Knowledge Refinement Research Articles

Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning.

Given a model well-trained with a large-scale base dataset, few-shot class-incremental learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding overfitting, without catastrophically forgetting all encountered classes previously. Currently, semi-supervised learning technique that harnesses freely available unlabeled data to compensate for limited labeled data can boost the performance in numerous vision tasks, which heuristically can be applied to tackle issues in FSCIL, i.e., the semi-supervised FSCIL (Semi-FSCIL). So far, very limited work focuses on the Semi-FSCIL task, leaving the adaptability issue of semi-supervised learning to the FSCIL task unresolved. In this article, we focus on this adaptability issue and present a simple yet efficient Semi-FSCIL framework named uncertainty-aware distillation with class-equilibrium (UaD-ClE), encompassing two modules: uncertainty-aware distillation (UaD) and class equilibrium (ClE). Specifically, when incorporating unlabeled data into each incremental session, we introduce the ClE module that employs a class-balanced self-training (CB_ST) to avoid the gradual dominance of easy-to-classified classes on pseudo-label generation. To distill reliable knowledge from the reference model, we further implement the UaD module that combines uncertainty-guided knowledge refinement with adaptive distillation. Comprehensive experiments on three benchmark datasets demonstrate that our method can boost the adaptability of unlabeled data with the semi-supervised learning technique in FSCIL tasks. The code is available at https://github.com/yawencui/UaD-ClE.

Toward Explainable Multiparty Learning: A Contrastive Knowledge Sharing Framework.

Multiparty learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multiparty learning approaches are confronted with obstacles, such as system heterogeneity, statistical heterogeneity, and incentive design. Determining how to deal with these challenges and further improve the efficiency and performance of multiparty learning has become an urgent problem to be solved. In this article, we propose a novel contrastive multiparty learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication and analogize multiparty learning as a many-to-one knowledge-sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets.

Multi-strategy continual learning for knowledge refinement and consolidation

Multi-strategy continual learning for knowledge refinement and consolidation

APHS-YOLO: A Lightweight Model for Real-Time Detection and Classification of Stropharia Rugoso-Annulata.

The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time remains a challenging task due to the difficulty of accurately identifying, locating and sorting large quantities of them simultaneously. Models must be deployable on resource-limited devices, making it challenging to achieve both a high accuracy and speed. This paper proposes the APHS-YOLO (YOLOv8n integrated with AKConv, CSPPC and HSFPN modules) model, which is lightweight and efficient, for identifying Stropharia rugoso-annulata of different grades and seasons. This study includes a complete dataset of runners of different grades in spring and autumn. To enhance feature extraction and maintain the recognition accuracy, the new multi-module APHS-YOLO uses HSFPNs (High-Level Screening Feature Pyramid Networks) as a thin-neck structure. It combines an improved lightweight PConv (Partial Convolution)-based convolutional module, CSPPC (Integration of Cross-Stage Partial Networks and Partial Convolution), with the Arbitrary Kernel Convolution (AKConv) module. Additionally, to compensate for the accuracy loss due to lightweighting, APHS-YOLO employs a knowledge refinement technique during training. Compared to the original model, the optimized APHS-YOLO model uses 57.8% less memory and 62.5% fewer computational resources. It has an FPS (frames per second) of over 100 and even achieves 0.1% better accuracy metrics than the original model. These research results provide a valuable reference for the development of automatic sorting equipment for forest farmers.

Course on Breeding, Handling, and Experimental Procedures of Zebrafish (Danio rerio): Training, Refinement, Paradigm Shift, and Dissemination of Knowledge from Brazil to the World.

Currently, in Brazil, all researchers involved in animal experimentation must undergo training in laboratory animal science to stay updated on biology, methodology, ethics, and legal considerations related to the use of animals. The training program presented in this study not only aims to fulfill a legal obligation but also intends to train students and professionals to effectively care for their biomodels. It seeks to help them understand the importance of this care, both for the welfare of the animals and for the results of their projects. In total, 58 participants were present at the event (pre-event and full-time course). These participants consisted students and professionals from 11 institutions and 5 different countries. These numbers demonstrate the successful attainment of the desired capillarity in the scientific community and the posterior dissemination of knowledge. Through this course, it was possible to train the participants and raise their awareness about the importance of applying scientific knowledge in their daily practices to maintain the animals, ensuring the welfare of the models and refining the research. Finally, the program presented in this study, as well as the strategies adopted, can serve as a model for other institutions aiming to achieve similar results.

INTERDISCIPLINARY INTEGRATION OF MICROBIOLOGY, VIROLOGY, AND IMMUNOLOGY IN THERAPEUTIC STOMATOLOGY

Introduction: This article examines the potential for integrating Therapeutic Stomatology with Microbiology, Virology, and Immunology. It highlights the significance of studying key concepts in these disciplines, which underpin various methods of microbiological diagnostics utilized in dentistry. Practical skills and theoretical knowledge essential for the study of Therapeutic Stomatology are exemplified. The aim of this paper is to underscore the significance of interdisciplinary integration between Microbiology, Virology, and Immunology with Therapeutic Stomatology in the education and training of future dental practitioners.
 Results and Discussion: A solid grasp of microbiology is crucial for fostering the clinical acumen of future dental professionals. The integration of disciplines should be systematically woven into all phases of the educational curriculum. By merging fundamental and clinical disciplines, a multidimensional approach to problem-solving is facilitated, highlighting the interconnectedness between fields. This integration enhances students' intellectual, cognitive, and communicative abilities, broadening their educational horizons and enabling the application of foundational knowledge in novel contexts. Consequently, this approach nurtures the development of clinical reasoning skills among aspiring dental practitioners.
 Conclusions: The interdisciplinary integration of Microbiology, Virology, and Immunology with Therapeutic Stomatology in professional training ensures educational coherence, enhances the organization and refinement of knowledge and skills, and lays the groundwork for cultivating professionals characterized by versatile thinking and comprehensive expertise in their field.

Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation

Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from cli ents without assembling their private data. Constrained communication and personalization requirements pose severe challenges to FL. Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead. However, most existing FD methods require a proxy dataset, which is often unavailable in reality. A few recent proxy-data-free FD approaches can eliminate the need for additional public data, but suffer from remarkable discrepancy among local knowledge due to client-side model heterogeneity, leading to ambiguous representation on the server and inevitable accuracy degradation. To tackle this issue, we propose a proxy-data-free FD algorithm based on distributed knowledge congruence (FedDKC). FedDKC leverages well-designed refinement strategies to narrow local knowledge differences into an acceptable upper bound, so as to mitigate the negative effects of knowledge incongruence. Specifically, from perspectives of peak probability and Shannon entropy of local knowledge, we design kernel-based knowledge refinement (KKR) and searching-based knowledge refinement (SKR) respectively, and theoretically guarantee that the refined-local knowledge can satisfy an approximately-similar distribution and be regarded as congruent. Extensive experiments conducted on three common datasets demonstrate that our proposed FedDKC significantly outperforms the state-of-the-art on various heterogeneous settings while evidently improving the convergence speed.

How Does Psychological Empowerment Affect Knowledge Management Improvement in Organizations? A Study of Cause-and-Effect Relationship Using the Fuzzy DEMATEL Method

The present article aims to investigate the cause-and-effect relationship between the employees’ psychological empowerment (PE) and dimensions of knowledge management (KM) in an oil company, using the fuzzy decision-making trial and evaluation laboratory method. The study population consists of oil company experts. The sample size is determined based on the theoretical saturation limit. The present findings show that PE is the strongest causative factor with significant effects on knowledge creation (KC) and knowledge dissemination (KD). Among the KM dimensions, the most influential factors are the dimensions of KC, knowledge application, and knowledge capture (KCA). Of all KM dimensions, knowledge refinement is the most influential. The KC, in addition to being the most important dimension of KM affected by PE, is also found to influence all other dimensions of KM; this indicates its key role as a starting point in the process of KM. Considering the reciprocal effects of PE and the two dimensions of KM (KC and KD), it is important to pay attention to KC when developing a KM system. Importantly, the present findings show that PE and KM have an interdependent interaction. In other words, not only by improving the employees’ PE, the organizations can accelerate the KM process, but also by improving the KM systems, they may increase their employees’ PE. Based on the sensitivity analyses, our proposed method is as efficient as the available methods, such as interpretive structural modeling and Bayesian best-worst method.

LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications

Medical Knowledge Graph (KG) has shown great potential in various healthcare scenarios, such as drug recommendation and clinical decision support system. The factors that determine the role of a medical KG in practical applications are the scale, coverage, and quality of the medical knowledge it can provide. Most existing medical KGs are extracted from a single or a few information sources. However, medical knowledge extracted from insufficient information sources is usually highly incomplete or even biased, which results in a lack of data completeness and may lessen their effectiveness in real-world scenarios. Besides, the coverage of entity and relation types is inadequate in most previous works, which also might restrict their potential usage in future applications. In this paper, we build a unified system that can extract and manage medical knowledge from heterogeneous information sources. We first employ named entity recognition and relation extraction methods to extract knowledge triplets from medical texts. Then we propose a hierarchical entity alignment framework for further knowledge refinement. Based on our system, we construct a large-scale, high-quality, multi-source, and multi-lingual medical KG named LMKG, which includes 13 entity types and 17 relation types, and contains 403,784 entity and 1,225,097 relation instances. We conduct extensive experiments to evaluate the quality of LMKG. Experimental results show that LMKG can effectively enhance the performance of both upstream and downstream intelligent medicine applications. We have publicly released the KG resources and corresponding management service interface to facilitate research and applications in the medical field.

A Comprehensive Survey on Automatic Knowledge Graph Construction

Automatic knowledge graph construction aims at manufacturing structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized structured knowledge beyond informative data. In addition, researchers have also been exploring new ways of handling sophisticated construction tasks in diversified scenarios. Thus, there is a demand for a systematic review of paradigms to organize knowledge structures beyond data-level mentions. To meet this demand, we comprehensively survey more than 300 methods to summarize the latest developments in knowledge graph construction. A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution. The processes of knowledge acquisition are reviewed in detail, including obtaining entities with fine-grained types and their conceptual linkages to knowledge graphs; resolving coreferences; and extracting entity relationships in complex scenarios. The survey covers models for knowledge refinement, including knowledge graph completion, and knowledge fusion. Methods to handle knowledge evolution are also systematically presented, including condition knowledge acquisition, condition knowledge graph completion, and knowledge dynamic. We present the paradigms to compare the distinction among these methods along the axis of the data environment, motivation, and architecture. Additionally, we also provide briefs on accessible resources that can help readers to develop practical knowledge graph systems. The survey concludes with discussions on the challenges and possible directions for future exploration.

Learning few-shot imitation as cultural transmission

Cultural transmission is the domain-general social skill that allows agents to acquire and use information from each other in real-time with high fidelity and recall. It can be thought of as the process that perpetuates fit variants in cultural evolution. In humans, cultural evolution has led to the accumulation and refinement of skills, tools and knowledge across generations. We provide a method for generating cultural transmission in artificially intelligent agents, in the form of few-shot imitation. Our agents succeed at real-time imitation of a human in novel contexts without using any pre-collected human data. We identify a surprisingly simple set of ingredients sufficient for generating cultural transmission and develop an evaluation methodology for rigorously assessing it. This paves the way for cultural evolution to play an algorithmic role in the development of artificial general intelligence.

Genetic Patterns of Selected Muscular Dystrophies in the Muscular Dystrophy Surveillance, Tracking, and Research Network.

To report the genetic etiologies of Emery-Dreifuss muscular dystrophy (EDMD), limb-girdle muscular dystrophy (LGMD), congenital muscular dystrophy (CMD), and distal muscular dystrophy (DD) in 6 geographically defined areas of the United States. This was a cross-sectional, population-based study in which we studied the genes and variants associated with muscular dystrophy in individuals who were diagnosed with and received care for EDMD, LGMD, CMD, and DD from January 1, 2008, through December 31, 2016, in the 6 areas of the United States covered by the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet). Variants of unknown significance (VUSs) from the original genetic test reports were reanalyzed for changes in interpretation. Among 243 individuals with definite or probable muscular dystrophy, LGMD was the most common diagnosis (138 cases), followed by CMD (62 cases), DD (22 cases), and EDMD (21 cases). There was a higher proportion of male individuals compared with female individuals, which persisted after excluding X-linked genes (EMD) and autosomal genes reported to have skewed gender ratios (ANO5, CAV3, and LMNA). The most common associated genes were FKRP, CAPN3, ANO5, and DYSF. Reanalysis yielded more definitive variant interpretations for 60 of 144 VUSs, with a mean interval between the original clinical genetic test of 8.11 years for all 144 VUSs and 8.62 years for the 60 reclassified variants. Ten individuals were found to have monoallelic pathogenic variants in genes known to be primarily recessive. This study is distinct for being an examination of 4 types of muscular dystrophies in selected geographic areas of the United States. The striking proportion of resolved VUSs demonstrates the value of periodic re-examinations of these variants. Such re-examinations will resolve some genetic diagnostic ambiguities before initiating repeat testing or more invasive diagnostic procedures such as muscle biopsy. The presence of monoallelic pathogenic variants in recessive genes in our cohort indicates that some individuals with muscular dystrophy continue to face incomplete genetic diagnoses; further refinements in genetic knowledge and diagnostic approaches will optimize diagnostic information for these individuals.

Knowledge Distillation for Enhancing a Lightweight Magnet Tile Target Detection Model: Leveraging Spatial Attention and Multi-Scale Output Features

Accurate and efficient sorting of diverse magnetic tiles during manufacturing is vital. However, challenges arise due to visual similarities among types, necessitating complex computer vision algorithms with large sizes and high computational needs. This impedes cost-effective deployment in the industry, resulting in the continued use of inefficient manual sorting. To address this issue, we propose an innovative lightweight magnetic tile detection approach that improves knowledge distillation for a compressed YOLOv5s model. Incorporating spatial attention modules into different feature extraction stages of YOLOv5s during the knowledge distillation process can enhance the ability of the compressed model to learn the knowledge of intermediate feature extraction layers from the original large model at different stages. Combining different outputs to form a multi-scale output, the multi-scale output feature in the knowledge refinement process enhances the capacity of the compressed model to grasp comprehensive target knowledge in outputs. Experimental results on our self-built magnetic tile dataset demonstrate significant achievements: 0.988 mean average precision, 0.5% discrepancy compared to the teacher’s network, and an 85% model size reduction. Moreover, a 36.70% boost in inference speed is observed for single image analysis. Our method’s effectiveness is also validated by the Pascal VOC dataset results, showing potential for broader target detection scenarios. This approach offers a solution to magnetic tile target detection challenges while being expected to expand to other applications.

Comprehensive learning and adaptive teaching: Distilling multi-modal knowledge for pathological glioma grading

The fusion of multi-modal data, e.g., pathology slides and genomic profiles, can provide complementary information and benefit glioma grading. However, genomic profiles are difficult to obtain due to the high costs and technical challenges, thus limiting the clinical applications of multi-modal diagnosis. In this work, we investigate the realistic problem where paired pathology-genomic data are available during training, while only pathology slides are accessible for inference. To solve this problem, a comprehensive learning and adaptive teaching framework is proposed to improve the performance of pathological grading models by transferring the privileged knowledge from the multi-modal teacher to the pathology student. For comprehensive learning of the multi-modal teacher, we propose a novel Saliency-Aware Masking (SA-Mask) strategy to explore richer disease-related features from both modalities by masking the most salient features. For adaptive teaching of the pathology student, we first devise a Local Topology Preserving and Discrepancy Eliminating Contrastive Distillation (TDC-Distill) module to align the feature distributions of the teacher and student models. Furthermore, considering the multi-modal teacher may include incorrect information, we propose a Gradient-guided Knowledge Refinement (GK-Refine) module that builds a knowledge bank and adaptively absorbs the reliable knowledge according to their agreement in the gradient space. Experiments on the TCGA GBM-LGG dataset show that our proposed distillation framework improves the pathological glioma grading and outperforms other KD methods. Notably, with the sole pathology slides, our method achieves comparable performance with existing multi-modal methods. The code is available at https://github.com/CUHK-AIM-Group/MultiModal-learning.

Social theory and overinterpretation

ABSTRACT Theory is the use of abstraction in the pursuit of understanding. In the human sciences, theory is a talmudic process of reading and conceptual dispute that carries the colligation of evidentiary signs (minimal interpretation) towards riskier, but more insightful and widely relevant, interpretations of the meanings, causes, and significance of human events (maximal interpretation). Yet, in making possible such maximal interpretations of society, politics, literature, and so forth, theory also introduces the possibility of overinterpreting evidence. Judgments that overinterpretation has occurred are made collectively within communities of inquiry. After developing Umberto Eco’s theory of overinterpretation as part of a hermeneutic-semiotic account of theory in the human sciences, this paper conducts a case study of the rise and partial fall of the Marxist interpretation of the French Revolution. This reveals aspects of the process whereby patterns of maximal interpretation, carried through several academic generations, allow the development and refinement of knowledge and insight about an object of inquiry, on the one hand, and yet are subject to judgment as overinterpreted, on the other. Much more than a matter of falsification and/or the politics of intellectuals, the decline of the Marxist interpretation of the French Revolution involved a complex series of judgments about the degree to which an abstract theoretical terminology could continue to produce new and deeper understandings. In conclusion, the paper suggests that the talmudic aspect of social theory has affinities with the universal human capacity for thinking.

Establishment of Trust Mechanism and Trust Crisis--The Influence of Scientific Symbols in Advertisements on Social Trust

Due to the refinement of professional knowledge, individuals have to trust authority in every field in a limited life, and social trust is constituted in this way. Cosmetics merchants add a lot of scientific symbols to advertisements to shape themselves as authorities in order to make people trust their products. However, with the phenomenon of advertising fraud exposed by the news media, scientific symbols in these advertisements have triggered customers' "trust crisis". This paper analyzes the establishment of trust mechanisms and trust crises through the scientific symbols in advertisements from the perspective of knowledge power. In the end, it puts forward three ways to alleviate the crisis of trust and puts forward suggestions from the government, legislature and intellectual elite, respectively. For example, to prevent some brands from attracting consumers by establishing knowledge power, including creating pseudo-scientific concepts, purposefully confounding scientific concepts, inflating product efficacy, and publicizing scientific conclusions that cannot be validated, the government should supervise the advertisements well as the products' quality.

Enabling Knowledge Refinement upon New Concepts in Abductive Learning

Recently there are great efforts on leveraging machine learning and logical reasoning. Many approaches start from a given knowledge base, and then try to utilize the knowledge to help machine learning. In real practice, however, the given knowledge base can often be incomplete or even noisy, and thus, it is crucial to develop the ability of knowledge refinement or enhancement. This paper proposes to enable the Abductive learning (ABL) paradigm to have the ability of knowledge refinement/enhancement. In particular, we focus on the problem that, in contrast to closed-environment tasks where a fixed set of symbols are enough to represent the concepts in the domain, in open-environment tasks new concepts may emerge. Ignoring those new concepts can lead to significant performance decay, whereas it is challenging to identify new concepts and add them to the existing knowledge base with potential conflicts resolved. We propose the ABL_nc approach which exploits machine learning in ABL to identify new concepts from data, exploits knowledge graph to match them with entities, and refines existing knowledge base to resolve conflicts. The refined/enhanced knowledge base can then be used in the next loop of ABL and help improve the performance of machine learning. Experiments on three neuro-symbolic learning tasks verified the effectiveness of the proposed approach.

Ethanol Sclerosis Therapy for Aggressive Vertebral Hemangioma of the Spine: A Narrative Review.

Vertebral hemangiomas of the spine are rare benign tumors. They occur primarily in the thoracic region and are often asymptomatic and found incidentally on radiological examination; however, some are symptomatic, aggressive, and gradually increase in size. Various therapeutic approaches have been proposed for their management. This study aimed to review the therapeutic management, focusing on ethanol sclerosis therapy. The PubMed database was searched from inception to January 2023 using the keywords "hemangioma", "spine OR vertebra", and "ethanol". Twenty studies were retrieved, including two letters. The first report of spinal therapy was published in 1994. Ethanol sclerosis therapy is effective in treating vertebral hemangiomas. It is performed independently or in combination with other techniques, such as vertebroplasty using cement and surgery. The therapy is performed under local or general anesthesia with fluoroscopic or computed tomography guidance. A total of 10-15 mL of ethanol is slowly injected via unilateral or bilateral pedicles. Complications of the therapy include hypotension and arrhythmia during the procedure, paralysis immediately after the procedure, and delayed compression fractures. This review could enable the refinement of knowledge regarding ethanol sclerosis therapy, which is a treatment option that could be adopted.

Discovery, mode of action, resistance mechanisms, and plan of action for sustainable use of Group 14 herbicides

AbstractProtoporphyrinogen oxidase (PPO)-inhibiting herbicides remain an important and useful chemistry 60 yr after their first introduction. In this review, based on topics introduced at the Weed Science Society of America 2021 symposium titled “A History, Overview, and Plan of Action on PPO Inhibiting Herbicides,” we discuss the current state of PPO-inhibiting herbicides. Renewed interest in the PPO-inhibiting herbicides in recent years, due to increased use and increased cases of resistance, has led to refinements in knowledge regarding the mechanism of action of PPO inhibitors. Herein we discuss the importance of the two isoforms of PPO in plants, compile a current knowledge of target-site resistance mechanisms, examine non–target site resistance cases, and review crop selectivity mechanisms. Consistent and reproducible greenhouse screening and target-site mutation assays are necessary to effectively study and compare PPO-inhibitor resistance cases. To this end, we cover best practices in screening to accurately identify resistance ratios and properly interpret common screens for point mutations. The future of effective and sustainable PPO-inhibitor use relies on development of new chemistries that maintain activity on resistant biotypes and the promotion of responsible stewardship of PPO inhibitors both new and old. We present the biorational design of the new PPO inhibitor trifludimoxazin to highlight the future of PPO-inhibitor development and discuss the elements of sustainable weed control programs using PPO inhibitors, as well as how responsible stewardship can be incentivized. The sustained use of PPO inhibitors in future agriculture relies on the effective and timely communication from mode of action and resistance research to agronomists, Extension workers, and farmers.

Clinical reasoning pattern used in oral health problem solving – A case study in Indonesian undergraduate dental students

BackgroundHealth professionals are known to use various combinations of knowledge and skills, such as critical thinking, clinical reasoning, clinical judgment, problem-solving, and decision-making, in conducting clinical practice. Clinical reasoning development is influenced by knowledge and experience, the more knowledge and experience, the more sophisticated clinical reasoning will be. However, clinical reasoning research in dentistry shows varying results .AimsThis study aims to observe the clinical reasoning pattern of undergraduate dental students when solving oral health problems, and their accordance with their knowledge acquisition.Material and methodsThis qualitative study employed the think-aloud method and the result was assessed through verbal protocol analyses. Five respondents from final year dental undergraduate students were agreed to participate. A unique hypothetical clinical scenario was used as a trigger. The audio data were transcribed, interpreted, and categorized as a clinical reasoning pattern; and the concept maps created were assessed by a Structure of Learning Outcomes (SOLO) taxonomy as knowledge acquisition.ResultsObservations on clinical reasoning patterns and the level of knowledge acquisition in five undergraduate dental students showed varying results. They applied clinical reasoning patterns according to their knowledge acquisition during didactical phase. Learners with inadequate knowledge relied on guessing, meanwhile learners with adequate knowledge applied more sophisticated reasoning pattern when solving problems.ConclusionsVarious problem-solving strategies were encountered in this study, which corresponded to the level of knowledge acquisition. Dental institutions must set minimum standards regarding the acquisition of conceptual knowledge accompanied by improvement of clinical reasoning skills, as well as refinement of knowledge and procedural skills.