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Fulltext Perceptions of Australian and Malaysian Educators in an Undergraduate Pharmacy Program on Case-based Learning

Jacob SA, Dhing OH, Malone D

Am J Pharm Educ, 2019 Apr;83(3):6597.
PMID: 31065163 DOI: 10.5688/ajpe6597

Objective. To determine the perceptions of lecturers toward case-based learning (CBL) and to elicit their feedback and opinions regarding the design of CBL sessions within the pharmacy curricula. Methods. One-on-one interviews were conducted with 10 academic staff members involved in teaching an undergraduate Bachelor of Pharmacy (BPharm) program. All sessions were audio-recorded and field notes were compiled. The recordings were transcribed, and thematic analysis of responses was performed. Results. Four key themes were identified: perceived benefits of CBL, challenges in implementing CBL within the curricula, characteristics of effective and engaging CBL, and relevance and implementation of CBL within the curriculum. Some of the specific benefits of CBL identified by participants included the applicability of knowledge learned to students' future role as pharmacists. Participants also identified challenges such as the design of CBL cases and course time constraints. Respondents also emphasized the need for more training for facilitators in how to design cases and facilitate sessions. Conclusion. While participants identified numerous benefits of CBL, they also identified challenges to implementing this learning method within the pharmacy school curriculum. Paying careful attention to selecting facilitators and providing appropriate facilitator training, in terms of facilitation and case design, is paramount in effectively implementing CBL sessions.

Matched MeSH terms: Problem-Based Learning/methods*
Fulltext Using machine learning to determine the nationalities of the fastest 100-mile ultra-marathoners and identify top racing events

Knechtle B, Weiss K, Valero D, Villiger E, Nikolaidis PT, Andrade MS, et al.

PLoS One, 2024;19(8):e0303960.
PMID: 39172797 DOI: 10.1371/journal.pone.0303960

The present study intended to determine the nationality of the fastest 100-mile ultra-marathoners and the country/events where the fastest 100-mile races are held. A machine learning model based on the XG Boost algorithm was built to predict the running speed from the athlete's age (Age group), gender (Gender), country of origin (Athlete country) and where the race occurred (Event country). Model explainability tools were then used to investigate how each independent variable influenced the predicted running speed. A total of 172,110 race records from 65,392 unique runners from 68 different countries participating in races held in 44 different countries were used for analyses. The model rates Event country (0.53) as the most important predictor (based on data entropy reduction), followed by Athlete country (0.21), Age group (0.14), and Gender (0.13). In terms of participation, the United States leads by far, followed by Great Britain, Canada, South Africa, and Japan, in both athlete and event counts. The fastest 100-mile races are held in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. The fastest athletes come mostly from Eastern European countries (Lithuania, Latvia, Ukraine, Finland, Russia, Hungary, Slovakia) and also Israel. In contrast, the slowest athletes come from Asian countries like China, Thailand, Vietnam, Indonesia, Malaysia, and Brunei. The difference among male and female predictions is relatively small at about 0.25 km/h. The fastest age group is 25-29 years, but the average speeds of groups 20-24 and 30-34 years are close. Participation, however, peaks for the age group 40-44 years. The model predicts the event location (country of event) as the most important predictor for a fast 100-mile race time. The fastest race courses were occurred in Romania, Israel, Switzerland, Finland, Russia, the Netherlands, France, Denmark, Czechia, and Taiwan. Athletes and coaches can use these findings for their race preparation to find the most appropriate racecourse for a fast 100-mile race time.

Matched MeSH terms: Machine Learning*
Fulltext Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

Pai YS, Yap HJ, Md Dawal SZ, Ramesh S, Phoon SY

Sci Rep, 2016 06 07;6:27380.
PMID: 27271840 DOI: 10.1038/srep27380

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

Matched MeSH terms: Learning
Fulltext Perception of content and non-content expert facilitators of PBL according to students' performance levels

Ismail NA, Alias E, Arifin KT, Damanhuri MH, Karim NA, Aan GJ

Pak J Med Sci, 2015 Nov-Dec;31(6):1537-41.
PMID: 26870131 DOI: 10.12669/pjms.316.8691

Problem-based learning (PBL) is a student-centred learning system that involves multidisciplinary fields focused on problem solving. Facilitators of PBL are not necessarily content experts but little is known on how this concept has affected the outcomes of PBL sessions in learning Medical Biochemistry. We aimed to evaluate the impact of having the content expert as a facilitator in conducting PBL.

Matched MeSH terms: Problem-Based Learning
Fulltext Alphabetic knowledge and phonological awareness: a comparison between Malaysian preschool children from public and private kindergartens

Julia Lee, Ai Cheng, Wang, Joanna Joo Ying

Journal of Cognitive Sciences & Human Development, 2020;6(2):1-15.
MyJurnal

Alphabetic knowledge and phonological awareness are essential skills in learning to read. This research examined the level of acquisition on alphabetic knowledge and phonological awareness among 60 preschoolers from private and public preschools in Kuching, Sarawak. The mean age of the children was 5.58. The children were administered letter name and sound knowledge, and letter naming fluency tests to examine their alphabetic knowledge; Comprehensive Test of Phonological Processing and Yopp-Singer Phoneme Segmentation Test to examine their phonological awareness. Higher achievement in alphabetic knowledge and phonological awareness was found among preschoolers from private preschools compared to those from public preschools. This study discusses the implications for practice and ways teachers could explicitly foster alphabetic knowledge and phonological awareness skills in classroom.

Matched MeSH terms: Learning
Fulltext Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Asli MF, Hamzah M, Ibrahim AAA, Ayub E

Heliyon, 2020 Dec;6(12):e05733.
PMID: 33426320 DOI: 10.1016/j.heliyon.2020.e05733

Malaysia and many other developing countries progressively adopting massively open online course (MOOC) in their national higher education approach. We have observed an increasing need for facilitating MOOC monitoring that is associated with the rising adoption of MOOCs. Our observation suggests that recent adoption cases led analyst and instructors to focus on monitoring enrolment and learning activities. Visual analytics in MOOC support education analysts in analyzing MOOC data via interactive visualization. Existing literature on MOOC visualization focuses on enabling visual analysis on MOOC data from forum and course material. We found limited studies that investigate and characterize domain problems or design requirements of visual analytics for MOOC. This paper aims to present the empirical problem characterization and abstraction for visual analytics in MOOC learner's support monitoring. Detailed characterization and abstraction of the domain problem help visualization designer to derive design requirements in generating appropriate visualization solution. We examined the literature and conducted a case study to elicit a problem abstraction based on data, users, and tasks. We interviewed five Malaysian MOOC experts from three higher education institutes using semi-structured questions. Our case study reveals the priority of enabling MOOC analysis on learner's progression and course completion. There is an association between design and analysis priority with the pedagogical type of implemented MOOC and users. The characterized domain problems and requirements offer a design foundation for visual analytics in MOOC monitoring analysis.

Matched MeSH terms: Problem-Based Learning
Fulltext Leaf venation networks of Bornean trees: images and hand-traced segmentations

Blonder B, Both S, Jodra M, Majalap N, Burslem D, Teh YA, et al.

Ecology, 2019 Nov;100(11):e02844.
PMID: 31336398 DOI: 10.1002/ecy.2844

The data set contains images of leaf venation networks obtained from tree species in Malaysian Borneo. The data set contains 726 leaves from 295 species comprising 50 families, sampled from eight forest plots in Sabah. Image extents are approximately 1 × 1 cm, or 50 megapixels. All images contain a region of interest in which all veins have been hand traced. The complete data set includes over 30 billion pixels, of which more than 600 million have been validated by hand tracing. These images are suitable for morphological characterization of these species, as well as for training of machine-learning algorithms that segment biological networks from images. Data are made available under the Open Data Commons Attribution License. You are free to copy, distribute, and use the database; to produce works from the database; and to modify, transform, and build upon the database. You must attribute any public use of the database, or works produced from the database, in the manner specified in the license. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database.

Matched MeSH terms: Machine Learning
Fulltext Effects of psychological ownership on teachers' beliefs about a cloud-based virtual learning environment

Yim JS, Moses P, Azalea A

Res Pract Technol Enhanc Learn, 2018;13(1):13.
PMID: 30595741 DOI: 10.1186/s41039-018-0081-0

Perceived usefulness and perceived ease of use constitute important belief factors when technology adoption decisions are made within a non-mandatory setting. This paper investigated the role played by psychological ownership in shaping teachers' beliefs about using a cloud-based virtual learning environment (VLE). Psychological ownership is increasingly becoming a relevant phenomenon in technology adoption research, where people can feel psychologically attached to a particular technology. The study proposed that such phenomenon can also occur when using a VLE, and a hypothesised model with six constructs was tested with 629 Malaysian teachers from 21 schools. Results from structural equation modelling-partial least squares analysis found teachers' experiences with the VLE significantly influenced psychological ownership, which in turn significantly predicted perceived usefulness and perceived ease of use of the VLE. Overall, the model possesses predictive relevance for the outcome predictors as indicated by Stone-Geisser's Q2, and accounted for 61.6% of variance in perceived usefulness and 62.0% of variance in perceived ease of use. This study provides insights into the motivation behind teachers' beliefs which are shaped by their experiences with the VLE. Implications for theory and practice were discussed based on the insights of the study.

Matched MeSH terms: Learning
Fulltext Teaching strategy: strength and limitation of teaching strategies for nursing education via lecture and simulation

Mardiana Mansor, Ayu Sulaini Jusoh, Lim, Chin Choon

Malaysian Journal of Medical Research, 2018;1(4):1-5.
MyJurnal

The purpose of this article is to discuss the strengths and limitations of two teaching strategies currently utilized
in Diploma in Nursing, in Malaysia. The diploma was started in 1994 with a 3 years duration of study. It also
requires certificates of qualification from the Malaysian Quality Agency (MQA) and the Malaysian Board of
Nursing.
Teaching strategies of individual teachers differ according to their teaching styles and generalized lesson plans,
which include structures, instructional objectives, outlines of teaching and learning tactics, and other
accessories needed to implement the strategies. A strategy does not necessarily follow a single track all the
time, but changes according to the demands of the situations such as the age, level, needs, interests and abilities
of the students. Thus, strategy is a method that is more comprehensive. It is directional in nature and refers to
the goal oriented activities of the teacher. Hence, it resembles science rather than arts.
Lecture and simulation methods are the best teaching strategies for nursing students in Malaysia. The lecture
method allows clarification on difficult concepts, organizes thinking, and promotes problem solving attitudes,
whereas simulation provides students with the opportunity for proper social, emotional and intellectual
development. Moreover, students are highly motivated by educational simulation, for they enjoy the learning
process while participating in it.

Matched MeSH terms: Learning
Fulltext Massive open online courses (MOOCs) for continuing medical education - why and how?

Setia S, Tay JC, Chia YC, Subramaniam K

Adv Med Educ Pract, 2019;10:805-812.
PMID: 31572042 DOI: 10.2147/AMEP.S219104

Continuing medical education (CME) is meant to not only improve clinicians' knowledge and skills but also lead to better patient care processes and outcomes. The delivery of CME should be able to encourage the health providers to accept new evidence-based practices, and discard or discontinue less effective care. However, continuing use of expensive yet least effective and inappropriate tools and techniques predominates for CME delivery. Hence, the evidence shows a disconnect between evidence-based recommendations and real-world practice - borne out by less than optimal patient outcomes or treatment targets not being met especially in low- to middle-income countries. There is an ethical and professional obligation on CME-providers and decision-makers to safeguard that CME interventions are appraised not only for their quality and effectiveness but also for cost-effectiveness. The process of learning needs to be engaging, convenient, user-friendly and of minimal cost, especially where it is most needed. Today's technology permits these characteristics to be integrated, along with further enhancement of the engagement process. We review the literature on the mechanics of CME learning that utilizes today's technology tools and propose a framework for more engaging, efficient and cost-effective approach that implements massive open online courses for CME, adapted for the twenty-first century.

Matched MeSH terms: Learning
Fulltext Smart Costing Kit: game based learning for cost and management accounting

Rafizan Abdul Razak, Eley Suzana Kasim, Dalila Daud

ESTEEM Academic Journal, 2019;15(2):1-10.
MyJurnal

Cost and management accounting courses are incorporated in the accounting syllabus for both accounting and non-accounting students. One of the challenges in teaching cost and management accounting to the nonaccounting students is the general fear of accounting subject. Notwithstanding, most of the students still viewed accounting as an interesting subject. Hence, the learning problem faced by students needs to be addressed in a fun yet beneficial way. As such, the objective of this study is to assess the effectiveness of using the “Smart Costing Game” as a learning method to overcome this problem. The Smart Costing Game Kit was developed as a learning tool to enable students to correctly classify costs according to certain criteria and subsequently used the cost figures to compute total costs, profit and selling price. Four business settings are chosen consisting of bakery, restaurant, laundry and clinic. At the end of the game activities, the students are required to complete an online survey. Results from the survey demonstrated that the majority of the students agreed that the games were found to be more effective, motivating and engaging than traditional teaching. This implies that students have strong
preferences in the use of educational games that added value to the costing subject. These results support the inclusion of Smart Costing Game as a successful learning strategy in cost and management accounting courses for the non-accounting students.

Matched MeSH terms: Learning
Fulltext Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks

Kheirollahpour MM, Danaee MM, Merican AFAF, Shariff AAAA

ScientificWorldJournal, 2020;2020:4194293.
PMID: 32508538 DOI: 10.1155/2020/4194293

The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.

Matched MeSH terms: Machine Learning
Fulltext Case sharing - an approach for improving clinical experience to cases of advanced complexity among undergraduate dental students

Ramachandra SS, Gupta VV, Muttalib KA

J Oral Biol Craniofac Res, 2020 11 10;11(1):1-4.
PMID: 33344152 DOI: 10.1016/j.jobcr.2020.11.001

Introduction/Problem: Clinical experience in cases of advanced complexity/rare cases is limited among undergraduate dental students. This commentary narrates a module termed "case sharing", wherein a small group of undergraduate dental students treat/assist, document and present advanced or rare cases to their entire cohort in eight weeks.
Approach: Undergraduate students perform procedures of straightforward and moderate complexity, and later assisted the clinical specialists during procedures of advanced complexity. students document these cases with clinical photographs and case notes to make presentations that were uploaded in the faculty's online management system to be reviewed by the entire cohort. student groups presented their cases with their entire cohort. an independent assessor assessed the groups for their organization of the case, information collected on the topic, critical analysis of the case, defending the diagnosis and treatment plan, teamwork and presentation skills.
Evaluation: Students reported improvement in the depth of knowledge on particular diseases/procedures, a better understanding of holistic management of advanced cases, improved rapport, team spirit and communication among group members. they also reported difficulties in recruiting and completing the procedures within eight weeks.
Discussion: Apart from improving the clinical experience of undergraduate students, the module provides an opportunity for the development of teamwork, communication skills, and ethical presentations among students, which are invaluable to the faculty to meet its programme learning outcomes. case sharing provides a platform for holistic learning and serves as an alternative learning method aside from didactic lectures and routine clinical supervision.

Matched MeSH terms: Learning
Fulltext Development of and First Experiences with a Framework (EASI) for Options and Implementation Opportunities for Online Clinical and Communication Skills Learning

Nadarajah VD, Sow CF, Syed Aznal SS, Montagu A, Boursicot K, Er HM

J Med Educ Curric Dev, 2020 11 19;7:2382120520970894.
PMID: 33283046 DOI: 10.1177/2382120520970894

A preparatory framework called EASI (Evaluate, Align, Student-centred, Implement and Improve) was developed with the aim of creating awareness about interim options and implementation opportunities for online Clinical and Communication Skills (CCS) learning. The framework, when applied requires faculty to evaluate current resources, align sessions to learning outcomes with student-centred approaches and to continuously improve based on implementation experiences. Using the framework, we were able to generate various types of online CCS learning sessions for implementation in a short period of time due to the recent Covid-19 pandemic. Importantly we learnt a few lessons post-implementation from both students and faculty perspective that will be used for planning and delivery of future sessions. In summary, the framework was useful for creating or redesigning CCS sessions which were disrupted during the pandemic, however post-implementation experience suggests the framework can also be used for future solutions in online CCS learning as healthcare systems and delivery are increasingly decentralised and widely distributed.

Matched MeSH terms: Learning
Fulltext Improved Reconstruction of MR Scanned Images by Using a Dictionary Learning Scheme

Ikram S, Shah JA, Zubair S, Qureshi IM, Bilal M

Sensors (Basel), 2019 Apr 23;19(8).
PMID: 31018597 DOI: 10.3390/s19081918

The application of compressed sensing (CS) to biomedical imaging is sensational since it permits a rationally accurate reconstruction of images by exploiting the image sparsity. The quality of CS reconstruction methods largely depends on the use of various sparsifying transforms, such as wavelets, curvelets or total variation (TV), to recover MR images. As per recently developed mathematical concepts of CS, the biomedical images with sparse representation can be recovered from randomly undersampled data, provided that an appropriate nonlinear recovery method is used. Due to high under-sampling, the reconstructed images have noise like artifacts because of aliasing. Reconstruction of images from CS involves two steps, one for dictionary learning and the other for sparse coding. In this novel framework, we choose Simultaneous code word optimization (SimCO) patch-based dictionary learning that updates the atoms simultaneously, whereas Focal underdetermined system solver (FOCUSS) is used for sparse representation because of a soft constraint on sparsity of an image. Combining SimCO and FOCUSS, we propose a new scheme called SiFo. Our proposed alternating reconstruction scheme learns the dictionary, uses it to eliminate aliasing and noise in one stage, and afterwards restores and ﬁlls in the k-space data in the second stage. Experiments were performed using different sampling schemes with noisy and noiseless cases of both phantom and real brain images. Based on various performance parameters, it has been shown that our designed technique outperforms the conventional techniques, like K-SVD with OMP, used in dictionary learning based MRI (DLMRI) reconstruction.

Matched MeSH terms: Learning
Fulltext Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network

Naqvi SF, Ali SSA, Yahya N, Yasin MA, Hafeez Y, Subhani AR, et al.

Sensors (Basel), 2020 Aug 07;20(16).
PMID: 32784531 DOI: 10.3390/s20164400

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.

Matched MeSH terms: Machine Learning
Fulltext The impact of using historical text document resources on the understanding of historical substantive concepts

M. Kaviza

Malaysian Journal of Social Sciences and Humanities, 2019;4(7):208-217.
MyJurnal

This study aims at investigating the effect of using historical text document resources on the historical substantive concepts understanding among Form Four students. A learning activities module was developed based on using historical text document resources as reference for intervention in this study. Pre-Experimental Design: One Group PreTest-PostTest was used in this study. The impact of using historical teks document resources on the historical substantive concepts understanding were measured in the pre-test, post-test and delayed post-test. A sample of respondents comprising 55 students from existing classes was recruited in this study using cluster sampling techniques. The historical substantive concept understanding test was used in this study. Data were analyzed by descriptive and inference statistics using Repeated-Measures One Way ANOVA test. The findings showed that the use of historical text document resources has an impact on the historical substantive concepts understandingand retaintion. The implication of this study is to provide the content and methods for implementation of history learning by using a set of collections of historical text document resources which relevant to a historical topic.

Matched MeSH terms: Problem-Based Learning
Fulltext A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Wu M, Lu Y, Yang W, Wong SY

Front Comput Neurosci, 2020;14:564015.
PMID: 33469423 DOI: 10.3389/fncom.2020.564015

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

Matched MeSH terms: Machine Learning
Fulltext Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh

Yaseen ZM, Ali M, Sharafati A, Al-Ansari N, Shahid S

Sci Rep, 2021 Feb 09;11(1):3435.
PMID: 33564055 DOI: 10.1038/s41598-021-82977-9

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.

Matched MeSH terms: Machine Learning
Fulltext PRELIMINARY STUDY ON E-HAILING DRIVERS NEEDS IN ENGLISH LANGUAGE LEARNING FOR THE PURPOSE OF SUCCESSFUL COMMUNICATION IN TOURISM INDUSTRY

Shahazwan Mat Yusoff, Anwar Farhan Mohamad Marzaini, Siti Maftuhah Damio

Journal of Academia Universiti Teknologi MARA Negeri Sembilan, 2021;9(1):14-20.
MyJurnal

E-hailing apps Grab and Uber have become household names, particularly among urbanites over these five years. Overall the consumer response to e-hailing services in Malaysia has been positive, with The Land Public Transport Commission (SPAD) reporting that 80% of consumers prefer e-hailing over taxis. As such, many believe the availability of e-hailing services will help to boost demand, and raise property prices and rentals and help the tourism sector in locations where they are available. As the demand grows, and tourists around the globe keep rising, the means of communication plays a vital role. Hence, this article explores the Grab drivers’ needs in English language learning for the purpose of successful communication in working environment. The needs are categorised into three elements: needs of English language at workplace, problems in English language usage, and preferences in learning English. A case study was carried out among 50 Grab drivers in Kuala Lumpur. The analysis of responses to the needs in English language learning among Grab drivers is hoped to fashion English language course or the syllabus to the e- hailing drivers.

Matched MeSH terms: Learning

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