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Fulltext Challenges of teachers when teaching sentence-based mathematics problem-solving skills

Ling ANB, Mahmud MS

Front Psychol, 2022;13:1074202.
PMID: 36817370 DOI: 10.3389/fpsyg.2022.1074202

Sentence-based mathematics problem-solving skills are essential as the skills can improve the ability to deal with various mathematical problems in daily life, increase the imagination, develop creativity, and develop an individual's comprehension skills. However, mastery of these skills among students is still unsatisfactory because students often find it difficult to understand mathematical problems in verse, are weak at planning the correct solution strategy, and often make mistakes in their calculations. This study was conducted to identify the challenges that mathematics teachers face when teaching sentence-based mathematics problem-solving skills and the approaches used to address these challenges. This study was conducted qualitatively in the form of a case study. The data were collected through observations and interviews with two respondents who teach mathematics to year four students in a Chinese national primary school in Kuala Lumpur. This study shows that the teachers have faced three challenges, specifically low mastery skills among the students, insufficient teaching time, and a lack of ICT infrastructure. The teachers addressed these challenges with creativity and enthusiasm to diversify the teaching approaches to face the challenges and develop interest and skills as part of solving sentence-based mathematics problems among year four students. These findings allow mathematics teachers to understand the challenges faced while teaching sentence-based mathematics problem solving in depth as part of delivering quality education for every student. Nevertheless, further studies involving many respondents are needed to understand the problems and challenges of different situations and approaches that can be used when teaching sentence-based mathematics problem-solving skills.
Fulltext Influence of game-based learning in mathematics education on the students' cognitive and affective domain: A systematic review

Hui HB, Mahmud MS

Front Psychol, 2023;14:1105806.
PMID: 37057144 DOI: 10.3389/fpsyg.2023.1105806

INTRODUCTION: Game-based learning (GBL) is one of the modern trends in education in the 21st century. Numerous research studies have been carried out to investigate the influence of teaching on the students' academic attainment. It is crucial to integrate the cognitive and affective domains into teaching and learning strategies. This study aims to review journal articles from 2018 to 2022 concerning the influence of GBL in mathematics T&L on the students' cognitive and affective domains.
METHODS: A research methodology based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) was used for the survey on the basis of the Scopus and Web of Science (WOS) databases wherein 773 articles relating to game-based learning (GBL) in mathematics were discovered. Based on the study topic, study design, study technique, and analysis, only 28 open-access articles were chosen for further evaluation. Two types of cognitive domain and five types of affective domain were identified as related to the implications of GBL on the students' T&L of mathematics.
RESULTS: The study results show that GBL has positively impacted students when they are learning mathematics. It is comprised of two types of cognitive domain (knowledge and mathematical skills) and five types of affective domain (achievement, attitude, motivation, interest, and engagement). The findings of this study are anticipated to encourage educators in the classrooms more effectively.
DISCUSSION: GBL in education is now one of the major learning trends of the 21st century. Since 2019, the number of studies relating to game-based learning has increased. There is an influence on the cognitive and affective domains due to T&L Mathematics utilizing a game-based learning (GBL) approach.
Effect of visible light on catalytic hydrolysis of p-nitrophenyl palmitate by the Pseudomonas cepacia lipase immobilized on sol-gel support

Ganasen P, Khan MR, Kalam MA, Mahmud MS

Bioprocess Biosyst Eng, 2014 Nov;37(11):2353-9.
PMID: 24879090 DOI: 10.1007/s00449-014-1213-6

This paper demonstrates Pseudomonas cepacia lipase catalyzed hydrolysis of p-nitrophenyl palmitate under irradiation of light with wavelengths of 250-750 nm. The reaction follows Michaelis-Menten Kinetics and the light irradiation increases the overall rate of hydrolysis. Using Lineweaver-Burk plot K M and V max values for the reaction in presence of light are found to be 39.07 and 66.67 mM/min/g, respectively; while for the same reaction under dark condition, the values are 7.08 and 10.21 mM/min/g. The linear form of enzyme dependent rate of reaction confirms that no mass-transfer limitations are present and the reaction is a kinetically controlled enzymatic reaction.
Fulltext Unsupervised bivariate data clustering for damage assessment of carbon fiber composite laminates

May Z, Alam MK, Mahmud MS, Rahman NAA

PLoS One, 2020;15(11):e0242022.
PMID: 33186372 DOI: 10.1371/journal.pone.0242022

Damage assessment is a key element in structural health monitoring of various industrial applications to understand well and predict the response of the material. The big uncertainty in carbon fiber composite materials response is because of variability in the initiation and propagation of damage. Developing advanced tools to design with composite materials, methods for characterizing several damage modes during operation are required. While there is a significant amount of work on the analysis of acoustic emission (AE) from different composite materials and many loading cases, this research focuses on applying an unsupervised clustering method for separating AE data into several groups with distinct evolution. In this paper, we develop an adaptive sampling and unsupervised bivariate data clustering techniques to characterize the several damage initiations of a composite structure in different lay-ups. An adaptive sampling technique pre-processes the AE features and eliminates redundant AE data samples. The reduction of unnecessary AE data depends on the requirements of the proposed bivariate data clustering technique. The bivariate data clustering technique groups the AE data (dependent variable) with respect to the mechanical data (independent variable) to assess the damage of the composite structure. Tensile experiments on carbon fiber reinforced composite laminates (CFRP) in different orientations are carried out to collect mechanical and AE data and demonstrate the damage modes. Based on the mechanical stress-strain data, the results show the dominant damage regions in different lay-ups of specimens and the definition of the different states of damage. In addition, the states of the damage are observed using Scanning Electron Microscope (SEM) analysis. Based on the AE data, the results show that the strong linear correlation between AE and mechanical energy, and the classification of various modes of damage in all lay-ups of specimens forming clusters of AE energy with respect to the mechanical energy. Furthermore, the validation of the cluster-based characterization and improvement of the sensitivity of the damage modes classification are observed by the combined knowledge of AE and mechanical energy and time-frequency spectrum analysis.
Fulltext Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

May Z, Alam MK, Nayan NA, Rahman NAA, Mahmud MS

PLoS One, 2021;16(12):e0261040.
PMID: 34914761 DOI: 10.1371/journal.pone.0261040

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.