Displaying publications 41 - 60 of 908 in total

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  1. Achike FI, Kwan DCY
    JUMMEC, 1997;2:89-93.
    Matched MeSH terms: Learning; Problem-Based Learning
  2. Babini MH, Kulish VV, Namazi H
    J Med Internet Res, 2020 06 01;22(6):e17945.
    PMID: 32478661 DOI: 10.2196/17945
    BACKGROUND: Education and learning are the most important goals of all universities. For this purpose, lecturers use various tools to grab the attention of students and improve their learning ability. Virtual reality refers to the subjective sensory experience of being immersed in a computer-mediated world, and has recently been implemented in learning environments.

    OBJECTIVE: The aim of this study was to analyze the effect of a virtual reality condition on students' learning ability and physiological state.

    METHODS: Students were shown 6 sets of videos (3 videos in a two-dimensional condition and 3 videos in a three-dimensional condition), and their learning ability was analyzed based on a subsequent questionnaire. In addition, we analyzed the reaction of the brain and facial muscles of the students during both the two-dimensional and three-dimensional viewing conditions and used fractal theory to investigate their attention to the videos.

    RESULTS: The learning ability of students was increased in the three-dimensional condition compared to that in the two-dimensional condition. In addition, analysis of physiological signals showed that students paid more attention to the three-dimensional videos.

    CONCLUSIONS: A virtual reality condition has a greater effect on enhancing the learning ability of students. The analytical approach of this study can be further extended to evaluate other physiological signals of subjects in a virtual reality condition.

    Matched MeSH terms: Learning/physiology*
  3. Rajhans V, Mohammed CA, Ve RS, Prabhu A
    Educ Health (Abingdon), 2021 7 3;34(1):22-28.
    PMID: 34213440 DOI: 10.4103/efh.EfH_69_20
    Background: Current trends in health professions education are aligned to meet the needs of the millennial learner. The aim of this study was to identify learners' perceptions of an ongoing journal club (JC) activity in the optometry curriculum and evaluate the utility and efficiency of this method in promoting student learning.

    Methods: A qualitative approach with a phenomenological research design was adopted. The perceptions of undergraduate and postgraduate optometry students about JCs were captured using focus group discussions. A narrative thematic analysis was done using the verbatim transcripts and moderator's notes. Results are reported using "consolidated criteria for reporting qualitative research" guidelines.

    Results: A total of 33 optometry students participated in the study. Data analysis revealed three major themes related to (i) The ongoing practice of JC, (ii) student perceptions of JC and its relevance in facilitating student learning, and (iii) suggestions for modification of JC for achieving optimal educational outcomes.

    Discussion: Student feedback indicates that an instructional redesigning of JC is necessary, considering the characteristics and expectations of the current generation of learners and the rapid strides made in the field of educational technology. The recommendations provided are likely to resurrect an age-old approach that still has educational relevance if blended with collaborative learning formats and appropriate technology.

    Matched MeSH terms: Learning*
  4. Tan JK, Nazar FH, Makpol S, Teoh SL
    Molecules, 2022 Oct 30;27(21).
    PMID: 36364200 DOI: 10.3390/molecules27217374
    Learning and memory are essential to organism survival and are conserved across various species, especially vertebrates. Cognitive studies involving learning and memory require using appropriate model organisms to translate relevant findings to humans. Zebrafish are becoming increasingly popular as one of the animal models for neurodegenerative diseases due to their low maintenance cost, prolific nature and amenability to genetic manipulation. More importantly, zebrafish exhibit a repertoire of neurobehaviors comparable to humans. In this review, we discuss the forms of learning and memory abilities in zebrafish and the tests used to evaluate the neurobehaviors in this species. In addition, the pharmacological studies that used zebrafish as models to screen for the effects of neuroprotective and neurotoxic compounds on cognitive performance will be summarized here. Lastly, we discuss the challenges and perspectives in establishing zebrafish as a robust model for cognitive research involving learning and memory. Zebrafish are becoming an indispensable model in learning and memory research for screening neuroprotective agents against cognitive impairment.
    Matched MeSH terms: Learning*
  5. Chua SL, Foo LK, Guesgen HW, Marsland S
    Sensors (Basel), 2022 Nov 03;22(21).
    PMID: 36366154 DOI: 10.3390/s22218458
    Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.
    Matched MeSH terms: Machine Learning*
  6. Hentabli H, Bengherbia B, Saeed F, Salim N, Nafea I, Toubal A, et al.
    Int J Mol Sci, 2022 Oct 30;23(21).
    PMID: 36362018 DOI: 10.3390/ijms232113230
    Determining and modeling the possible behaviour and actions of molecules requires investigating the basic structural features and physicochemical properties that determine their behaviour during chemical, physical, biological, and environmental processes. Computational approaches such as machine learning methods are alternatives to predicting the physiochemical properties of molecules based on their structures. However, the limited accuracy and high error rates of such predictions restrict their use. In this paper, a novel technique based on a deep learning convolutional neural network (CNN) for the prediction of chemical compounds' bioactivity is proposed and developed. The molecules are represented in the new matrix format Mol2mat, a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. To evaluate the performance of the proposed methods, a series of experiments were conducted using two standard datasets, namely the MDL Drug Data Report (MDDR) and Sutherland, datasets comprising 10 homogeneous and 14 heterogeneous activity classes. After analysing the eight fingerprints, all the probable combinations were investigated using the five best descriptors. The results showed that a combination of three fingerprints, ECFP4, EPFP4, and ECFC4, along with a CNN activity prediction process, achieved the highest performance of 98% AUC when compared to the state-of-the-art ML algorithms NaiveB, LSVM, and RBFN.
    Matched MeSH terms: Machine Learning*
  7. Hussain S, Mustafa MW, Al-Shqeerat KHA, Saeed F, Al-Rimy BAS
    Sensors (Basel), 2021 Dec 17;21(24).
    PMID: 34960516 DOI: 10.3390/s21248423
    This study presents a novel feature-engineered-natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data pre-processing, feature engineering, and model evaluation. It utilized the random forest algorithm-based imputation technique initially to impute the missing data entries in the acquired smart meter dataset. In the second phase, the majority weighted minority oversampling technique (MWMOTE) algorithm was used to avoid an unequal distribution of data samples among different classes. The time-series feature-extraction library and whale optimization algorithm were utilized to extract and select the most relevant features from the kWh reading of consumers. Once the most relevant features were acquired, the model training and testing process was initiated by using the NGBoost algorithm to classify the consumers into two distinct categories ("Healthy" and "Theft"). Finally, each input feature's impact (positive or negative) in predicting the target variable was recognized with the tree SHAP additive-explanations algorithm. The proposed framework achieved an accuracy of 93%, recall of 91%, and precision of 95%, which was greater than all the competing models, and thus validated its efficacy and significance in the studied field of research.
    Matched MeSH terms: Machine Learning*
  8. Muthaiyah S, Phang K, Sembakutti S
    F1000Res, 2021;10:892.
    PMID: 35035890 DOI: 10.12688/f1000research.72880.1
    Background: Changing trends in the use of technology have become an impelling force to be reckoned with for the accounting and finance profession. The curriculum offered in higher learning institutions must be quickly revamped so that students who complete a bachelor's degree are digitally competent upon graduation. With US$55.3 billion invested in FinTech in 2019 alone and more than 72% of accounting jobs being automated, graduates must be trained on digital skills to be future proof. Accounting and finance graduates must be made competent in skills that are related to digital content such as blockchain technology, information assets and autonomous peer to peer systems, to name a few. Methods: We used a three-phase approach: 1) careful mapping of digital topics taught within the course structure offered at these institutions; 2) review of current best practices and digital learning tools for digital inclusion which was ascertained from literature; and 3) 80 experts in a think tank group were interviewed on antecedents, awareness and problems in relation to digital inclusion within the curriculum to validate our research objective. Results: Eleven key tools for inclusion in the curriculum were discussed with experts and then mapped to current curriculum offered at institutions. We discovered that less than 5% of these were being taught. In total, 78% of experts agreed that digital content is inevitable, 90% agreed that digital inclusion based on tools that were discussed will yield great benefits for students, and lastly 75% agreed that giving digital exposure to students must be standard practice. Conclusions: The response from experts confirms that digital inclusion is imperative, but instructors themselves lacked the know-how of emerging technologies. Only the curriculum of institutions with approved bachelor's programs were included in this research. In our future work we hope to include all institutions and professional bodies as well.
    Matched MeSH terms: Learning*
  9. Adnan MSG, Siam ZS, Kabir I, Kabir Z, Ahmed MR, Hassan QK, et al.
    J Environ Manage, 2023 Jan 15;326(Pt B):116813.
    PMID: 36435143 DOI: 10.1016/j.jenvman.2022.116813
    Globally, many studies on machine learning (ML)-based flood susceptibility modeling have been carried out in recent years. While majority of those models produce reasonably accurate flood predictions, the outcomes are subject to uncertainty since flood susceptibility models (FSMs) may produce varying spatial predictions. However, there have not been many attempts to address these uncertainties because identifying spatial agreement in flood projections is a complex process. This study presents a framework for reducing spatial disagreement among four standalone and hybridized ML-based FSMs: random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and hybridized genetic algorithm-gaussian radial basis function-support vector regression (GA-RBF-SVR). Besides, an optimized model was developed combining the outcomes of those four models. The southwest coastal region of Bangladesh was selected as the case area. A comparable percentage of flood potential area (approximately 60% of the total land areas) was produced by all ML-based models. Despite achieving high prediction accuracy, spatial discrepancy in the model outcomes was observed, with pixel-wise correlation coefficients across different models ranging from 0.62 to 0.91. The optimized model exhibited high prediction accuracy and improved spatial agreement by reducing the number of classification errors. The framework presented in this study might aid in the formulation of risk-based development plans and enhancement of current early warning systems.
    Matched MeSH terms: Machine Learning*
  10. Wang C, Omar Dev RD, Soh KG, Mohd Nasirudddin NJ, Yuan Y, Ji X
    Front Public Health, 2023;11:1073423.
    PMID: 36969628 DOI: 10.3389/fpubh.2023.1073423
    This review aims to provide a detailed overview of the current status and development trends of blended learning in physical education by reviewing journal articles from the Web of Science (WOS) database. Several dimensions of blended learning were observed, including research trends, participants, online learning tools, theoretical frameworks, evaluation methods, application domains, Research Topics, and challenges. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a total of 22 journal articles were included in the current review. The findings of this review reveal that the number of blended learning articles in physical education has increased since 2018, proving that the incorporation of online learning tools into physical education courses has grown in popularity. From the reviewed journal articles, most attention is given to undergraduates, emphasizing that attention in the future should be placed on K-12 students, teachers, and educational institutions. The theoretical framework applied by journal articles is also limited to a few articles and the assessment method is relatively homogeneous, consisting mostly of questionnaires. This review also discovers the trends in blended learning in physical education as most of the studies focus on the topic centered on dynamic physical education. In terms of Research Topics, most journal articles focus on perceptions, learning outcomes, satisfaction, and motivation, which are preliminary aspects of blended learning research. Although the benefits of blended learning are evident, this review identifies five challenges of blended learning: instructional design challenges, technological literacy and competency challenges, self-regulation challenges, alienation and isolation challenges, and belief challenges. Finally, a number of recommendations for future research are presented.
    Matched MeSH terms: Learning*
  11. Zehra S, Faseeha U, Syed HJ, Samad F, Ibrahim AO, Abulfaraj AW, et al.
    Sensors (Basel), 2023 Jun 05;23(11).
    PMID: 37300067 DOI: 10.3390/s23115340
    Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.
    Matched MeSH terms: Machine Learning*
  12. Wang X, Yu L, Wang Z
    J Environ Public Health, 2022;2022:9602876.
    PMID: 36200091 DOI: 10.1155/2022/9602876
    Blended learning has become the dominant teaching approach in colleges and universities as they evolve. A good learning environment design can represent college and university teaching quality, improve undergraduates' literacy, and boost talent training. This paper introduces the data mining method of undergraduate comprehensive literacy education, discovers the association rules of the evaluation data, and introduces the undergraduate comprehensive literacy evaluation model and BP neural network model driven by theory and technology in a mixed learning environment, which promotes students' comprehensive literacy evaluation and builds a good learning environment. The results demonstrate that undergraduate classification prediction accuracy is similar by data mining, and most reach 99.58 percent. So, whether it is the training sample or the test sample, the prediction result of undergraduate comprehensive literacy is acceptable, which illustrates the validity of the data mining algorithm model and has strong application importance for developing a better learning environment.
    Matched MeSH terms: Learning*
  13. Asim Shahid M, Alam MM, Mohd Su'ud M
    PLoS One, 2023;18(4):e0284209.
    PMID: 37053173 DOI: 10.1371/journal.pone.0284209
    The benefits and opportunities offered by cloud computing are among the fastest-growing technologies in the computer industry. Additionally, it addresses the difficulties and issues that make more users more likely to accept and use the technology. The proposed research comprised of machine learning (ML) algorithms is Naïve Bayes (NB), Library Support Vector Machine (LibSVM), Multinomial Logistic Regression (MLR), Sequential Minimal Optimization (SMO), K Nearest Neighbor (KNN), and Random Forest (RF) to compare the classifier gives better results in accuracy and less fault prediction. In this research, the secondary data results (CPU-Mem Mono) give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80/20 (77.01%), 70/30 (76.05%), and 5 folds cross-validation (74.88%), and (CPU-Mem Multi) in terms of 80/20 (89.72%), 70/30 (90.28%), and 5 folds cross-validation (92.83%). Furthermore, on (HDD Mono) the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80/20 (87.72%), 70/30 (89.41%), and 5 folds cross-validation (88.38%), and (HDD-Multi) in terms of 80/20 (93.64%), 70/30 (90.91%), and 5 folds cross-validation (88.20%). Whereas, primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80/20 (97.14%), 70/30 (96.19%), and 5 folds cross-validation (95.85%) in the primary data results, but the algorithm complexity (0.17 seconds) is not good. In terms of 80/20 (95.71%), 70/30 (95.71%), and 5 folds cross-validation (95.71%), SMO has the second highest accuracy and less fault prediction, but the algorithm complexity is good (0.3 seconds). The difference in accuracy and less fault prediction between RF and SMO is only (.13%), and the difference in time complexity is (14 seconds). We have decided that we will modify SMO. Finally, the Modified Sequential Minimal Optimization (MSMO) Algorithm method has been proposed to get the highest accuracy & less fault prediction errors in terms of 80/20 (96.42%), 70/30 (96.42%), & 5 fold cross validation (96.50%).
    Matched MeSH terms: Machine Learning*
  14. Menon S, Anand D, Kavita, Verma S, Kaur M, Jhanjhi NZ, et al.
    Sensors (Basel), 2023 Jul 04;23(13).
    PMID: 37447981 DOI: 10.3390/s23136132
    With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.
    Matched MeSH terms: Machine Learning; Learning
  15. Lim JY, Lim KM, Lee CP, Tan YX
    Neural Netw, 2023 Aug;165:19-30.
    PMID: 37263089 DOI: 10.1016/j.neunet.2023.05.037
    Few-shot learning aims to train a model with a limited number of base class samples to classify the novel class samples. However, to attain generalization with a limited number of samples is not a trivial task. This paper proposed a novel few-shot learning approach named Self-supervised Contrastive Learning (SCL) that enriched the model representation with multiple self-supervision objectives. Given the base class samples, the model is trained with the base class loss. Subsequently, contrastive-based self-supervision is introduced to minimize the distance between each training sample with their augmented variants to improve the sample discrimination. To recognize the distant sample, rotation-based self-supervision is proposed to enable the model to learn to recognize the rotation degree of the samples for better sample diversity. The multitask environment is introduced where each training sample is assigned with two class labels: base class label and rotation class label. Complex augmentation is put forth to help the model learn a deeper understanding of the object. The image structure of the training samples are augmented independent of the base class information. The proposed SCL is trained to minimize the base class loss, contrastive distance loss, and rotation class loss simultaneously to learn the generic features and improve the novel class performance. With the multiple self-supervision objectives, the proposed SCL outperforms state-of-the-art few-shot approaches on few-shot image classification benchmark datasets.
    Matched MeSH terms: Learning*
  16. Ong SQ, Isawasan P, Ngesom AMM, Shahar H, Lasim AM, Nair G
    Sci Rep, 2023 Nov 05;13(1):19129.
    PMID: 37926755 DOI: 10.1038/s41598-023-46342-2
    Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models. We trained and validated seven ML algorithms, including an ensemble ML method, and compared their performance using the receiver operating characteristic (ROC) with the area under the curve (AUC), accuracy and F1 score. Our results show that an ensemble ML such as XG Boost, AdaBoost and Random Forest perform better than the logistics regression, Naïve Bayens, decision tree, and support vector machine (SVM), with XGBoost having the highest AUC, accuracy and F1 score. Analysis of the importance of the variables showed that the container index was the least important. By removing this variable, the ML models improved their performance by at least 6% in AUC and F1 score. Our result provides a framework for future studies on the use of predictive models in the development of an early warning system.
    Matched MeSH terms: Machine Learning*
  17. AlThuwaynee OF, Kim SW, Najemaden MA, Aydda A, Balogun AL, Fayyadh MM, et al.
    Environ Sci Pollut Res Int, 2021 Aug;28(32):43544-43566.
    PMID: 33834339 DOI: 10.1007/s11356-021-13255-4
    This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction variables. A variable drop-off loop function, based on the concept of early termination for reduction of model capacity, regularization, and generalization control, was tested. A susceptibility index for airborne particulate matter of less than 10 μm diameter (PM10) was modeled using monthly maximum values and spectral bands and indices from Landsat 8 imagery, and Open Street Maps were used to prepare a range of independent variables. Probability and classification index maps were prepared using extreme-gradient boosting (XGBOOST) and random forest (RF) algorithms. These were assessed against utility criteria such as a confusion matrix of overall accuracy, quantity of variables, processing delay, degree of overfitting, importance distribution, and area under the receiver operating characteristic curve (ROC).
    Matched MeSH terms: Machine Learning*
  18. Azer SA
    Kaohsiung J. Med. Sci., 2009 May;25(5):240-9.
    PMID: 19502144 DOI: 10.1016/S1607-551X(09)70068-3
    Problem-based learning (PBL) is an excellent opportunity for students to take responsibility for their learning and to develop a number of cognitive skills. These include identifying problems in the trigger, generating hypotheses, constructing mechanisms, developing an enquiry plan, ranking their hypotheses on the basis of available evidence, interpreting clinical and laboratory findings, identifying their learning needs, and dealing with uncertainty. Students also need to work collaboratively in their group, communicate effectively, and take active roles in the tutorials. Therefore, interaction in the group between students and their tutor is vital to ensure deep learning and successful outcomes. The aims of this paper are to discuss the key principles for successful interaction in PBL tutorials and to highlight the major symptoms of superficial learning and poor interactions. This comprises a wide range of symptoms for different group problems, including superficial learning. By early detection of such problems, tutors will be able to explore actions with the group and negotiate changes that can foster group dynamics and enforce deep learning.
    Matched MeSH terms: Learning; Problem-Based Learning*
  19. Lau MN, Sivarajan S, Kamarudin Y, Othman SA, Wan Hassan WN, Soh EX, et al.
    J Dent Educ, 2022 Nov;86(11):1477-1487.
    PMID: 35650663 DOI: 10.1002/jdd.12954
    OBJECTIVE: This study aimed to explore students' perceptions of flipped classroom (FC) compared to live demonstration (LD) in transferring skills of fabricating orthodontic wire components for orthodontic removable appliances.

    METHODS: Forty third-year undergraduate dental students were randomly assigned to two groups: FC (n = 20) and LD (n = 20). Students in group FC attended FC, while students in group LD attended LD. Both groups underwent a series of standardized teaching sessions to acquire skills in fabricating six types of orthodontic wire components. Eight students (four high achievers and four low achievers) from each group were randomly selected to attend separate focus group discussion (FGD) sessions. Students' perceptions on the strengths, weaknesses, and suggestions for improvement on each teaching method were explored. Audio and video recordings of FGD were transcribed and thematically analyzed using NVivo version 12 software.

    RESULTS: Promoting personalized learning, improvement in teaching efficacy, inaccuracy of three-dimensional demonstration from online video, and lack of standardization among instructors and video demonstration were among the themes identified. Similarly, lack of standardization among instructors was one of the themes identified for LD, in addition to other themes such as enabling immediate clarification and vantage point affected by seating arrangement and class size.

    CONCLUSIONS: In conclusion, FC outperformed LD in fostering personalized learning and improving the efficacy of physical class time. LD was more advantageous than FC in allowing immediate question and answer. However, seating arrangement and class size affected LD in contrast to FC.

    Matched MeSH terms: Learning*; Problem-Based Learning
  20. Kim YJ
    Medicine (Baltimore), 2023 Sep 29;102(39):e35143.
    PMID: 37773837 DOI: 10.1097/MD.0000000000035143
    The objective of this study was to investigate the impact of the problem-based learning (PBL) method on Neurology education for Traditional Chinese Medicine (TCM) undergraduate students. This observational study was conducted during the 2020/02 and 2020/04 intakes of the third year TCM undergraduate students at School of Traditional Chinese Medicine, Xiamen University Malaysia. A total of 86 students were enrolled in the study and randomly assigned to either conventional learning groups or PBL groups. Students who missed more than 1 session of the course or did not complete the questionnaires during the evaluation periods were excluded from the study (n = 0). An independent sample t test was used to compare the results between the 2 groups, with a significance level set as P 
    Matched MeSH terms: Learning; Problem-Based Learning/methods
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