Collaborative Learning Network Research Articles

A MULTILEVEL ANALYSIS OF THE NATIONAL TEACHERS' STANDARDS' IMPACT ON MATHEMATICS TEACHERS' INSTRUCTIONAL PRACTICES

<p>The study examined the impact of the National Teachers’ Standards on the instructional methods of mathematics educators in the Sekyere Central District of the Ashanti Region of Ghana, guided by the Concerns-Based Adoption Model. A cross-sectional research design was adopted. The target population was all basic school teachers in the district, whereas the accessible population was all junior high school mathematics teachers, thus, through the census sampling technique, a sample size of 82 respondents participated in the study. The researchers deployed a closed-ended questionnaire to unravel the impact of the National Teachers’ Standards on instructional methods among mathematics teachers. Reliability coefficients for NTS and instructional practices constructs were 0.92 and 0.85, respectively, indicating strong reliability. Data analysis utilized frequency, percentage, mean, standard deviations, simple linear regression, and Pearson’s correlation. The findings revealed prevalent practices such as question-based learning support and group engagement, with limited emphasis on low-cost materials and gender-responsive pedagogy. Teachers demonstrated a robust understanding of NTS and the curriculum framework, though variability existed. Notably, teachers’ NTS knowledge significantly predicted instructional practices, highlighting its crucial role. Hence, it was recommended, among others, that the Sekyere Central Directorate of Education should invest in the design and delivery of evidence-based, teacher-centered professional development interventions and collaborative learning networks to enhance teachers' pedagogical content knowledge and facilitation skills in alignment with the National Teachers Standards, thereby improving teaching quality, student learning outcomes, and overall educational effectiveness.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0407/a.php" alt="Hit counter" /></p>

Multi-perspective feature collaborative perception learning network for non-destructive detection of pavement defects

Pavement defects detection has made significant progress with the development of convolutional neural networks. Due to the topological complexity of pavement defects regions, most existing detection methods are limited to a limited number of types of defects, which results in unsatisfactory detection results for multi-classified defects. Several studies have used enhanced data to address this issue, however, few have considered the inefficiency caused by imbalanced differences between classes from the perspective of the network model itself. In light of this issue, our goal is to fill this gap by better exploring the fusion of different feature information to improve the performance of the network. As a result, we propose a multi-perspective feature collaborative perception learning network (MFCPLNet), which uses the model's contextual information to guide high-quality multi-classified defects detection and that can be adapted to various pavement conditions. Specifically, for adaptive feature extraction, an irregular perceptual extraction network (IPENet) with enhanced edge information is first designed to draw upon hybrid deformable convolution for efficient network block hybridization. Additionally, a multi-balanced collaborative learning mechanism (MCLM) consisting of auxiliary axial information and 3D feature guidance is constructed to enhance deep semantic features, improve the localization of defects in defective regions, and compensate for weak category supervision. Lastly, in the detection head, an iterative soft aggregation module has been designed to enhance scalable spatial interactions. The results of extensive testing were based on publicly available pavement defects datasets. Based on the proposed network, the mAP index reaches 29.6% and 33.8%, respectively, an increase of 8.3% and 4.6% over the baseline, which is qualitatively and quantitatively superior to mainstream methods.

Bridging borders in learning: an international collaboration experience in higher education Between Institutions from El Salvador and Mexico

In the face of a context that demands the formation of individuals with a broad perspective of the world, the Association of Universities Entrusted to the Society of Jesus in Latin America (AUSJAL) promotes intercultural dialogue through various projects. One such project is Collaborative Online International Learning (COIL). This essay presents the systematization of a COIL experience, from its design to its implementation. Students and professors from the Central American University José Simeón Cañas (UCA, El Salvador) and the Jesuit University of Guadalajara (ITESO, Mexico) achieved learning objectives related to values such as solidarity, social justice, intercultural dialogue, and the formation of individuals committed to building a more inclusive world. The COIL methodology involved: flexible duration, groups of students sharing tasks and projects, an initial activity, synchronous or asynchronous interaction, and group reflection on the shared experience. The systematization of this experience has a qualitative approach, assuming social phenomena from the perspective of the participating actors. The results showed that the exchange between students from different countries allowed them to collaboratively construct, in a virtual environment, an academic text that argued their position on social issues, aligned with the UN Sustainable Development Goals (SDGs), from an intercultural perspective. The participants achieved common goals, built collaborative learning networks, and expressed improvement in their skills for respectful and empathetic communication that enriched them interculturally, allowing them to identify points of convergence regarding other contexts, from a regional perspective.

CCNet: Collaborative Camouflaged Object Detection via decoder-induced information interaction and supervision refinement network

Recently, research based on the camouflaged object detection (COD) task has achieved great progress, while the collaborative camouflaged object detection (CoCOD) task is still lacking. Our research focuses on the simultaneous detection and localization of the collaborative camouflaged objects, i.e., CoCOD task. We use the cooperative information between a single image and a group of camouflage images to discover cooperative camouflaged objects effectively. In this paper, we propose a collaborative cross-scale feature learning network (CCNet). Our model is characterized by two innovative constructions: We proposed an edge augmentation module (EAM), which effectively extracts edge information of the camouflaged object and integrates it with the collaborative information employed in auxiliary supervision. In addition, we design a group decoder module (GDM) to extract and merge co-camouflage information. Extensive experiments on CoCOD8K datasets demonstrate that our CCNet significantly outperforms the existing 13 state-of-the-art COD and 6 state-of-the-art collaborative salient object detection (CoSOD) methods under six widely used evaluation metrics. The code will be available at: https://github.com/zc199823/CCNet--CoCOD.

Collaborative Learning and Style-Adaptive Pooling Network for Perceptual Evaluation of Arbitrary Style Transfer.

Although the research of arbitrary style transfer (AST) has achieved great progress in recent years, few studies pay special attention to the perceptual evaluation of AST images that are usually influenced by complicated factors, such as structure-preserving, style similarity, and overall vision (OV). Existing methods rely on elaborately designed hand-crafted features to obtain quality factors and apply a rough pooling strategy to evaluate the final quality. However, the importance weights between the factors and the final quality will lead to unsatisfactory performances by simple quality pooling. In this article, we propose a learnable network, named collaborative learning and style-adaptive pooling network (CLSAP-Net) to better address this issue. The CLSAP-Net contains three parts, i.e., content preservation estimation network (CPE-Net), style resemblance estimation network (SRE-Net), and OV target network (OVT-Net). Specifically, CPE-Net and SRE-Net use the self-attention mechanism and a joint regression strategy to generate reliable quality factors for fusion and weighting vectors for manipulating the importance weights. Then, grounded on the observation that style type can influence human judgment of the importance of different factors, our OVT-Net utilizes a novel style-adaptive pooling strategy guiding the importance weights of factors to collaboratively learn the final quality based on the trained CPE-Net and SRE-Net parameters. In our model, the quality pooling process can be conducted in a self-adaptive manner because the weights are generated after understanding the style type. The effectiveness and robustness of the proposed CLSAP-Net are well validated by extensive experiments on the existing AST image quality assessment (IQA) databases. Our code will be released at https://github.com/Hangwei-Chen/CLSAP-Net.

GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector.

In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the following two essential criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module (GAM); 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module (GCM) conditioning on the inconsistent consensus. To further improve the accuracy, we design a series of simple yet effective components as follows: i) a recurrent auxiliary classification module (RACM) promoting model learning at the semantic level; ii) a confidence enhancement module (CEM) assisting the model in improving the quality of the final predictions; and iii) a group-based symmetric triplet (GST) loss guiding the model to learn more discriminative features. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and CoSal2015, demonstrate that our GCoNet+ outperforms the existing 12 cutting-edge models. Code has been released at https://github.com/ZhengPeng7/GCoNet_plus.

TCCL-Net: Transformer-Convolution Collaborative Learning Network for Omnidirectional Image Super-Resolution

As virtual reality and metaverse become more and more popular, the Omnidirectional Image (OI) has attracted extreme attention due to its immersive display characteristics. However, users only watch a portion of the content in a specific viewport extracted from a panoramic view, which will lead to a problem of resolution mismatch that requires High-Resolution (HR) for clear near-eye displays in viewports. Hence, it is necessary to exploit a Super Resolution (SR) solution for reconstructing Low-Resolution (LR) OIs. Different from 2D SR methods, the variation of pixel distributions along latitudes is a critical factor in designing an Omnidirectional Image Super-Resolution (OISR) scheme. In this paper, we put forward a novel end-to-end network with a Transformer and Convolution Collaborative Learning Network (TCCL-Net) for OISR. Firstly, Swin Transformer blocks and residual convolution blocks are employed to extract long-range and short-range dependencies, thereby digging into more rich and heterogeneous features from these two branches. Secondly, to better fuse these two features, cross-guided enhanced attention mechanisms are designed for bidirectional information enhancement onto both channel and spatial features. Thirdly, to alleviate nonuniformly pixel distributions across latitudes, we add an absolute positional encoding into Swin Transformer to represent patch weights at different positions and propose a tile-based panoramic reconstruction module to super-resolve various bands with different pixel sampling characteristics across latitudes. Experimental results on two available benchmark datasets demonstrate the superiority of the proposed approach over the state-of-the-art method in achieving OISR task.

Measuring Pediatric Palliative Care Quality: Challenges and Opportunities.

Pediatric palliative care (PPC) programs vary widely in structure, staffing, funding, and patient census, resulting in inconsistency in service provision. Improving the quality of palliative care for children living with serious illness and their families requires measuring care quality, ensuring that quality measurement is embedded into day-to-day clinical practice, and aligning quality measurement with healthcare policy priorities. Yet, numerous challenges exist in measuring PPC quality. This paper provides an overview of PPC quality measurement, including challenges, current initiatives, and future opportunities. While important strides toward addressing quality measurement challenges in PPC have been made, including ongoing quality measurement initiatives like the Cambia Metrics Project, the PPC What Matters Most study, and collaborative learning networks, more work remains. Providing high-quality PPC to all children and families will require a multi-pronged approach. In this paper, we suggest several strategies for advancing high-quality PPC, which includes 1) considering how and by whom success is defined, 2) evaluating, adapting, and developing PPC measures, including those that address care disparities within PPC for historically marginalized and excluded communities, 3) improving the infrastructure with which to routinely and prospectively measure, monitor, and report clinical and administrative quality measures, 4) increasing endorsement of PPC quality measures by prominent quality organizations to facilitate accountability and possible reimbursement, and 5) integrating PPC-specific quality measures into the administrative, funding, and policy landscape of pediatric healthcare.

Deep Cross-Layer Collaborative Learning Network for Online Knowledge Distillation

Deep Cross-Layer Collaborative Learning Network for Online Knowledge Distillation

Advancing Team-Based Care for Spinal Muscular Atrophy: A Multi-State Project ECHO Initiative and National Education Strategy (S2.002)

<h3>Objective:</h3> N/A <h3>Background:</h3> An academic medical center, medical education provider, and faculty experts developed a comprehensive, multicomponent initiative, with Project ECHO® serving as the primary delivery platform. The goal was to help multidisciplinary, community-based teams across the US enhance care of their patients with spinal muscular atrophy (SMA). <h3>Design/Methods:</h3> We conducted monthly group tele-mentoring sessions using the proven Project ECHO model as our primary educational framework. Project ECHO creates collaborative learning networks led by expert interprofessional teams and includes de-identified patient case discussion, mentoring, and didactic presentations using a web-based videoconferencing platform to conduct virtual sessions with learners. Sessions were accredited for physicians and nurses. We assessed changes in knowledge, competency, and performance pre-/post-initiative and gathered participant feedback using online surveys and phone interviews. We also created an accredited, online activity for clinicians across the country; its content aligned with that of the live ECHO sessions. <h3>Results:</h3> Clinicians from 7 sites participated in the Project ECHO portion of the initiative. Sites included academic medical centers, community hospitals, and private practices. The online national educational activity launched April 27, 2022, with over 350 participants thus far. Learners for this initiative included neurologists, pediatricians, nurses, NPs, PAs, and other professionals who collectively care for approximately 1,000 patients with SMA on a regular basis. Outcomes data and feedback revealed positive effects on learning and performance with statistically significant increases in knowledge and competency. However, persistent learning gaps remained post-education related to appropriate use of available and emerging treatments, suggesting areas for continued education. <h3>Conclusions:</h3> This initiative’s high level of interactivity allowed for in-depth exploration of challenges clinicians face in SMA management, which is not easily accomplished within traditional educational frameworks. In the setting of SMA management, where clinicians may feel professionally isolated, the ECHO model proved critical in improving quality of patient care and cultivating communities of practice. <b>Disclosure:</b> Mrs. Gabriel has nothing to disclose. Ana Carolina Tesi Rocha, MD has nothing to disclose. Dr. Klotz has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Genentech Roche. Dr. Adewuya has nothing to disclose. Ms. Hack has nothing to disclose. Whitney Hess has nothing to disclose.

Saliency-Assisted Collaborative Learning Network for Road Scene Semantic Segmentation

Saliency-Assisted Collaborative Learning Network for Road Scene Semantic Segmentation

Dual-branch collaborative learning network for crop disease identification.

Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture's efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels via the multi-layer cascade design strategy. Extensive experiments on the Plant Village dataset demonstrated the best classification performance of our DBCLNet method compared to the state-of-the-art methods for the identification of 38 categories of crop diseases. Besides, the Accuracy, Precision, Recall, and F-score of our DBCLNet for the identification of 38 categories of crop diseases are 99.89%, 99.97%, 99.67%, and 99.79%, respectively. 811.

EEG-CLNet: Collaborative Learning for Simultaneous Measurement of Sleep Stages and OSA Events Based on Single EEG Signal

Sleep-stage and apnea–hypopnea index (AHI) are the most important metrics in the diagnosis of sleep syndrome disease. In previous studies, these two tasks are usually implemented separately, which is both time- and resource-consuming. In this work, we propose a novel single electroencephalogram (EEG)-based collaborative learning network (EEG-CLNet) for simultaneous sleep staging and obstructive sleep apnea (OSA) event detection through multitask collaborative learning. The EEG-CLNet regards different tasks as a common unit to extract features from intragroups via both local parameter sharing and cross-task knowledge distillation (CTKD), rather than just sharing parameters or shortening the distance between different tasks. Our approach has been validated on two datasets with the same or better performance than other methods. The experimental results show that our method achieves a performance gain of 1%–5% compared with the baseline. Compared to previous works where two or even more models were required to perform sleep staging and OSA event detection, the EEG-CLNet could reduce the total number of model parameters and facilitate the model to mine the hidden relationships between different task semantic information. More importantly, it effectively alleviates the task bias problem in hard parameter sharing. As a consequence, this approach has notable potential to be a solution for a lightweight wearable sleep monitoring system in the future.

Collaborative Learning Network for Change Detection and Semantic Segmentation of Remote Sensing Images

Collaborative Learning Network for Change Detection and Semantic Segmentation of Remote Sensing Images

Collaborative learning network for head pose estimation

• Propose a collaborative learning framework for head pose estimation. • Learn complementary information from the landmark-based and landmark-free branches. • Design a Landmark-MLP-Mixer to obtain the head pose angles from facial landmarks. • Introduce a union loss function to optimize two complementary branches. • Conduct comparison experiments and ablation studies on various datasets. Head pose estimation is an important task in many real-world applications, such as human–computer interaction, driver monitoring, face localization and gaze estimation. In this paper, we present a novel collaborative learning framework based on Convolutional Neural Networks (CNNs) for head pose estimation. The proposed framework consists of a landmark-based branch and a landmark-free branch. The former first estimates facial landmarks and then follows the Landmark-MLP-Mixer module which models the complex nonlinear mapping relationship from facial landmarks to head pose angles. While the later adopts a label distribution learning strategy to estimate head pose. The two branches both dedicate themselves to head pose estimation task, and they collaborate with each other for mutual promotion and complementary semantic learning. Specifically, we introduce a dual-branch transfer module in the middle of the network to achieve explicit semantic interaction and introduce a multi-loss strategy that induces to implicit information interaction. We conduct extensive experiments on several popular benchmarks, including AFLW, AFLW2000 and BIWI, the results show that our method is competitive compared to other state-of-the-art methods.

The Efficacy of Global Networked Collaborative PBL Courses on Fostering Global Competence among Pupils

&lt;p&gt;為了因應全球化的浪潮與提昇國際競爭力，如何培養學生之全球勝任力的議題正日漸受到重視，因此，本研究提出國際網路專題導向式合作學習課程的學習模式，此學習模式要求中小學學生在一個國際化的虛擬學習環境中共同探討與應對聯合國所倡議的全球永續發展的相關議題。本研究之目的，係採用實證研究方法，驗證國際網路專題導向式合作學習課程的學習模式是否能增進學生的全球勝任力。本研究首先完成了四門國際網路專題導向式合作學習課程的設計，並建置於一個國際化的網路合作學習平台中，接著邀請世界各地有興趣的中小學學生以小組的方式參與10 週的課程活動。為了評量課程活動在培養全球勝任力上的效能，本研究發展出一個全球勝任力專家量表，並以前、後測的方式請求小組帶隊的教師為其組員填寫量表。此全球勝任力專家量表共分為洞察力、體認力、溝通力、與實踐力四個向度。研究結果顯示，本研究所提出的學習模式，可以顯著提昇學習者的全球勝任力。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;It is on the spotlight currently in education regarding how to nurture global competence in K-12. The study proposes the learning genre of global networked collaborative project-based learning for meeting the goal. The genre places students in a virtual learning environment with a global context and encourage them to deal with sustainable development goals, which was initiated by United Nations in 2015, in a joint effort collaboratively. The goal of the study is to verify by incorporating empirical methodologies whether the genre could foster global competence or not? First, four designated project-based learning courses were designed and implemented on a renown international collaborative learning platform. International participants then were recruited in an open manner and took part in the learning tasks of the courses online for 10 weeks in a team-based format. For assessing the performance in global competence, a rubric was developed and administered in pretest and posttest fashion, which were responded by teachers of the participating teams for all team members individually before and after the courses respectively. The criteria of the rubric are composed of four pillars, which are Investigate the World, Recognize Perspectives, Communicate Ideas, and Take Action. Results of the data analysis reveal that the proposed learning genre raises global competence significantly.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Preventing Cardiac Arrest in the Pediatric Cardiac Intensive Care Unit Through Multicenter Collaboration

Preventing in-hospital cardiac arrest (IHCA) likely represents an effective strategy to improve outcomes for critically ill patients, but feasibility of IHCA prevention remains unclear. To determine whether a low-technology cardiac arrest prevention (CAP) practice bundle decreases IHCA rate. Pediatric cardiac intensive care unit (CICU) teams from the Pediatric Cardiac Critical Care Consortium (PC4) formed a collaborative learning network to implement the CAP bundle consistent with the Institute for Healthcare Improvement framework; 15 hospitals implemented the bundle voluntarily. Risk-adjusted IHCA incidence rates were analyzed across 2 time periods, 12 months (baseline) and 18 months after CAP implementation (intervention) using difference-in-differences (DID) regression to compare 15 CAP and 16 control PC4 hospitals that chose not to participate in CAP but had IHCA rates tracked in the PC4 registry. Patients deemed at high risk for IHCA, based on a priori evidence-based criteria and empirical hospital-specific criteria, were selected to receive the CAP bundle. Data were collected from July 2018 to December 2019, and data were analyzed from March to August 2020. CAP bundle included 5 elements developed to promote increased situational awareness and communication among bedside clinicians to recognize and mitigate deterioration in high-risk patients. Risk-adjusted IHCA incidence rate across all CICU admissions (IHCA events divided by all admissions). The bundle was activated in 2664 of 10 510 CAP hospital admissions (25.3%); admission characteristics were similar across study periods. There was a 30% relative reduction in risk-adjusted IHCA incidence rate at CAP hospitals (intervention period: 2.6%; 95% CI, 2.2-2.9; baseline: 3.7%; 95% CI, 3.1-4.0), but no change at control hospitals (intervention period: 2.7%; 95% CI, 2.3-2.9; baseline: 2.7%; 95% CI, 2.2-3.0). DID analysis confirmed significantly reduced odds of IHCA among all admissions at CAP hospitals compared with control hospitals during the intervention period vs baseline (odds ratio, 0.72; 95% CI, 0.56-0.91; P = .01). DID odds ratios were 0.72 (95% CI, 0.53-0.98) for the surgical subgroup, 0.74 (95% CI, 0.48-1.14) for the medical subgroup, and 0.72 (95% CI, 0.50-1.03) for the high-risk admission subgroup at CAP hospitals after intervention. All-cause risk-adjusted mortality rate did not change after intervention. Implementation of this CAP bundle led to significant IHCA reduction across multiple pediatric CICUs. Future studies may determine if this bundle can be effective in other critically ill populations.

Traffic Accident Detection via Self-Supervised Consistency Learning in Driving Scenarios

With the rapid progress of autonomous driving and advanced driver assistance systems, there are growing efforts to promote their safety in natural driving scenarios, especially for the detection of the traffic accidents. However, because of the dynamic camera motion and complex scene in driving situations, traffic accident detection is still challenging. In this work, we aim to give the ability of Traffic Accident Detection for driving systems by proposing a Self-Supervised Consistency learning framework, termed as SSC-TAD, that involves the appearance, motion, and context consistency learning. The key formulation is to find the inconsistency of video frames, object locations and the spatial relation structure of scene temporally between different frames captured by the dashcam videos. Within this field, different from the previous works which concentrate on predicting the future object locations or frames, we further focus on predicting the visual scene context in driving scenarios and detecting the traffic accident by considering the temporal frame consistency, temporal object location consistency, and the spatial-temporal relation consistency of road participants. In this work, this formulation is fulfilled by a collaborative multi-task consistency learning network and the visual scene context feature is represented by a graph convolution network. The superiority to the state-of-the-art is verified by exhaustive evaluations on two large scale datasets, i.e., the AnAn Accident Detection (A3D) dataset and DADA-2000 dataset collected recently.

Open set recognition of underwater acoustic targets based on GRU-CAE collaborative deep learning network

Aiming at the problem of underwater acoustic target open set recognition, a new recognition method based on deep learning and template matching is proposed in this paper. The proposed method includes three stages: feature extraction based on GRU-CAE collaborative deep learning network, template establishment and template matching. Firstly, the GRU-CAE collaborative deep learning network is proposed to extract the deep collaborative features. The GRU-CAE cooperative deep learning network combines the advantages of CAE network in extracting spatial information of spectrogram and GRU network in extracting temporal structure features of spectrogram. Then, the feature template of each class of underwater acoustic target is constructed by finding an optimal vector in Euclidean space. Finally, the distance between the deep collaborative features of the test set and templates are calculated to distinguish the category of the samples. The experimental data of 5 kinds of underwater acoustic targets are used to verify the recognition performance of the proposed method. The results show that the deep collaborative features have excellent class separability, and the recognition accuracy rate of open set recognition can reach 82.21%. Compared with the other 3 open set recognition methods, the proposed method has the highest recognition accuracy rate.

Self-Directed Learning During the COVID-19 Pandemic: Perspectives of South African Final-Year Health Professions Students.

PurposeSelf-directed learning (SDL) has been advocated for effective training of final-year health professions students. COVID-19 challenges conventional teaching, learning, and assessment in the clinical environment. This study aimed to identify and explore enablers and barriers to SDL among final-year health professions students training during the COVID-19 pandemic.MethodsAdopting the SWOT (strengths, weaknesses, opportunities and threats) framework, this study explored the clinical learning and training experiences of final-year health professions students during the pandemic. A survey was conducted via online platforms. Data from 155 respondents were thematically analyzed.ResultsPersonal attributes such as reflection, self-determination, motivation, resilience, and positive learning behaviors and skills were identified as SDL enablers. Collaborative learning networks and online learning platforms facilitate learning needs and goals. Fear and anxiety, untrusted learning sites, uncertainty about graduation, financial issues and challenges in the learning environments were the major themes related to barriers to SDL.ConclusionThe importance of SDL as a skill for uncertain times warrants further investigation in the training of future healthcare professionals. Inclusive planning and engagement with final-year health professions students to address identified stressors, as well as the creation of shared platforms where students are part of the decision-making processes for clinical learning and training are recommended. Responsive curricula that optimize unpredictable disruptions in clinical training are needed to equip students to diagnose their own learning needs and implement appropriate learning strategies.