The implementation of problem-based learning started in 1969 and has spread since then throughout different parts of the world with variations in its implementation. In spite of its growth and advantages, there is continuing debate about its effectiveness over the conventional teaching learning methods. In the School of Dental Sciences (SDS), Universiti Sains Malaysia (USM), the Doctor of Dental Sciences (DDS) program follows a 5-year integrated curriculum. Basically the curriculum is problem-based and community oriented. This study was to explore the perception of DDS students about PBL sessions. This questionnaires-based cross sectional descriptive study were carried out on all the 110 students of the SDS who completed their second year of the course and participated in PBL sessions. Ninety five (86%) students responded to the questionnaires. Dental students found PBL session interesting and wanted to maintain PBL from the beginning of year 2 up to the end of year 3. Most students reported their participation in discussion during PBL sessions but the level of participation varied. Some of them worked hard to prepare themselves for discussion while others were relatively passive. PBL helped them with in-depth understanding of certain topics and link their basic science knowledge to clinical classes. They felt that guidance from subject specialists and well-prepared facilitators of the sessions were beneficial. The students believed that repetition of triggers from year to year discouraged their active search for learning issues. Majority of the students were undecided or disagreed about the availability of adequate learning resources Most of the students were undecided or disagreed about the availability of adequate learning resources for their self-study. Reviewing and renewing the PBL triggers, providing guidelines for searching for resource materials and briefing the students and facilitators about the philosophy and principles of PBL may make the PBL sessions more beneficial.
This study explores the experience of both learners and a teacher during a team-based learning (TBL) session. TBL involves active learning that allows medical students to utilise their visual, auditory, writing and kinetic learning styles in order to strengthen their knowledge and retain it for longer, which is important with regard to applying basic sciences in clinical settings. This pilot study explored the effectiveness of TBL in learning medical genetics, and its potential to replace conventional lectures. First-year medical students (n = 194) studying at Universiti Kebangsaan, Malaysia, during 2014/2015 were selected to participate in this study. The topic of 'Mutation and Mutation Analysis' was selected, and the principles of TBL were adhered to during the study. It was found that the students' performance in a group readiness test was better than in individual readiness tests. The effectiveness of TBL was further shown in the examination, during which the marks obtained were tremendously improved. Collective commentaries from both the learners and the teacher recommended TBL as another useful tool in learning medical genetics. Implementation strategies should be advanced for the benefit of future learners and teachers.
This study employs diverse machine learning models, including classic artificial neural network (ANN), hybrid ANN models, and the imperialist competitive algorithm and emotional artificial neural network (EANN), to predict crucial parameters such as fresh water production and vapor temperatures. Evaluation metrics reveal the integrated ANN-ICA model outperforms the classic ANN, achieving a remarkable 20% reduction in mean squared error (MSE). The emotional artificial neural network (EANN) demonstrates superior accuracy, attaining an impressive 99% coefficient of determination (R2) in predicting freshwater production and vapor temperatures. The comprehensive comparative analysis extends to environmental assessments, displaying the solar desalination system's compatibility with renewable energy sources. Results highlight the potential for the proposed system to conserve water resources and reduce environmental impact, with a substantial decrease in total dissolved solids (TDS) from over 6,000 ppm to below 50 ppm. The findings underscore the efficacy of machine learning models in optimizing solar-driven desalination systems, providing valuable insights into their capabilities for addressing water scarcity challenges and contributing to the global shift toward sustainable and environmentally friendly water production methods.
This study aims to develop two alternate forms for Malay version of Auditory Verbal Learning Test (MAVLT) and to determine their equivalency and practice effect. Ninety healthy volunteers were subjected to the following neuropsychological tests at baseline, and at one month interval according to their assigned group; group 1 (MAVLT - MAVLT), group 2 (MAVLT - Alternate Form 1 - Alternate Form 1), and group 3 (MAVLT - Alternate Form 2 - Alternate Form 2). There were no significant difference in the mean score of all the trials at baseline among the three groups, and most of the mean score of trials between MAVLT and Alternate Form 1, and between MAVLT and Alternate Form 2. There was significant improvement in the mean score of each trial when the same form was used repeatedly at the interval of one month. However, there was no significant improvement in the mean score of each trial when the Alternate Form 2 was used during repeated neuropsychological testing. The MAVLT is a reliable instrument for repeated neuropsychological testing as long as alternate forms are used. The Alternate Form 2 showed better equivalency to MAVLT and less practice effects.
Public universities in Saudi Arabia today are making substantial investments in e-learning as part of their educational system, especially in the implementation of learning management systems (LMS). To our knowledge, this is the first study conducted in Saudi Arabia exploring medical students' experience with an LMS, particularly as part of a medical informatics course.
In the current study we evaluated adverse effects of monosodium glutamate (MSG) on memory formation and its retrieval as well as the role of ascorbic acid (Vitamin-C) in prevention of MSG-induced alteration of neurobehavioral performance in periadolescent rats.
Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein.
An interactive teaching tool that utilizes a game board strategy to facilitate the learning of statistics has been developed and employed. This study aims to determine the impact of the board game on the motivation of diploma students towards learning statistics. Data are collected from 7 respondents using a face to face interview. Responses are qualitatively analysed. Results show that respondents are generally positive about the effectiveness of the board game as a teaching tool to enhance le arning and understanding. More importantly, respondents have shown a change in attitude towards statistics and are more motivated to learn statistics under this innovative learning environment. Some suggestions for future research are outlined.
To foster a milieu in which student learning can be optimum, teachers need to be aware of the attributes of a safe learning environment. This is the space created in the students' minds to seamlessly promote learning. The 10 maxims, presented in this paper, are the cornerstones, nay, the capstones, for making this happen.
In this paper, a simple analysis yet a straight forward method of determining the Planck’s constant by
evaluating the stopping potential of five different colors of light emitting diodes (LEDs) is presented.
The study aimed to identify the Planck’s constant based on the relationship between the potential
difference of LEDs to their respective frequencies under room temperature with low illumination of
ambient light by applying a simple theoretical analysis. The experiment was performed by connecting
the circuit in series connection and the voltage reading of LEDs were recorded and then presented in a
graph of frequency, f versus stopping voltage, Vo. To determine the Planck’s constant, the best fit line
was analyzed and the centroid was also identified in order to find the minimum and maximum errors
due the gradient of the graph. From the analysis, results showed that the Planck constant value was
(5.997 ± 1.520) × 10–34 J.s with approximately 10% of deviation from the actual value. This
demonstrates that a simple analysis can be utilized to determine the Planck’s constant for the purpose
of the laboratory teaching and learning at the undergraduate level and can be served as a starting point
for the students to understand the concept of quantization of energy in Modern Physics more
effectively. This is to further suggest that the Planck’s constant can be identified via a low-cost and
unsophisticated experimental setup.
In predicting palm oil mill effluent (POME) degradation efficiency, previous developed quadratic model quantitatively evaluated the effects of O2 flowrate, TiO2 loadings and initial concentration of POME in labscale photocatalytic system, which however suffered from low generalization due to the overfitting behaviour. Evidently, high RMSE (131.61) and low R2 (-630.49) obtained indicates its insufficiency in describing POME degradation at unseen factor ranges, hence verified the fact of poor generalization. To overcome this issue, several models were developed via machine learning-assisted techniques, namely Gaussian Process Regression (GPR), Linear Regression (LR), Decision Tree (DT), Supported Vector Machine (SVM) and Regression Tree Ensemble (RTE), subsequently being assessed systematically. To achieve high generalization, all models were subjected to 'train-all-test-all' strategy, 5-fold and 10-fold cross validation. Specifically, GPR model was furnished with high accuracy in 'train-all-test-all' strategy, judging from its low RMSE (1.0394) and high R2 (0.9962), which however menaced by the risk of overfitting. In contrast, despite relatively poorer RMSE and R2 (1.7964 and 0.9886) obtained in 5-fold cross validation, GPR model was rendered with highest generalization, while sufficiently preserving its accuracy in development process. Besides, SVM and RTE models were also demonstrated promising R2 (0.9372 and 0.9208), which however shadowed by their high RMSEs (4.2174 and 4.7366). Furthermore, the extraordinary generalization of GPR model was coincidentally verified in 10-fold cross validation. The lowest RMSE (2.1624) and highest R2 (0.9835) obtained with feature number of 36 asserted its sufficiency in both generalization and accuracy prospect. Other models were all rendered with slight lower R2 (> 0.9), plausibly due to the higher RMSE (> 4.0). According to GPR model, optimized POME degradation (52.52%) can be obtained at 70 mL/min of O2, 70.0 g/L of TiO2 and 250 ppm of POME concentration, with only ∼3% error as compared to the actual data.
This study describes the review on maintenance related issues during design and construction stage
within construction industry. The paper highlights the causes and errors made during design and
construction stage and their impact during the operation/production/occupancy stage as well as the
maintenance costs associated with it. The study identifies the mistakes in the working processes within
design and construction stage leading to the errors that affect the durability, performance, reliability,
maintainability, availability and safety of the systems. The paper presents a comprehensive review of
the published literatures, journals, technical papers in the related areas in the construction field. The
review highlights the new approaches and decision framework which link the designers and
construction personnel that could reduce the errors and defects in construction which then lead to
maintenance issues and asset management. The factors of accessibility, materials, design and
documentation standardization have been discussed thoroughly for better understanding in improving
maintenance and physical asset management in project commissioning.
The data presented in this article are based on provides a systematic and organized review of 219 studies regarding using of Massive Open Online Courses (MOOCs) in higher education from 2012 to 2017. Consequently, the extant, peer-reviewed literature relating to MOOCs was methodically assessed, as a means of formulating a classification for MOOC-focused scholarly literature. The publication journal, country of origin, researchers, release data, theoretical approach, models, methodology and study participants were all factors used to assess and categorise the MOOC. These data contribute to materials required by readers who are interested in different aspects related to the literature of using Massive Open Online Courses (MOOCs) in higher education. Intention to use, interaction, engagement, motivations and satisfaction were five dynamics assessed in relation to the improvement of MOOCs. Students' academic performance can be influenced by MOOC which has the advantage of facilitating the learning process through offering materials and enabling the share of information.
Optimization is central to any problem involving decision making. The area
of optimization has received enormous attention for over 30 years and it is still popular
in research field to this day. In this paper, a global optimization method called Improved
Homotopy with 2-Step Predictor-corrector Method will be introduced. The method in-
troduced is able to identify all local solutions by converting non-convex optimization
problems into piece-wise convex optimization problems. A mechanism which only consid-
ers the convex part where minimizers existed on a function is applied. This mechanism
allows the method to filter out concave parts and some unrelated parts automatically.
The identified convex parts are called trusted intervals. The descent property and the
global convergence of the method was shown in this paper. 15 test problems have been
used to show the ability of the algorithm proposed in locating global minimizer.
This paper discusses methodological dilemma that arise in qualitative research, specifically in education field. It outlines the broad principles that underpin good qualitative research and the aspects of practice that qualitative researchers should consider when designing, conducting, and disseminating their research. Two primary methodological dilemma are (i) lack of objectivity, and (ii) issue of generalizability in qualitative research. The aim of this paper is to argue the dilemmas and encourage researchers to examine the relevance of qualitative issues to their own research. These dilemmas could be taken as important consideration for others who wish to conduct qualitative research in education.
As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.
The role of caregivers is very important in the management of person with dementia, where it is not uncommon for them to experience psychological distress. However, the level of distress can be managed and reduced through stra- tegic educational intervention. A systematic review has been conducted through searching Medline, Science direct, Cochrane library and EMBASE databases to provide a narrative synthesis that elaborate on methods and outcomes of the educational intervention among informal caregiver of person with dementia. From a total of 5125 records, eight studies were selected and included in this review, where the results show that educational intervention can be implemented either as individual or group intervention. Group intervention methods mainly focus on training pro- grams such as workshops and lectures, and also group-based discussions. While for individual intervention, most of the activities were implemented through self-learning using technology or computer-based systems. In conclusion, based on the outcome of the studies, both methods of implementations are found to be useful in reducing psycho- logical distress of the informal caregiver.