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Fulltext Considering phrasal verbs and collocations for ESL vocabulary teaching

Weng, Brandon Chai An

Borneo Akademika, 2020;4(4):1-8.
MyJurnal

Words have a habit of appearing in recurrent patterns. These recurring patterns may take the
form of phrasal verbs, collocations, and other multi-word expressions (MWEs). Since these
patterns constantly re-occur in both speech and writing, it would seem prudent to teach
vocabulary to ESL learners in the manner in which words actually present themselves: in
typical chunks. A common example of such a chunk would be “to deal with the problem”, in
which “deal with” and “problem” are collocates. This paper contains three sections. The first
examines what phrasal verbs and collocates are in the first place, and why they are particularly
important for learners. The second part is a review of recent studies that support the teaching
of MWEs and chunking pedagogy. This paper concludes by discussing the potential of
incorporating chunking pedagogy in one’s own ESL vocabulary teaching.

Matched MeSH terms: Learning
Protective effect of daidzein against streptozotocin-induced Alzheimer's disease via improving cognitive dysfunction and oxidative stress in rat model

Wei J, Yang F, Gong C, Shi X, Wang G

J Biochem Mol Toxicol, 2019 Jun;33(6):e22319.
PMID: 30897277 DOI: 10.1002/jbt.22319

Oxidative stress is performing an essential role in developing Alzheimer's disease (AD), and age-related disorder and other neurodegenerative diseases. In existing research, we have aimed at investigating the daidzein (4',7-dihydroxyisoflavone) effect (10 and 20 mg/kg of body weight), as a free radical scavenger and antioxidant in streptozotocin (STZ) infused AD in rat model. Daidzein treatment led to significant improvement in intracerebroventricular-streptozotocin (ICV-STZ)-induced memory and learning impairments that was evaluated by Morris water maze test and spontaneous locomotor activity. It significantly restored the alterations in malondialdehyde, catalase, superoxide dismutase, and reduced glutathione levels. In addition, histopathological observations in cerebral cortex and hippocampal areas confirmed the neuroprotective effect of daidzein. These outcomes provide experimental proof showing preventive effect of daidzein on memory, learning dysfunction and oxidative stress in case of ICV-STZ rats. In conclusion, daidzein offers a potential treatment module for various neurodegenerative disorders with regard to mental deficits like AD.

Matched MeSH terms: Learning
Injecting realism in surgical training-initial simulation experience with custom 3D models

Waran V, Narayanan V, Karuppiah R, Pancharatnam D, Chandran H, Raman R, et al.

J Surg Educ, 2014 Mar-Apr;71(2):193-7.
PMID: 24602709 DOI: 10.1016/j.jsurg.2013.08.010

The traditionally accepted form of training is direct supervision by an expert; however, modern trends in medicine have made this progressively more difficult to achieve. A 3-dimensional printer makes it possible to convert patients imaging data into accurate models, thus allowing the possibility to reproduce models with pathology. This enables a large number of trainees to be trained simultaneously using realistic models simulating actual neurosurgical procedures. The aim of this study was to assess the usefulness of these models in training surgeons to perform standard procedures that require complex techniques and equipment.

Matched MeSH terms: Learning Curve
Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index

Wang Z, Zhang F, Zhang X, Chan NW, Kung HT, Ariken M, et al.

Sci Total Environ, 2021 Feb 12;775:145807.
PMID: 33618298 DOI: 10.1016/j.scitotenv.2021.145807

Soil salinization is an extremely serious land degradation problem in arid and semi-arid regions that hinders the sustainable development of agriculture and food security. Information and research on soil salinity using remote sensing (RS) technology provide a quick and accurate assessment and solutions to address this problem. This study aims to compare the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction and exploration of the potential application of derivatives to RS prediction of salinized soils. It explores the ability of derivatives to be used in the Landsat-8 OLI and Sentinel-2A MSI multispectral data, and it was used as a data source as well as to address the adaptability of salinity prediction on a regional scale. The two-dimensional (2D) and three-dimensional (3D) optimal spectral indices are used to screen the bands that are most sensitive to soil salinity (0-10 cm), and RS data and topographic factors are combined with machine learning to construct a comprehensive soil salinity estimation model based on gray correlation analysis. The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. This study compared the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction in finding the potential application of derivatives to RS prediction of salinized soils, with the results providing some theoretical basis and technical guidance for salinized soil prediction and environmental management planning.

Matched MeSH terms: Machine Learning
Fulltext Design of Learning Environment for Undergraduate Comprehensive Literacy Education under Blended Learning Environment

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*
Fulltext Classification of community-acquired outbreaks for the global transmission of COVID-19: Machine learning and statistical model analysis

Wang WC, Lin TY, Chiu SY, Chen CN, Sarakarn P, Ibrahim M, et al.

J Formos Med Assoc, 2021 Jun;120 Suppl 1:S26-S37.
PMID: 34083090 DOI: 10.1016/j.jfma.2021.05.010

BACKGROUND: As Coronavirus disease 2019 (COVID-19) pandemic led to the unprecedent large-scale repeated surges of epidemics worldwide since the end of 2019, data-driven analysis to look into the duration and case load of each episode of outbreak worldwide has been motivated.
METHODS: Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt

Matched MeSH terms: Machine Learning
Fulltext Blended learning in physical education: A systematic review

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*
Change in level of classroom lighting at a special education school distortion of vision and comparison of lighting at different lighting conditions

Wan Nur ‘Amirah Ibrahim, Zainora Mohammed, Norliza Mohamad Fadzil, Sumithira Narayanasamy, Mohd ‘Izzuddin Hairol

Sains Malaysiana, 2018;47:1835-1842.

Illumination is one of the important physical aspects that influences comfortability during learning session particularly
among visually impaired students. The purpose of this study was to determine changes in illumination level in classrooms
during learning session at Sekolah Menengah Pendidikan Khas (SMPK), Setapak. The second objective was to compare
the illumination level in the classrooms under three different lighting conditions: daylight only, with additional artificial
light and with removal of obstructions to daylight. Illumination levels in 17 classrooms was measured at one hour interval,
between 8 am to 1 pm for the first stage and 19 classrooms under three different lighting conditions from 11 am to 12 noon
for the second stage, using ILM1335 (ISO-TECH, Taiwan) digital luxmeter. Illumination level increased significantly from
8 am to 11 am (One-Way Repeated Measures ANOVA: F(2.14, 34.26)=76.49, p<0 .001) and was maximum at 1 pm. The
illumination level was highest for the condition of daylight with additional artificial light (One-Way Repeated Measures
ANOVA: F(2,34)=110.51, p<0.001) compared to other conditions. Illumination levels for daylight without obstruction
was significantly higher than daylight only (pairwise comparison: p=0.001). Classroom illumination level was lowest
in the early morning. However, classroom illumination can be increased either by removing the obstructions to daylight
or with additional artificial lighting.

Matched MeSH terms: Learning
Fulltext Students’ learning styles and their academic performance in accounting course

Wan Mardyatul Miza Wan Tahir, Akma Hidayu Dol @ Abdul Wahid, Amariah Hanum Hussin, Ja’izah Abdul Jabar

Journal of Academia Universiti Teknologi MARA Negeri Sembilan, 2019;7(101):10-17.
MyJurnal

Learning accounting for non accounting major students is constantly considered challengin g. Therefore, the objective of the study is to identify the relationship between the learning style adopted by non accounting students in learning accounting course and the impact on their course performance. The Kolb’s learning style survey model that was re designed by Honey and Mumford in 1986 was adopted to recognise the learning style preferred by students. The students’ academic performance in accounting course was obtained from their scores in major assessment methods including assignment, test, quiz, an d final examination result, which represented their final grade. Further, this paper identified other factors affecting students’ academic performance. The result indicated that students who adopted the Pragmatist and Theorist learning styles were more exc ellent in their academic performance in accounting course, while those who adopted the Activist learning style were poorer in their academic result. Accordingly, accounting course does not only involve number, data, and calculation but requires fact fin din g and applying critical thinking, areas in which the Activist learning style lacks. Other factors found that educators who conducted the lecture were recognised as important contributors towards the students’ achievement in accounting course. Neverthele ss, students with a higher level of anxiety performed better academically as compared to those with low anxiety. In conclusion, to succeed in accounting course, students should not rely merely on one style in the learning process.

Matched MeSH terms: Learning
Fulltext Teaching listening skills in the 21st century

Wan Faizatul Azirah Ismayatim, Nur Dalila Mohamad Nazri, Ramiaida Darmi, Nursyuhada’ Ab Wahab, Nur Adibah Zamri, Haliza Harun, et al.

Jurnal Inovasi Malaysia, 2020;4(1):173-192.
MyJurnal

This paper presents an innovation of a revolutionized self-directed English learning module entitled My Electronic Visual and Audio (MyEVO), which is designed and developed to assist language learners to conveniently acquire the required listening skills through the combination of current and state-of-the-art technology - Augmented Reality (AR) and mobile applications. Using Video Media method introduced by Gruba (1997, 2004), all listening practices in this module are based on video recording. Feedbacks gained from the users of the module indicate that learners are very excited and happy to use technology assisted module in acquiring listening skills compared to the traditional module. Educators also believe that this module cater the needs of the 21st century learners and is suitable to be used inside the classroom or as a self-directed learning module. Another key feature of this smart module highlighted by the educators is the ability of the mobile application that allows learners to engage with the e-global community known as ‘MyEVO community, where all users can share their answers and exchange opinions regarding the given questions. In addition, listening activities that were designed in this module also cover the Higher Order Thinking Skills (HOTS) needed in learning. Educators also agreed that this interactive feature does not only encourage the learners to be active in their learning but it also helps to reduce their anxiety, learning process becomes more interesting and helps to aid their understanding of the topics covered.

Matched MeSH terms: Learning
Fulltext Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Voon W, Hum YC, Tee YK, Yap WS, Salim MIM, Tan TS, et al.

Sci Rep, 2022 Nov 10;12(1):19200.
PMID: 36357456 DOI: 10.1038/s41598-022-21848-3

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

Matched MeSH terms: Machine Learning
Learning and memory-enhancing effect of Bacopa monniera in neonatal rats

Vollala VR, Upadhya S, Nayak S

Bratisl Lek Listy, 2011;112(12):663-9.
PMID: 22372329

The aim of this study was to evaluate the learning and memory-enhancing effect of Bacopa monniera in neonatal rats.

Matched MeSH terms: Avoidance Learning/drug effects*; Maze Learning/drug effects*
Fulltext Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction

Vijayasarveswari V, Andrew AM, Jusoh M, Sabapathy T, Raof RAA, Yasin MNM, et al.

PLoS One, 2020;15(8):e0229367.
PMID: 32790672 DOI: 10.1371/journal.pone.0229367

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

Matched MeSH terms: Machine Learning
Fulltext Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients

Vepa A, Saleem A, Rakhshan K, Daneshkhah A, Sedighi T, Shohaimi S, et al.

Int J Environ Res Public Health, 2021 06 09;18(12).
PMID: 34207560 DOI: 10.3390/ijerph18126228

BACKGROUND: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making.
METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks.
RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.

Matched MeSH terms: Machine Learning
Student-friendly methods of formative assessment in paediatrics

Venkataramani P, Sadanandan T, Savanna RS, Sugathan S

Med Educ, 2019 05;53(5):499-500.
PMID: 30891812 DOI: 10.1111/medu.13860
Matched MeSH terms: Learning*
Assessing clinical reasoning skills of final year medical students using the script concordance test

Velayudhan Menon, Rifdy Mohideen

International e-Journal of Science, Medicine & Education, 2016;10(1):36-40.
MyJurnal

Background: Clinical reasoning is the name given to
the cognitive processes by which doctors evaluate and
analyse information from patients. It is a skill developed
by experiential learning and is difficult to assess
objectively. The script concordance test, an assessment
tool introduced into the health sciences about 15 years
ago, is a way of assessing clinical reasoning ability in
an objective manner and allows comparisons of the
decisions made by medical students and experts in
situations of uncertainty.

Methods: Twenty-six final year medical students from
the International Medical University, Kuala Lumpur,
were tested on their decision making skills regarding a
young febrile patient. The students evaluated different
pieces of information in five different scenarios and
made decisions on a five-point Likert scale in the
standard format of the script concordance test. Their
decisions were compared to the decisions of a panel of
experienced clinicians in Internal Medicine.

Results: The script concordance test scores for the
different scenarios were calculated with higher scores
being indicative of greater concordance between the
reasoning of students and doctors. The students showed
poor concordance with doctors in evaluating clinical
information. Overall, only 20 percent of the choices
made by students were the same as the choices made by
the majority of doctors.

Conclusion: Medical students vary in their ability to
interpret the significance of clinical information. Using
the script concordance test, this preliminary study looked
at the ability of final year medical students to interpret
information about a patient with a febrile illness. The
results showed poor concordance between students and
doctors in the way they interpreted clinical information.
The script concordance test has the potential to be a
tool for teaching and assessing clinical reasoning.

Matched MeSH terms: Problem-Based Learning
Fulltext Parkinson's Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks

Veeraragavan S, Gopalai AA, Gouwanda D, Ahmad SA

Front Physiol, 2020;11:587057.
PMID: 33240106 DOI: 10.3389/fphys.2020.587057

Gait analysis plays a key role in the diagnosis of Parkinson's Disease (PD), as patients generally exhibit abnormal gait patterns compared to healthy controls. Current diagnosis and severity assessment procedures entail manual visual examinations of motor tasks, speech, and handwriting, among numerous other tests, which can vary between clinicians based on their expertise and visual observation of gait tasks. Automating gait differentiation procedure can serve as a useful tool in early diagnosis and severity assessment of PD and limits the data collection to solely walking gait. In this research, a holistic, non-intrusive method is proposed to diagnose and assess PD severity in its early and moderate stages by using only Vertical Ground Reaction Force (VGRF). From the VGRF data, gait features are extracted and selected to use as training features for the Artificial Neural Network (ANN) model to diagnose PD using cross validation. If the diagnosis is positive, another ANN model will predict their Hoehn and Yahr (H&Y) score to assess their PD severity using the same VGRF data. PD Diagnosis is achieved with a high accuracy of 97.4% using simple network architecture. Additionally, the results indicate a better performance compared to other complex machine learning models that have been researched previously. Severity Assessment is also performed on the H&Y scale with 87.1% accuracy. The results of this study show that it is plausible to use only VGRF data in diagnosing and assessing early stage Parkinson's Disease, helping patients manage the symptoms earlier and giving them a better quality of life.

Matched MeSH terms: Machine Learning
Fulltext Physiotherapy students’ perception of their educational environment: a study to identify the areas of concern for remedial measures at two schools of physiotherapy in Malaysia

Veasuvalingam, Bhavani, Hafiza Arzuman

Education in Medicine Journal, 2014;6(3):30-39.
MyJurnal

Introduction: In order to produce competent physiotherapy graduates with the generic attributes much sought after by the health care providers in the country, the higher education institution needs to ensure the educational environment of the school is positive. Students' positive perception of their educational environment would facilitate their learning experience to be more meaningful and relevant. Objective: The aim of this study was to measure physiotherapy students' perception of their educational environment at the School of Physiotherapy AIMST University and Kolej Sains Kesihatan Bersekutu Sungai Buloh and to identify the areas of concern for remedial measures. Method: This research was a cross sectional study consisting of two phases using both quantitative followed by qualitative methods. The DREEM inventory consisting of 50 items under 5 domains was circulated to all the students (N=158) from both schools (AIMST and KSKB). The item mean scored below 2.00 were considered as problem areas and it was explored further through focus group discussion (N=12) as a qualitative study. Result: The overall mean score on the 50 items was 132.84 (SD 19.22) out of 200. Students' Perception of Learning (SPOL) scored the highest 32.34 (SD 4.17) followed by students' perception of Atmosphere (SPOA) 30.63 (SD 4.84), Students Perception of Teachers (SPOT) scored 30.52 (SD 3.98),Students Academic Self Perception (SASP) scored 22.03 (SD 3.20) and the last domain Students' Social Self Perception (SSSP) scored the least 17.32 (SD 19.22).All the domains scored toward more positive side of the educational environment. Four items scored less than 2.00 and these items were explored further with focus group discussion. Students from both schools had similarities as well as differences in their views over the concerned areas. Conclusion: This study revealed important information regarding the low scored items. Overall the students from both schools perceived their schools positively. Implementing the remedial measures for the problem areas would further enhance the respective educational environment and thus provide a conducive place for physiotherapy students to excel in their academic endeavour.

Matched MeSH terms: Problem-Based Learning
Fulltext Academics' continuance intention to use learning technologies during COVID-19 and beyond

Vasuthevan K, Vaithilingam S, Ng JWJ

PLoS One, 2024;19(1):e0295746.
PMID: 38166113 DOI: 10.1371/journal.pone.0295746

The COVID-19 pandemic has revolutionized the teaching pedagogy in higher education as universities are forecasted to increase investments in learning technology infrastructure to transition away from traditional teaching methods. Therefore, it is crucial to investigate whether academics intend to continually integrate learning technologies as part of a permanent pedagogical change beyond the COVID-19 pandemic. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT), and Expectation Confirmation Model (ECM), this study examines the salient determinants influencing the continuance intention of academics to use learning technologies in their teaching pedagogy during and after COVID-19. Primary data collected from a private university was analyzed using the partial least squares structural equation modelling technique (PLS-SEM). The findings revealed two sequential mediating relationships which serve as the mechanism linking the relationship between facilitating conditions and their continuance intention to use learning technologies during and beyond the COVID-19 pandemic.

Matched MeSH terms: Learning
Fulltext Choral speaking: perceptions and challenges in an ESL context

Vasuthavan, Evelyn Sharminnie, Vijayarajoo, Angeline Ranjethamoney, Kumaran, Arutchelvi, Nur Hidayah Mohd Razali

Journal of Academia Universiti Teknologi MARA Negeri Sembilan, 2019;7(101):18-24.
MyJurnal

Choral Speaking is known for its’ numerous benefits in the enhancement of the English Language in the ESL context. However, it has been found that both – learners and teachers alike, perceive Choral Speaking to be arduous. Hence, when performances and competitions are organised, there is a dual resistance and anxiety from the learners and teachers. This study looked at perceptions and challenges on Choral Speaking, of learners from a public university, and that of teachers from secondary schools in Malaysia. Methodology comprised qualitative and quantitative methods, where questionnaires and interviews were administered to the participants. Hence, data comprised responses from these two instruments. The findings showed that though the majority of the learners and teachers perceive Choral Speaking as beneficial, the challenges identified, caused reluctance in participation. This paper provides recommendations to address these issues.

Matched MeSH terms: Learning

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