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Fulltext The need to have a valid and reliable tool to measure the anatomy education environment

Siti Nurma Hanim Hadie, Asma' Hassan, Zul Izhar Mohd Ismail, Mohd Asnizam Asari, Aaijaz Ahmed Khan, Fazlina Kasim, et al.

Education in Medicine Journal, 2013;5(3):81-85.
MyJurnal

Anatomy is an important knowledge for medical practice. Insufficient anatomy knowledge leading to errors in identification of anatomical structures during medical practices has been reported in many countries. Many medical students seem to have difficulties in learning anatomy and retaining the knowledge for future practice, thus this might reflect the possible flaws in anatomy education. In order to achieve optimum anatomy education environment and to close the gaps in education, measuring the students' perception on anatomy teaching and learning is a pre-emptive measure needed by educationists. At present, there is no valid and reliable inventory available to specifically evaluate the anatomy education environment. Therefore, this article highlights the importance of having such inventory.

Matched MeSH terms: Learning
Fulltext ABC of manuscript writing

Muhamad Saiful Bahri Yusof

Education in Medicine Journal, 2018;10(2):61-67.
MyJurnal

This paper describes a systematic and practical guide on manuscript writing. A step-by-step approach
as easy as learning ABC to facilitate authors to plan their manuscript writing. Research has shown that
experienced writers plan extensively, in which a writing plan is a road map, without it we will probably
lose our way in circles. Generally, authors start writing a manuscript by introduction, methods, results,
discussion and conclusion. However, this paper proposes a different approach to start writing a
manuscript based on the ABC of manuscript writing worksheet.

Matched MeSH terms: Learning
Predictor-corrector block iteration method for solving ordinary differential equations

Zanariah Abdul Majid, Mohamed Suleiman

Sains Malaysiana, 2011;40:1179-1186.

Predictor-corrector two point block methods are developed for solving first order ordinary differential equations (ODEs) using variable step size. The method will estimate the solutions of initial value problems (IVPs) at two points simultaneously. The existence multistep method involves the computations of the divided differences and integration coefficients when using the variable step size or variable step size and order. The block method developed will be presented as in the form of Adams Bashforth - Moulton type and the coefficients will be stored in the code. The efficiency of the predictor-corrector block method is compared to the standard variable step and order non block multistep method in terms of total number of steps, maximum error, total function calls and execution times.

Matched MeSH terms: Learning
Rational finite difference approximation of high order Accuracy for nonlinear two point boundary value problems

Pandey P

Sains Malaysiana, 2014;43:1105-1108.

In this paper, we present a new method for solving nonlinear general two point boundary value problems. A method based on finite differences and rational function approximation and we call this method as rational approximation method. A rational approximation method is applied to construct the numerical solution for two point boundary value problems. The novel method is tested on three model problems. Thus the numerical results obtained for these model problems show the performance and efficiency of the developed method.

Matched MeSH terms: Learning
New parallel R-point explicit block method for solving linear high-order ordinary differential equations directly

Zurni Omar, Mohamed Suleiman

Sains Malaysiana, 2009;38.

A new method called parallel R-point explicit block method for solving a single equation of higher order ordinary differential equation directly using a constant step size is developed. This method calculates the numerical solution at R point simultaneously is parallel in nature. Computational advantages are presented by comparing the results obtained with the new method with that of the conventional 1-point method. The numerical results show that the new method reduces the total number of steps and execution time. The accuracy of the parallel block and the conventional 1-point methods is comparable particularly when finer step sizes are used.

Matched MeSH terms: Learning
Fulltext Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition

Habeeb D, Noman F, Alkahtani AA, Alsariera YA, Alkawsi G, Fazea Y, et al.

Comput Intell Neurosci, 2021;2021:3971834.
PMID: 34782832 DOI: 10.1155/2021/3971834

Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.

Matched MeSH terms: Machine Learning
Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review

Kee OT, Harun H, Mustafa N, Abdul Murad NA, Chin SF, Jaafar R, et al.

Cardiovasc Diabetol, 2023 Jan 19;22(1):13.
PMID: 36658644 DOI: 10.1186/s12933-023-01741-7

Prediction model has been the focus of studies since the last century in the diagnosis and prognosis of various diseases. With the advancement in computational technology, machine learning (ML) has become the widely used tool to develop a prediction model. This review is to investigate the current development of prediction model for the risk of cardiovascular disease (CVD) among type 2 diabetes (T2DM) patients using machine learning. A systematic search on Scopus and Web of Science (WoS) was conducted to look for relevant articles based on the research question. The risk of bias (ROB) for all articles were assessed based on the Prediction model Risk of Bias Assessment Tool (PROBAST) statement. Neural network with 76.6% precision, 88.06% sensitivity, and area under the curve (AUC) of 0.91 was found to be the most reliable algorithm in developing prediction model for cardiovascular disease among type 2 diabetes patients. The overall concern of applicability of all included studies is low. While two out of 10 studies were shown to have high ROB, another studies ROB are unknown due to the lack of information. The adherence to reporting standards was conducted based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) standard where the overall score is 53.75%. It is highly recommended that future model development should adhere to the PROBAST and TRIPOD assessment to reduce the risk of bias and ensure its applicability in clinical settings. Potential lipid peroxidation marker is also recommended in future cardiovascular disease prediction model to improve overall model applicability.

Matched MeSH terms: Machine Learning
Response to Commentary on Watt E, Murphy M, Pascoe E, Scanlon A & Gan S (2011) An evaluation of a structured learning programme as a component of the clinical practicum in final year bachelor of nursing programme: a pre-post test analysis. Journal of Clinical Nursing 20, 2286-2293

Ho SE, Das S

J Clin Nurs, 2012 Jan;21(1-2):297-8.
PMID: 22150867 DOI: 10.1111/j.1365-2702.2011.03966.x
Matched MeSH terms: Learning*
Team-based learning in a medical centre in Malaysia: Perspectives of the faculty

Salam A, Mohamad N, Siraj HH, Kamarudin MA, Yaman MN, Bujang SM

Natl Med J India, 2014 Nov-Dec;27(6):350.
PMID: 26133346
Matched MeSH terms: Learning*
Fulltext Applying educational theories into planning a psychomotor learning activity: an undergraduate neonatal resuscitation programme experience

Khoo EJ, Chua SH, Kutzsche S

Arch Argent Pediatr, 2019 04 01;117(2):e181-e187.
PMID: 30869503 DOI: 10.5546/aap.2019.eng.e181

The Neonatal Resuscitation Programme is a good example of an effective educational intervention that has improved perinatal mortality rates in many countries. This paper shares our experience of planning an undergraduate Neonatal Resuscitation Programme using basic principles of education theory of spiral curriculum, Bloom's taxonomy in planning learning outcomes, Kolb's learning model and Miller's model of clinical assessment. Engaging clinicians in pedagogical theories may not be well aligned with how clinicians traditionally thought they learnt best, yet it is key to improving learning concept and educational intervention outcomes in the healthcare professions. This article aims to illustrate the application of such educational theories into one example of practice. We structured this paper in the scope of content, delivery and assessment when planning a psychomotor learning activity.

Matched MeSH terms: Learning; Problem-Based Learning
Photodentistry - an innovative approach to improving students' empathy and learning experiences in comprehensive patient care

Ahmad MS, Radhi DSM, Rusle FF, Zul MF, Jalaluddin J, Baharuddin IH

J Dent Educ, 2020 Nov;84(11):1219-1229.
PMID: 32645212 DOI: 10.1002/jdd.12295

OBJECTIVES: Preparing future dental school graduates to provide comprehensive patient care with empathy requires the completion of adequate training in such practice. This study was undertaken to investigate the effectiveness of the Photodentistry learning activity, which uses visual arts, in improving dental students' empathy and learning experience in comprehensive patient care.
METHODS: All fourth-year undergraduate dental students (n = 69, response rate = 100%) participated in the Photodentistry learning activity developed by specialists from the areas of dentistry, arts, education, and psychology. A survey using the Toronto Empathy Questionnaire (TEQ) was conducted both pretest and posttest, followed by an open-ended written survey of their reflection towards the learning activity. Quantitative data were analyzed via paired t-test (P < 0.05), while qualitative data were analyzed using thematic analysis.
RESULTS: There was a significant increase in both students' total mean empathy score and the individual scores for 8 (out of 16) items of the TEQ after the learning activity. Students stated that they had an improved understanding of managing patients in a comprehensive manner (e.g., managing medically compromised patients, performing treatment planning, communication with patients who have special health care needs). Students also reported the development of skills (e.g., observation, critical thinking) and positive attitudes (e.g., empathy, responsibility) towards patients.
CONCLUSION: Photodentistry is an effective learning approach for improving dental students' empathy and learning experience in comprehensive patient care.

Matched MeSH terms: Learning*
Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning

Woon KL, Chong ZX, Ariffin A, Chan CS

J Mol Graph Model, 2021 06;105:107891.
PMID: 33765526 DOI: 10.1016/j.jmgm.2021.107891

Fused tricyclic organic compounds are an important class of organic electronic materials. In designing molecules for organic electronics, knowing what chemical structure that be used to tune the molecular property is one of the keys that can help to improve the material performance. In this research, we applied machine learning and data analytic approaches in addressing this problem. The energy states (Lowest Unoccupied Molecular Orbital (HOMO), Highest Occupied Molecular Orbitals (LUMO), singlet (Es) and triplet (ET) energy) of more than 10 thousand fused tricyclics are calculated. Corresponding descriptors are also generated. We find that the Coulomb matrix is a poorer descriptor than high-level descriptors in a multilayer perceptron neural network. Correlations as high as 0.95 is obtained using a multilayer perceptron neural network with Mean Absolute Error as low as 0.08 eV. The descriptors that are important in tuning the energy levels are revealed using the Random Forest algorithm. Correlations of such descriptors are also plotted. We found that the higher the number of tertiary amines, the deeper are the HOMO and LUMO levels. The presence of NN in the aromatic rings can be used to tune the ES. However, there is no single dominant descriptor that can be correlated with the ET. A collection of descriptors is found to give a far better correlation with ET. This research demonstrated that machine learning and data analytics in guiding how certain chemical substructures correlate with the molecule energy states.

Matched MeSH terms: Machine Learning*
Transfer of knowledge, skills and confidence from a faculty development programme for health professions educators into practice

Chia CF, Nadarajah VD, Lim V, Kutzsche S

Med Teach, 2021 Jul;43(sup1):S46-S52.
PMID: 32552199 DOI: 10.1080/0142159X.2020.1776239

BACKGROUND: Faculty development programmes should incorporate the transfer of knowledge, skills, and confidence from the training to educational practice. However, there is a risk that transfer may fail due to inadequate integration of knowledge, skills, and confidence. The study evaluated transfer levels, guided by learned principles from a faculty development programme.
METHOD: The submitted self-reports on a pedagogical intervention of 92 out of 190 health professions educators who participated in a mandatory teaching and learning training programme, were analysed by a mixed-method approach guided by a structured conceptual framework.
RESULTS: Overall 93.4% reported the successful transfer of learning. Participants incorporated sustainable changed practice (level A, 57.6%), showed reflection on the impact of changed practice (level B, 21.7%), and performed effect analysis (level C, 14.1%). The rest planned application of learning (level D, 4.4%) and identified gaps in current practice or developed an idea for educational intervention but did not implement (level E, 2.2%).
CONCLUSION: The majority of participants transferred their learning. Faculty development programmes must ensure successful transfer of knowledge, skills, and confidence from the training to educational practice to ensure sustainable development of teaching and learning practices.

Matched MeSH terms: Learning*
Pembelajaran berasaskan projek: takrifan, teori dan perbandingannya dengan pembelajaran berasaskan masalah

Nitce Isa Medina Machmudi Isa, Mai Shihah Hj Abdullah

CREAM : Current Research in Malaysia (Penyelidikan Terkini di Malaysia), 2013;2(1):181-194.
MyJurnal

Selaras dengan ledakan pengetahuan berasaskan komputer dan teknologi pada abad ke-21, kaedah Pembelajaran Berasaskan Projek (PBP) atau Project Based Learning (PBL) telah diperkenalkan oleh Kementerian Pelajaran Malaysia (KPM) pada tahun 2006. Pada awalnya, implementasi kaedah PBP telah dimulakan di sekolah-sekolah bestari perdana. Ini merupakan tinjauan literatur yang membincangkan takrifan dan teori yang digunapakai dalam kaedah PBP. Selain itu, perbandingan kaedah PBP dengan kaedah Pembelajaran Berasaskan Masalah (PBM) atau Problem Based Learning (PBL) turut dibincangkan memandangkan kedua-dua kaedah ini menggunakan akronim yang sama dalam bahasa Inggeris. Berdasarkan tinjauan literatur ini, didapati bahawa kaedah PBP mempunyai kelebihan dan kelemahan yang tersendiri, maka terpulang kepada budi bicara guru yang mengajar untuk mengaplikasikan kaedah ini dalam pengajaran dan pembelajaran (P dan P) bersesuaian dengan kebolehan murid.

Matched MeSH terms: Learning; Problem-Based Learning
Fulltext Self-directed learning and construction of knowledge

Azer, Samy A.

Medical Health Reviews, 2008;2008(1):81-95.
MyJurnal

With the introduction of problem-based learning (PBL) in medical and health professionals’ undergraduate courses, self-directed learning (also known as self-regulated learning) becomes an integral component of the learning process. There may be slight variations in how educators and students perceive self-directed learnin .However, self-directed learning provides an opportunity for collaborative discussion of the new information collected and allows learners to construct new knowledge as they address their learning issues. Therefore, self-directed learning is not just about researching for new knowledge or finding answers for questions; self-directed learning is about developing competencies, skills and attitudes that foster the learning processes. Interestingly, not all learners will be able to adapt this approach of learning once they enroll in a PBL course. The process will develop gradually and require a number of actions from the learner, including: (i) Realising the need to change their learning style to suite the needs of the medical curriculum, (ii) constructing a plan that accommodates the new learning objectives, (iii) Practicing self-directed learning and sharing their experiences with peers, and (iv) Continuing evaluation of their self-directed learning approach and improving their learning style. Therefore, the aims of this manuscript are: (i) discuss the meaning of self-directed learning in the context of PBL, and review the research outcomes in this area, (ii) understand the different factors that may affect student’s self-directed learning strategies, and (iii) briefly explore the meaning of construction of knowledge and how it can enforce students’ self-directed learning, integration of knowledge and deeper understanding of topics learnt.

Matched MeSH terms: Learning; Problem-Based Learning
Insight into potential mechanisms of hypobaric hypoxia-induced learning and memory deficit - Lessons from rat studies

Qaid E, Zakaria R, Sulaiman SF, Yusof NM, Shafin N, Othman Z, et al.

Hum Exp Toxicol, 2017 Dec;36(12):1315-1325.
PMID: 28111974 DOI: 10.1177/0960327116689714

Impairment of memory is one of the most frequently reported symptoms during sudden hypoxia exposure in human. Cortical atrophy has been linked to the impaired memory function and is suggested to occur with chronic high-altitude exposure. However, the precise molecular mechanism(s) of hypoxia-induced memory impairment remains an enigma. In this work, we review hypoxia-induced learning and memory deficit in human and rat studies. Based on data from rat studies using different protocols of continuous hypoxia, we try to elicit potential mechanisms of hypobaric hypoxia-induced memory deficit.

Matched MeSH terms: Learning/physiology*
Thinking, learning and learning disabilities

Sinniah, Davendralingam

Malaysian Journal of Child Health, 1997;9(2):160-165.
MyJurnal

Thinking is something that we do all through our lives - an activity thcit possibly antedates our very birth itself Yet our children and we are not told about thinking or taught about the thinking process that dominates our lives, possibly, because of our own limited under-standing. Consequently, children are told to be logical and are discouraged from thinking differently, because it is the only type of think-ing we know and can understand. Methods of assessing their performance based on logical thinking underestimate their true potentials. The creative potentials of these children, 40% of who are right-brained need to be harnessed by approaches to learning that utilize methods of teaching and assessment, appropriate for their style of thinking. Another group of children, who need special attention, are those with learning disabilities that have been ignored, but can be corrected with appropriate programmes that provide a comprehensive approach to regular and special education.

Matched MeSH terms: Learning; Learning Disorders
Fulltext Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

Albadr MAA, Tiun S, Al-Dhief FT, Sammour MAM

PLoS One, 2018;13(4):e0194770.
PMID: 29672546 DOI: 10.1371/journal.pone.0194770

Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.

Matched MeSH terms: Machine Learning*
Fulltext A soft artificial muscle driven robot with reinforcement learning

Yang T, Xiao Y, Zhang Z, Liang Y, Li G, Zhang M, et al.

Sci Rep, 2018 09 28;8(1):14518.
PMID: 30266999 DOI: 10.1038/s41598-018-32757-9

Soft robots driven by stimuli-responsive materials have their own unique advantages over traditional rigid robots such as large actuation, light weight, good flexibility and biocompatibility. However, the large actuation of soft robots inherently co-exists with difficulty in control with high precision. This article presents a soft artificial muscle driven robot mimicking cuttlefish with a fully integrated on-board system including power supply and wireless communication system. Without any motors, the movements of the cuttlefish robot are solely actuated by dielectric elastomer which exhibits muscle-like properties including large deformation and high energy density. Reinforcement learning is used to optimize the control strategy of the cuttlefish robot instead of manual adjustment. From scratch, the swimming speed of the robot is enhanced by 91% with reinforcement learning, reaching to 21 mm/s (0.38 body length per second). The design principle behind the structure and the control of the robot can be potentially useful in guiding device designs for demanding applications such as flexible devices and soft robots.

Matched MeSH terms: Machine Learning*
Fulltext Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization

Al-Khaleefa AS, Ahmad MR, Isa AAM, Esa MRM, Aljeroudi Y, Jubair MA, et al.

Sensors (Basel), 2019 May 25;19(10).
PMID: 31130657 DOI: 10.3390/s19102397

Wi-Fi has shown enormous potential for indoor localization because of its wide utilization and availability. Enabling the use of Wi-Fi for indoor localization necessitates the construction of a fingerprint and the adoption of a learning algorithm. The goal is to enable the use of the fingerprint in training the classifiers for predicting locations. Existing models of machine learning Wi-Fi-based localization are brought from machine learning and modified to accommodate for practical aspects that occur in indoor localization. The performance of these models varies depending on their effectiveness in handling and/or considering specific characteristics and the nature of indoor localization behavior. One common behavior in the indoor navigation of people is its cyclic dynamic nature. To the best of our knowledge, no existing machine learning model for Wi-Fi indoor localization exploits cyclic dynamic behavior for improving localization prediction. This study modifies the widely popular online sequential extreme learning machine (OSELM) to exploit cyclic dynamic behavior for achieving improved localization results. Our new model is called knowledge preserving OSELM (KP-OSELM). Experimental results conducted on the two popular datasets TampereU and UJIndoorLoc conclude that KP-OSELM outperforms benchmark models in terms of accuracy and stability. The last achieved accuracy was 92.74% for TampereU and 72.99% for UJIndoorLoc.

Matched MeSH terms: Machine Learning; Learning

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