surgical residents in Guatemala-an "off the shelf" online curriculum and a new in-person curriculum specific to the local context. METHODS: In 2018, 160 surgical residents in 3 large urban hospitals in Guatemala City completed 2 online programs in RCR/HSP. Residents in the intervention arm also completed 7 weeks of in-person training. Pre- and post-assessments tested awareness of key concepts with particular attention to international and Guatemalan research regulations. Group differences in matched (pre- and post-) mean scores were analyzed using t-tests. RESULTS: One hundred forty residents completed pre- and post-training assessments and were included in the analytic sample. Overall mean scores improved modestly from 52.7 to 58.7 points out of 100. Intervention-arm trainees reported greater confidence in recognizing ethical issues, understanding legal and ethical requirements for research, and identifying, reporting and avoiding scientific misconduct than control-arm trainees. CONCLUSION: Given the limited availability of RCR/HSP faculty, financial resources, and time in the surgical training schedule, the investigators recommend that academic authorities in Guatemala consider online training programs in RCR/HSP in all surgical residency programs as an affordable and scalable strategy to build ethical research skills in its surgical workforce. Investment in human resources to support in-person ethics education as a way to build self-efficacy in ethical decision-making should be considered.

Facilitating active learning of sectional anatomy with technology-enhanced small-group tasks: Assessment of knowledge gains, technology usability, and students' perceptions.

Learning 2D sectional anatomy facilitates the comprehension of 3D anatomical structures, anatomical relationships, and radiological anatomy. However, the efficacy of technology-enhanced collaborative instructional activities in sectional anatomy remains unclear, especially if theoretical frameworks, namely the Cognitive Theory of Multimedia Learning (CTML), are applied in instructional design. Thus, this study compared the educational impact of distinct 45-min-long technology-enhanced collaborative learning tasks in sectional anatomy. A sample of 115 first-year medical students was randomly divided into three experimental groups that used different supporting technologies to learn the sectional anatomy of the chest: IMAIOS e-learning platform and Microsoft Surface Hub (n = 37); anatomage table (n = 38); anatomage table with CTML-based presets (n = 40). Prelearning and postlearning tests revealed that significant knowledge gains in sectional anatomy were obtained by all groups even though no inter-group differences were found. Moreover, a five-point Likert scale questionnaire showed that the learning session was highly valued by all participants and that users of the anatomage with CTML-based presets reported higher enjoyment than users of the IMAIOS system (mean difference = 0.400; p = 0.037). In addition, students using the IMAIOS system and the anatomage with CTML-based presets provided System Usability Scale (SUS) scores of 67.64 and 67.69, respectively, reaching the benchmark of usability. By contrast, students using the anatomage table without presets awarded a SUS score of 64.14. These results suggest that the integration of multimedia technologies in anatomy teaching and learning should be grounded on CTML principles of instructional design. Otherwise, students' perceptions of ed-tech usability are potentially hindered.

Using Modern Learning Method to Teach Pharmacy Students Psychopharmacotherapy.

Background: Applying modern educational methods for digital native students seems necessary. Active learning strategies promote students' skills and knowledge. This study was conducted to design and evaluate active learning methods by teaching psychopharmacotherapy to pharmacy students. Methods: This was a quasi-experimental study with three randomized study groups (control, game, and multimedia), using a pre-and post-test design, conducted on 155 students of 5-year pharmacy in 2022 at the Faculty of Pharmacy of Tehran University of Medical Sciences, Iran. Overall, 18 clinical cases were designed for the basic structure of interventions. After teaching psychopharmacotherapy contents through lecturing, the pre-test was held. The next steps were playing the educational game, studying the multimedia case-based learning files, and then completing questionnaires, respectively. Then, a post-test was held. Results: 65.33% of participants were female and 34.66% were male. The pre-test and post-test scores comparison showed no difference in control group (P=0.409). However, in the serious game and multimedia groups, the average score of pre-test and post-test had a statistically significant difference (P<0.001, P=0.002 respectively), this difference was higher in the serious game group. Questionnaire evaluation showed substantial differences between game and multimedia groups. Conclusion: The educational interventions were able to improve student's knowledge and skills so they can better help patients and promote public health. In the sections of Confidence, Social Interactions, Fun, Focused attention, Learnability, Relevance, and Perceived Learning, the serious game far outweighed the multimedia case-based learning.

Exploring dental and oral hygiene students' interprofessional readiness: a cross-sectional study in joint paediatric outreach training.

BACKGROUND: Interprofessional education is vital in oral healthcare education and should be integrated into both theoretical and work-based education. Little research addresses interprofessional education in dental hands-on training in authentic oral healthcare settings. The aim of the study was to examine the readiness and attitudes of dental and oral hygiene students towards interprofessional education during joint paediatric outreach training. METHODS: In the spring of 2022, a cross-sectional study was done involving dental and oral hygiene students using the Readiness for Interprofessional Learning Scale (RIPLS) during joint paediatric outreach training. The 19-item tool was answered on a five-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree). Means, standard deviations, minimums, maximums, and medians were calculated for each subscale and overall score. Students grouped according to their categorical variables were compared for statistically significant differences. The Mann-Whitney U-test was used for groups of two and the Kruskal-Wallis one-way analysis for groups of three or more. The internal consistency of the scale was measured with Cronbach's alpha. Statistical level was set at 0.05. RESULTS: The survey included 111 participants, consisting of 51 oral hygiene students and 60 dental students, with a response rate of 93%. The questionnaire yielded a high overall mean score of 4.2. Both oral hygiene (4.3) and dental students (4.2) displayed strong readiness for interprofessional education measured by the RIPLS. The subscale of teamwork and collaboration achieved the highest score of 4.5. Students lacking prior healthcare education or work experience obtained higher RIPLS scores. Oral hygiene students rated overall items (p = 0.019) and the subscales of positive professional identity (p = < 0.001) and roles and responsibilities (p = 0.038) higher than dental students. The Cronbach's alpha represented high internal consistency for overall RIPLS scores on the scale (0.812). CONCLUSIONS: Both oral hygiene and dental students perceived shared learning as beneficial and showcased high readiness for interprofessional education, as evident in their RIPLS scores. Integrating interprofessional learning into oral hygiene and dental curricula is important. Studying together can form a good basis for future working life collaboration.

Cost-effectiveness Analysis of Colorectal Cancer Screening Strategies Using Active Learning and Monte Carlo Simulation.

INTRODUCTION: Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies. METHODS: We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search. RESULTS: Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior. CONCLUSION: Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective. HIGHLIGHTS: We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.

Semi-supervised active learning using convolutional auto- encoder and contrastive learning.

Active learning is a field of machine learning that seeks to find the most efficient labels to annotate with a given budget, particularly in cases where obtaining labeled data is expensive or infeasible. This is becoming increasingly important with the growing success of learning-based methods, which often require large amounts of labeled data. Computer vision is one area where active learning has shown promise in tasks such as image classification, semantic segmentation, and object detection. In this research, we propose a pool-based semi-supervised active learning method for image classification that takes advantage of both labeled and unlabeled data. Many active learning approaches do not utilize unlabeled data, but we believe that incorporating these data can improve performance. To address this issue, our method involves several steps. First, we cluster the latent space of a pre-trained convolutional autoencoder. Then, we use a proposed clustering contrastive loss to strengthen the latent space's clustering while using a small amount of labeled data. Finally, we query the samples with the highest uncertainty to annotate with an oracle. We repeat this process until the end of the given budget. Our method is effective when the number of annotated samples is small, and we have validated its effectiveness through experiments on benchmark datasets. Our empirical results demonstrate the power of our method for image classification tasks in accuracy terms.

Using origami and Shrinky Dinks to create active learning activities to tackle two microbiology concepts: cell structure differences and operon regulation.

This paper presents two low-cost hands-on activities designed to enhance student understanding and address the pedagogical challenges faced by microbiology professors in teaching concepts related to cell structure and gene regulation. In the first activity, we used Shrinky Dinks and Jeopardy-style game questions to explore the differences between prokaryotic and eukaryotic cells. Students have to collect pieces and physically build their cell models. The second activity uses origami organelles sets from Edvotek to illustrate the regulation of gene expression in the lac and trp operons, incorporating mutation scenarios for analysis. The intended audience comprises undergraduate students in microbiology, including biology, pre-medical studies, and health profession majors. The activities were deployed in three microbiology lectures, and students were surveyed. Students' feedback highlights the efficacy of the hands-on approach and increased class participation, as two of the recurring words in the students' survey were "helpful" and "fun."

Behavioral assessment of soft skill development in a highly structured pre-health biology course for undergraduates.

In this study, we assessed a highly structured, yearlong, case-based course designed for undergraduate pre-health students. We incorporated both content learning assessments and developed a novel method called Multiple Mini Exams for assessing course impact on the development of skills that professional schools often seek in pre-health students, focusing on students' abilities to collaborate with others, display bedside manners, synthesize patient case details, appropriately use scientific and medical language, and effectively attain patients' medical histories. This novel method utilized a rubric based on desired medical student skills to score videotaped behaviors and interactions of students role playing as doctors in a hypothetical patient case study scenario. Overall, our findings demonstrate that a highly structured course, incorporating weekly student performance and presentation of patient cases encompassing history taking, diagnosis, and treatment, can result in content learning, as well as improve desired skills specific for success in medical fields.

Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification.

Federated Learning (FL) enables multiple institutes to train models collaboratively without sharing private data. Current FL research focuses on communication efficiency, privacy protection, and personalization and assumes that the data of FL have already been ideally collected. In medical scenarios, however, data annotation demands both expertise and intensive labor, which is a critical problem in FL. Active learning (AL), has shown promising performance in reducing the number of data annotations in medical image analysis. We propose a federated AL (FedAL) framework in which AL is executed periodically and interactively under FL. We exploit a local model in each hospital and a global model acquired from FL to construct an ensemble. We use ensemble-entropy-based AL as an efficient data-annotation strategy in FL. Therefore, our FedAL framework can decrease the amount of annotated data and preserve patient privacy while maintaining the performance of FL. To our knowledge, this FedAL framework applied to medical images has not been previously reported. We validated our framework on real-world dermoscopic datasets. Using only 50% of samples, our framework was able to achieve state-of-the-art performance on a skin-lesion classification task. Our framework performed better than several state-of-the-art AL methods under FL and achieved comparable performance to full-data FL.

Using Team-Based Learning to Teach Pharmacology within the Medical Curriculum.

The primary goal of pharmacology teaching is to prepare medical students to prescribe medications both safely and efficiently. At the Utrecht University Medical School, pharmacology is integrated into the three-year bachelor's curriculum, primarily through large group sessions with limited interaction. A recent evaluation highlighted students' appreciation for pharmacology teaching, but students admitted to attending these teaching moments unprepared, resulting in passive learning. To address this, team-based learning (TBL) was implemented to facilitate learning through interaction, critical thinking, problem solving and reflection through six steps, from superficial to deeper cognitive learning. This study, conducted over two academic years, assessed students' perception and performance regarding TBL. Analysis of a digital questionnaire using a 5-point Likert scale showed high student satisfaction with TBL as a teaching methodology. However, confidence in pharmacology knowledge following TBL was moderate. TBL attendees outperformed non-attendees in pharmacology-related exam questions, indicating that TBL has a positive impact on student performance. We conclude that TBL is an engaging and effective method for pharmacology education, positively influencing student learning and performance. This method could be broadly applied for teaching pharmacology within the medical curriculum or other biomedical programs.

An Exploration of the Relationship Between Active Learning and Student Motivation in STEM: A Mixed Methods Study.

Much of the research about STEM students' motivation measures the relationship between student motivation and academic outcomes, focusing on the student's mindset. This study takes a different approach, considering student motivation and instructional practices. Teaching practices and student motivation were analyzed simultaneously in undergraduate Biology classes using a self-determination theory-based survey and the Classroom Observation Protocol for Undergraduate STEM, and observation notes were collected to document instructor and student behaviors. Quantitative data was used to differentiate students' motivational levels and qualitative data was collected to describe how instructors use specific teaching practices. The results provide a lens into how students' intrinsic motivation varies alongside the instructional practices and interactions in these classes. We found a correlation between higher levels of student motivation in interactive lecture and student-centered teaching profiles. This study highlights how the same practice can be implemented by multiple instructors with varying student motivation scores, pointing out the importance of fidelity to evidence-based instructional practice methods. The results of this study are discussed in the context of published empirical studies examining evidence-based instructional practices that are conceptually supportive of autonomy, competence, and relatedness. Active learning practices observed in this study correlated to positive learning outcomes are discussed and may serve as a guide for instructors interested in implementing specific active learning practices. Recommendations for instructors and departments that are interested in flexible methods to monitor progress toward active learning practices in biology and other STEM disciplines by combining the COPUS and self-determination survey results are presented.

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning.

The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm.

[A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning].

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.

Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning.

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa.

From skeletons to screens: Transforming pedagogy in the age of remote learning.

This Educational Perspective explores the challenges and opportunities in Radiologic Technology education, focusing on the impact of active learning strategies and technological tools in enhancing asynchronous learning experiences. Radiologic Technology, a field reliant on hands-on experience and practical application, faced significant disruptions during the COVID-19 pandemic, necessitating a shift towards remote learning modalities. The Educational Perspective synthesizes the literature on the self-relevance effect, scaffolding, active learning, and metacognitive strategies to elucidate their role in promoting student engagement and success. The article offers recommendations to address the observed challenges, including creating scenario-based eLearning modules, providing immediate feedback and reflection opportunities, and incorporating gamification elements. These strategies aim to enrich asynchronous learning experiences, empowering radiology students to effectively adapt to changing educational landscapes and achieve optimal learning outcomes.