Displaying publications 61 - 80 of 986 in total

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  1. 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*
  2. Zhang J, Geok Soh K, Bai X, Mohd Anuar MA, Xiao W
    PLoS One, 2024;19(12):e0311957.
    PMID: 39630649 DOI: 10.1371/journal.pone.0311957
    BACKGROUND: There is a notable gap in systematic reviews concerning hybrid pedagogical models (PMs) integrated with the Sport Education Model (SEM) and their impact on students' outcomes.

    PURPOSE: Which hybrid PMs incorporating SEM are currently the mainstream choices in research, and what are the main factors supporting their integration? How does SEM function as a foundational model in these hybrid teaching approaches? What learning outcomes are optimized through the hybrid models that combine SEM with other PMs?

    METHODS: A systematic search was conducted in major databases in December 2023 following PRISMA guidelines. Out of the identified 1342 studies, 30 met the eligibility criteria, all of which were deemed to be of high quality.

    RESULTS: Seven hybrid types were identified, primarily composed of two PMs, among which the blend of SEM and Teaching Games for Understanding (TGfU) emerges as the mainstream in current research. SEM, serving as the foundational structure, provides a stable framework for the hybrid, termed the "SEM + 1 model," yielding positive effects on enhancing students' learning outcomes.

    CONCLUSIONS: Pedagogical models align with PMs' motivational aspects, thus enhancing learning outcomes. However, evidence for partial hybrids is lacking. Future research should explore diverse interventions, addressing coherence and teacher competence, while maintaining fidelity.

    Matched MeSH terms: Learning*
  3. Abu Bakar YI, Hassan A, Yusoff MSB, Kasim F, Abdul Manan Sulong H, Hadie SNH
    Anat Sci Educ, 2022 Jan;15(1):166-177.
    PMID: 33650315 DOI: 10.1002/ase.2067
    To become skilled physicians, medical students must master surface anatomy. However, the study of surface anatomy is less emphasized in medical and allied health science curricula, and the time devoted to direct engagement with the human body is limited. This scoping review was designed to answer one research question: "What are the elements and strategies that are effective in teaching surface anatomy?" The review was performed using a five-stage scoping review framework, including research question identification, relevant study identification, study selection, data charting, and result collating and reporting. Three databases were searched using two search terms combined with a Boolean operator: "teaching" and "surface anatomy." The initial pool of 3,294 sources was assessed for duplication, and study eligibility was evaluated using inclusion and exclusion criteria. Data were abstracted from 26 original articles by one researcher and verified by two other researchers. A thematic analysis was performed, and several elements of effective teaching strategies for surface anatomy were identified, namely contextualized teaching, embracing experiential learning, and learning facilitation. This review revealed that a multimodal approach was most commonly used in surface anatomy instruction. Hence, future research should explore the effectiveness of multimodal teaching strategies that adopt the three aforementioned primary elements of effective teaching in an authentic learning environment. It is pertinent to clarify the effectiveness of these teaching strategies by evaluating their impact on student learning, organizational changes, and benefits to other stakeholders.
    Matched MeSH terms: Learning; Problem-Based Learning
  4. Yeow MYH, Chong CY, Lim MK, Yee Yen Y
    PLoS One, 2025;20(2):e0314512.
    PMID: 39946354 DOI: 10.1371/journal.pone.0314512
    Software reuse is an essential practice to increase efficiency and reduce costs in software production. Software reuse practices range from reusing artifacts, libraries, components, packages, and APIs. Identifying suitable software for reuse requires pinpointing potential candidates. However, there are no objective methods in place to measure software reuse. This makes it challenging to identify highly reusable software. Software reuse research mainly addresses two hurdles: 1) identifying reusable candidates effectively and efficiently, and 2) selecting high-quality software components that improve maintainability and extensibility. This paper proposes automating software reuse prediction by leveraging machine learning (ML) algorithms, enabling future research and practitioners to better identify highly reusable software. Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. Software metrics were extracted from Maven artifacts and used to train classification and regression models to predict and estimate software reuse. The average F1-score of the ML classification models is 77.19%. The best-performing model, Ridge Regression, achieved an F1-score of 79.17%. Additionally, this research aims to assist developers by identifying key metrics that significantly impact software reuse. Our findings suggest that the file-level PUA (Public Undocumented API) metric is the most important factor influencing software reuse. We also present suitable value ranges for the top five important metrics that developers can follow to create highly reusable software. Furthermore, we developed a tool that utilizes the trained models to predict the reuse potential of existing GitHub projects and rank Maven artifacts by their domain.
    Matched MeSH terms: Machine Learning*
  5. Hassan MK, Syed Ariffin SH, Ghazali NE, Hamad M, Hamdan M, Hamdi M, et al.
    Sensors (Basel), 2022 May 09;22(9).
    PMID: 35591282 DOI: 10.3390/s22093592
    Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.
    Matched MeSH terms: Machine Learning*
  6. Rosa D, Elya B, Hanafi M, Khatib A, Budiarto E, Nur S, et al.
    PLoS One, 2025;20(1):e0313592.
    PMID: 39752479 DOI: 10.1371/journal.pone.0313592
    One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics analysis of Artabotrys sumatranus leaf extract was used in this research to predict the active compounds as α-glucosidase inhibitors from this extract. Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions. After performance comparisons with other machine learning methods, random forest was chosen to make predictive model for the activity of the extract samples. Feature importance analysis (using random feature permutation and Shapley score calculation) was used to identify the predicted active compound as the important features that influenced the activity prediction of the extract samples. The combined analysis of multivariate statistical analysis and machine learning predicted 9 active compounds, where 6 of them were identified as mangiferin, neomangiferin, norisocorydine, apigenin-7-O-galactopyranoside, lirioferine, and 15,16-dihydrotanshinone I. The activities of norisocorydine, apigenin-7-O-galactopyranoside, and lirioferine as α-glucosidase inhibitors have not yet reported before. Molecular docking simulation, both to 3A4A (α-glucosidase enzyme from Saccharomyces cerevisiae, usually used in bioassay test) and 3TOP (a part of α-glucosidase enzyme in human gut) showed strong to very strong binding of the identified predicted active compounds to both receptors, with exception of neomangiferin which only showed strong binding to 3TOP receptor. Isolation based on bioassay guided fractionation further verified the metabolomics prediction by succeeding to isolate mangiferin from the extract, which showed strong α-glucosidase activity when subjected to bioassay test. The correlation analysis also showed a possibility of 3 groups in the predicted active compounds, which might be related to the biosynthesis pathway (need further research for verification). Another result from correlation analysis was that in general the α-glucosidase inhibition activity in the extract had strong correlation to antioxidant activity, which was also reflected in the predicted active compounds. Only one predicted compound had very low positive correlation to antioxidant activity.
    Matched MeSH terms: Machine Learning*
  7. 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*
  8. 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
  9. 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
  10. Yap MKK
    Biochem Mol Biol Educ, 2023 Jan;51(1):77-80.
    PMID: 36194083 DOI: 10.1002/bmb.21680
    Experiential learning is compromised in meeting the educational demands of our students during the challenging time of the COVID-19 pandemic. A more inclusive, flexible, and objective-oriented experiential learning environment is required. In this context, module-based experiential learning that is executable on a digital platform was designed. The learning module focused on protein biochemistry, contained a combination of asynchronous and synchronous activities categorized into 'Knowledge Hub' and 'Lab-based Movie', across 5 weeks. Digital and module-based experiential learning provides equitable, inclusive, and flexible access to students at remote locations. Furthermore, it is an objective-oriented and highly organized experiential learning framework that encourages students to engage and participate more in the learning process.
    Matched MeSH terms: Learning; Problem-Based Learning*
  11. Othman SA, Kamarudin Y, Sivarajan S, Soh EX, Lau MN, Zakaria NN, et al.
    Eur J Dent Educ, 2023 Aug;27(3):419-427.
    PMID: 35579042 DOI: 10.1111/eje.12823
    OBJECTIVE: To explore students' perception on the implementation of flipped classroom (FC) combined with formative assessment during the undergraduate teaching of orthodontic wire-bending skills.

    METHODS: Third-year undergraduate dental students were taught wire-bending skills via FC teaching method using a series of pre-recorded online video demonstrations. As part of the formative assessment, the students were given the results and assessment rubrics of their prior wire-bending assessment before every subsequent session. Purposive sampling method for focus group discussion was used to recruit eight students comprising four high achievers and four low achievers. Strengths, weaknesses and suggestions for improvement of the FC with formative assessment were explored. Data were transcribed and thematically analysed.

    RESULTS: Students perceived that FC allowed for a more convenient and flexible learning experience with personalised learning and improved in-class teaching efficiency. The pre-recorded online videos were useful to aid in teaching wire-bending skills but lacked three-dimensional representation of the wire-bending process. Students suggested better standardisation of instructions and access to the marking rubric before and after assessment.

    CONCLUSIONS: FC teaching with continuous formative assessment and constructive feedback as a form of personalised learning was viewed favourably by students. The implementation of periodic individual feedback can further enhance their learning experience.

    Matched MeSH terms: Learning*; Problem-Based Learning
  12. Lin GSS, Tan WW, Foong CC
    Eur J Dent Educ, 2023 Nov;27(4):956-962.
    PMID: 36527313 DOI: 10.1111/eje.12887
    INTRODUCTION: Limited studies have been conducted on the use of a hybrid team-based learning (TBL) and case-based learning (CBL) approach in dental education. The present study aims to evaluate students' experience of the hybrid TBL-CBL in learning dental materials science subjects.

    METHODS: All second-year undergraduate Bachelor of Dental Surgery (BDS) students were invited to participate in a TBL-CBL session. These participants were randomly allocated to six different groups of 10-12 students, and the session was conducted by one lecturer as the facilitator. A 23-item questionnaire assessing four domains (perceptions of effectiveness, teacher, team interaction and learning environment) was administered at the end of the TBL-CBL session.

    RESULTS: The response rate was 91.9% (n = 68). Mean scores for the questionnaire items ranged from 4.13 to 4.60 suggesting a positive perception among the students towards the hybrid TBL-CBL approach. Regarding the open-response questions, students emphasised that the TBL-CBL session was effective for team interaction and group discussions. However, students wished to have a better venue for future sessions.

    CONCLUSION: Positive perceptions of the students encourage future educators to consider the use of TBL-CBL approach in teaching dental materials science and to avoid the reliance on standalone conventional lectures. Future research could consider examining its effects on students' academic achievement as well as the perspectives of teachers regarding its adoption in different dental specialities.

    Matched MeSH terms: Learning; Problem-Based Learning*
  13. Azer SA
    Kaohsiung J. Med. Sci., 2008 Jul;24(7):361-6.
    PMID: 18805751 DOI: 10.1016/S1607-551X(08)70133-5
    Portfolios have been used in the medical curriculum to evaluate difficult-to-assess areas such as students' attitudes, professionalism and teamwork. However, their use early in a problem-based learning (PBL) course to foster deep learning and enhance students' self-directed learning has not been adequately studied. The aims of this paper are to: (1) understand the uses of portfolios and the rationale for using reflection in the early years of a PBL curriculum; (2) discuss how to introduce portfolios and encourage students' critical thinking skills, not just reflection; and (3) provide students with tips that could enhance their skills in constructing good portfolios.
    Matched MeSH terms: Learning*; Problem-Based Learning*
  14. Ruslai NH, Salam A
    Pak J Med Sci, 2016 Mar-Apr;32(2):324-8.
    PMID: 27182232 DOI: 10.12669/pjms.322.9248
    Foundational elements of problem based learning (PBL) are triggers, tutors and students. Ineffective triggers are important issues for students' inability to generate appropriate learning issues. The objective of this study was to evaluate PBL triggers and to determine similarities of students' generated learning issues with predetermined faculty objectives.
    Matched MeSH terms: Problem-Based Learning
  15. Jefferelli, S.B., Sunthar, R., Trauth B., Bauder, K.
    MyJurnal
    Good reporting of medical drill is important to optimise learning and benefit from the activity. This article shares our opinion on what constitutes a good medical emergency drill report. A good medical emergency medical drill report should include medical drill background, observation, remarks on observation and details of observers and reporter.
    Matched MeSH terms: Learning
  16. Hasan RI, Yusuf SM, Alzubaidi L
    Plants (Basel), 2020 Oct 01;9(10).
    PMID: 33019765 DOI: 10.3390/plants9101302
    Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.
    Matched MeSH terms: Machine Learning
  17. Tamrin, K.F.
    MyJurnal
    Learning is often quoted as a lifelong process. In other words, life is about learning. As prominent institutions, universities are concerned with valued and measurable learning among undergraduate students so that their mastery level of a particular content knowledge can be quantitatively gauged. Of many types of assessments, summative assessment plays a greater role in majority of engineering courses due to nature of the content knowledge. This paper mathematically investigates the fairness issue of equal weightage for all summative assessments i.e., assignments, mid-term test and end-term examination. A multiple objective optimization on the basis of ratio analysis (MOORA) is utilized to assign equal weight for the aforementioned assessments. It was found that the number of students failing the selected engineering course increases by about five times using the MOORA method. The finding clearly reveals the advantages of the former method (unequal weights) as compared to MOORA method in terms of catering students with different learning styles and speed of knowledge acquisition.
    Matched MeSH terms: Learning
  18. Dympna James Jemson, Sabariah Sharif, Soon Singh A/L Bikar nSingh
    MyJurnal
    Kajian ini dilaksanakan untuk melihat penerimaan terhadap penggunaan mobile learning di kalangan pelajar tingkatan 6 dalam mata pelajaran geografi. Kaedah kuantitatif digunakan dalam kajian ini yang melibatkan sebuah pusat tingkatan 6 yang terletak di daerah Kota Kinabalu, Sabah seramai 137 orang pelajar. Semua pelajar yang mengambil mata pelajaran geografi telah diambil sebagai sampel kajian. Borang soal selidik digunakan sebagai instrumen dalam kajian ini yang telah diubahsuai dan dirujuk daripada Technology Acceptance Model (TAM) untuk menentukan tahap penerimaan terhadap penggunaan mobile learning di kalangan pelajar tingkatan 6 dalam mata pelajaran Geografi. Hasil daripada analisis statistik menunjukkan bahawa tahap penerimaan pelajar terhadap mobile learning berada di tahap yang tinggi.
    Matched MeSH terms: Learning
  19. Rahman MM, Khatun F, Uzzaman A, Sami SI, Bhuiyan MA, Kiong TS
    Int J Health Serv, 2021 10;51(4):446-461.
    PMID: 33999732 DOI: 10.1177/00207314211017469
    The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic's dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.
    Matched MeSH terms: Machine Learning
  20. Fadzilah Siraji, Yong Zulina Zubairi, Abdul Razak Saleh, Rohana Jani, Md. Yusoff Abu Bakar, Md. Radzi Johari
    MyJurnal
    Learning Strategy and Study Inventory (LASSI) merupakan suatu instrumen laporan kendiri yang digunakan untuk menilai strategi pembelajaran berdasarkan model umum pembelajaran kognitif dan model strategik pembelajaran. untuk mendapatkan maklumat tentang strategi pembelajaran pelajar Perubatan dan Pergigian di Institusi Pengajian Tinggi Awam (IPTA) dan Swasta (IPTS). Instrumen yang telah dibangunkan oleh LASSI diadaptasi dan digunapakai. Tiga komponen utama yang diukur dalam LASSI iaitu KEMAHuAn, KEMAHIrAn dan PErATurAn KEnDIrI. Populasi kajian merangkumi pelajar lepasan STPM dan Matrikulasi dari IPTA dan IPTS yang mengikuti program Perubatan serta Pergigian atau program Perubatan sahaja. Secara keseluruhannya, persepsi pelajar menunjukkan keperihatinan pelajar untuk mempelajari maklumat baru, sikap dan minat terhadap bidang yang dipelajari dan disiplin diri amat rendah berbanding pelajar di negara maju. Perbandingan skor pelajar IPTA dan IPTS menunjukkan terdapat perbezaan bagi faktor Kebimbangan, Pemprosesan Maklumat dan Strategi Pengujianan. Perbandingan skor pelajar lepasan Matrikulasi dan STPM pula menunjukkan tiada perbezaan signifikan bagi hampir semua skor bagi faktor LASSI kecuali Mat Bantu Pembelajaran dan Pengujian Kendiri. Perbandingan skor pelajar Perubatan dan Pergigian pula menunjukkan strategi pembelajaran bagi kedua dua kumpulan pelajar yang mengikuti bidang kritikal tersebut adalah sama dengan tiada sebarang perbezaan yang signifikan dalam sebarang faktor LASSI. Dapatan kajian juga menunjukkan bahawa keseluruhan pelajar yang mengikuti program kritikal mempunyai strategi pembelajaran yang kurang baik. Sehubungan itu pihak pengurusan perlu mengambil inisiatif untuk membantu pelajar dalam memperkemaskan strategi pembelajaran mereka. Strategi pembelajaran yang kurang efektif akan mengundang kesan sampingan yang tidak sihat seperti kemurungan atau stress di kalangan pelajar.
    Matched MeSH terms: Learning
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