Search results for: deep Learning

Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

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Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

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Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

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Authors: Ali Zefeiti

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There has always been a debate on critical versus pragmatic approach to learning English. Different elements of teaching take different shapes in the two approaches. This study concerns itself with the students who are the main pillar of the teaching/learning operation. Students have always been placed into classrooms to learn what the curricula of different courses offer. There is little room for students to state their own learning needs as they often have to conform with the group requirement. This study focuses on an extra-curricular activity students did alongside their mainstream learning. The students come from different colleges and different EAP courses. They are united by their passion for the task and learning many things along the way. The data are collected through interviews and students' journals. The study was concerned with the effect of this extra-curricular activity on students' main learning trajectory. The students were engaged in the task of film production over the period of their English Language course. The findings show that students are able to set their own agenda for learning and have actually had a lot of skills and vocabulary to take to class.

Keywords: critical EAP, pragmatic EAP, self-directed learning, teaching methods

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Authors: Malini Ganapathy, Sarjit Kaur

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Maximising learning with higher-order thinking skills with Information and Communications Technology (ICT) has been deep-rooted and emphasised in various developed countries such as the United Kingdom, the United States of America and Singapore. The transformation of the education curriculum in the Malaysia Education Development Plan (PPPM) 2013-2025 focuses on the concept of Higher Order Thinking (HOT) skills which aim to produce knowledgeable students who are critical and creative in their thinking and can compete at the international level. HOT skills encourage students to apply, analyse, evaluate and think creatively in and outside the classroom. In this regard, the National Education Blueprint (2013-2025) is grounded based on high-performing systems which promote a transformation of the Malaysian education system in line with the vision of Malaysia’s National Philosophy in achieving educational outcomes which are of world class status. This study was designed to investigate ESL students’ learning practices on the emphasis of promoting HOTs while using ICT in their curricula. Data were collected using a stratified random sampling where 100 participants were selected to take part in the study. These respondents were a group of undergraduate students who undertook ESL courses in a public university in Malaysia. A three-part questionnaire consisting of demographic information, students’ learning experience and ICT utilization practices was administered in the data collection process. Findings from this study provide several important insights on students’ learning experiences and ICT utilization in developing HOT skills.

Keywords: English as a second language students, critical and creative thinking, learning, information and communication technology and higher order thinking skills

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Authors: Agoubi Belgacem, Adel Kharroubi

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Geothermal groundwater is the main water source to supply various sectors in El Hamma city, southeastern Tunisia. This region was long the destination of thousands of people from Tunisia and neighboring countries for care and bathing. The main objective of this study is to understand the groundwater mineralization origins and factors that control. The second goal is the appraisal of radon in geothermal groundwater in the study area. For this aim, geothermal groundwater was sampled and collected from different locations (thermal baths and deep wells). Physical parameters were measured and major ions were analyzed. Results reveal three water types. The water first type has Na-Mg-Ca-SO4-Cl facies and T>55°C. The second water type dominated by Na-Ca-Cl-SO4 facies with a temperature < 45 °C. However the third water type is dominated by Ca-SO4-Na-Cl-Mg. The three water types may be controlled by depth and geology. The first represent groundwater from deep aquifer (lower cretaceous), the second type was the shallow aquifer and the first is mixed water from deep and shallow water with a temperature ranging from 45 to 55°C. Measured Radon shows that shallow aquifer has a higher 222Rn concentration (677 to 2903 Bq.m-3) than deep water (203 to 1100 Bq.m-3). R-222 in El Hamma thermal aquifer was controlled by structures, porosity and permeability of aquifers. Geostatistical analyses of hydrogeological data and radon activities confirm the vertical flow and communication between deep and shallow aquifers through vertical faults system.

Keywords: Radon-222, geothermal, water, environment, Tunisia

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Authors: Tatyana Gavrilova, Vadim Onufriev

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Knowledge-based systems’ design requires the developer’s owning the advanced analytical skills. The efficient development of that skills within university courses needs a deep understanding of main pitfalls and drawbacks, which students usually make during their analytical work in form of visual modeling. Thus, it was necessary to hold an analysis of 5-th year students’ learning exercises within courses of 'Intelligent systems' and 'Knowledge engineering' in Saint-Petersburg Polytechnic University. The analysis shows that both lack of system thinking skills and methodological mistakes in course design cause the errors that are discussed in the paper. The conclusion contains an exploration of the issues and topics necessary and sufficient for the implementation of the improved practices in educational design for future curricula of teaching programs.

Keywords: knowledge based systems, knowledge engineering, students’ errors, visual modeling

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Authors: Hanan A. Algamdi, Huda Y. Alyami

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Learning object repositories contribute to developing educational process through its advantages; as they employ technology effectively, and use it to create new resources for effective learning, as well as they provide opportunities for collaboration in content through providing the ability for editing, modifying and developing it. This supports the relationships between communities that benefit from these repositories, and reflects positively on the content quality. Therefore, this study aims at exploring the most prominent learning topics in the 21st century, which should be included in learning object repositories, and identifying the necessary set of learning skills that the repositories should develop among today students. For conducting this study, the analytical descriptive method will be employed, and study sample will include a group of leaders, experts, and specialists in curricula and e-learning at ministry of education in Kingdom of Saudi Arabia.

Keywords: learning object, repositories, 21st century, quality

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Authors: Masaki Omata, Shumma Hosokawa

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An experiment to verify the relationships between physiological indexes of an e-learner and the presence or absence of an operation during e-learning is described. Electroencephalogram (EEG), hemoencephalography (HEG), skin conductance (SC), and blood volume pulse (BVP) values were measured while participants performed experimental learning tasks. The results show that there are significant differences between the SC values when reading with clicking on learning materials and the SC values when reading without clicking, and between the HEG ratio when reading (with and without clicking) and the HEG ratio when resting for four of five participants. We conclude that the SC signals can be used to estimate whether or not a learner is performing an active task and that the HEG ratios can be used to estimate whether a learner is learning.

Keywords: e-learning, physiological index, physiological signal, state of learning

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Authors: Jame O. Ezema, Odenigbo Veronica

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The study covers the topic Information Communication and Technology (ICTs) knowledge as a way of enhancing Literacy and Lifelong learning in Nigeria. This work delved into defining of ICTs. Types of ICTs and media technologies were also mentioned. It further explained how ICTs can be strengthened and the uses of ICTs in education was duly emphasized. The paper also enumerated some side effects of ICTs on learners while the role of ICTs in enhancing literacy was explained. The study carried out strategies to use ICTs meaningfully in Literacy Programs and also emphasized the word lifelong learning in Nigeria. Some recommendations were made towards acquiring ICTs knowledge, so as to enhance Literacy and Lifelong learning in Nigeria.

Keywords: literacy, distance-learning, life-long learning for sustainable development, e-learning

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Authors: Min Kyeong Cha, Ju Yeon Mun, Seong Baeg Kim

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Recently, ‘play of learning’ became important and is emphasized as a useful learning tool. Therefore, interest in edutainment contents is growing. Storytelling is considered first as a method that improves the transmission of information and learner's interest when planning edutainment contents. In this study, we designed edutainment contents in the form of an adventure game that applies the storytelling method. This content provides questions and items constituted dynamically and reorganized learning contents through analysis of test results. It allows learners to solve various questions through effective iterative learning. As a result, the learners can reach mastery learning.

Keywords: storytelling, edutainment, mastery learning, computer operating principle

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Authors: Shu-Chen Wu

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This qualitative study investigated Chinese teachers’ perspectives on learning at play. Six kindergarten teachers were interviewed to obtain their understanding of learning at play. Exemplary play episodes from their classrooms were selected with the assistance of the participating teachers. Four three-minute videos containing the largest amount of learning elements based on the teachers’ views were selected for analysis. Applying video-stimulated interviews, the selected video clips were shown to eight teachers in two focus groups to elicit their perspectives on learning at play. The findings revealed that Chinese teachers have a very structured representation of learning at play, which should contribute to the development of professional practices and curricular policies.

Keywords: learning at play, teachers’ perspectives, co-constructed views, video-stimulated interviews

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Marian Sorin Ionescu, Olivia Negoita, Cosmin Dobrin

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This scientific communication reports and discusses learning models adaptable to modern business problems and models specific to digital concepts and paradigms. In the PAC (probably approximately correct) learning model approach, in which the learning process begins by receiving a batch of learning examples, the set of learning processes is used to acquire a hypothesis, and when the learning process is fully used, this hypothesis is used in the prediction of new operational examples. For complex business models, a lot of models should be introduced and evaluated to estimate the induced results so that the totality of the results are used to develop a predictive rule, which anticipates the choice of new models. In opposition, for online learning-type processes, there is no separation between the learning (training) and predictive phase. Every time a business model is approached, a test example is considered from the beginning until the prediction of the appearance of a model considered correct from the point of view of the business decision. After choosing choice a part of the business model, the label with the logical value "true" is known. Some of the business models are used as examples of learning (training), which helps to improve the prediction mechanisms for future business models.

Keywords: machine learning, business models, convex analysis, online learning

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Authors: Masoomeh Alsadat Mirshafaei

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The present study aims to explore the effect of an 8-week aerobic training regimen combined with cinnamon extract on serum irisin and leptin levels in insulin-resistant rats. Additionally, this research leverages various machine learning (ML) and deep learning (DL) algorithms to model the complex interdependencies between exercise, nutrition, and metabolic markers, offering a groundbreaking approach to obesity and diabetes research. Forty-eight Wistar rats were selected and randomly divided into four groups: control, training, cinnamon, and training cinnamon. The training protocol was conducted over 8 weeks, with sessions 5 days a week at 75-80% VO2 max. The cinnamon and training-cinnamon groups were injected with 200 ml/kg/day of cinnamon extract. Data analysis included serum data, dietary intake, exercise intensity, and metabolic response variables, with blood samples collected 72 hours after the final training session. The dataset was analyzed using one-way ANOVA (P<0.05) and fed into various ML and DL models, including Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN). Traditional statistical methods indicated that aerobic training, with and without cinnamon extract, significantly increased serum irisin and decreased leptin levels. Among the algorithms, the CNN model provided superior performance in identifying specific interactions between cinnamon extract concentration and exercise intensity, optimizing the increase in irisin and the decrease in leptin. The CNN model achieved an accuracy of 92%, outperforming the SVM (85%) and RF (88%) models in predicting the optimal conditions for metabolic marker improvements. The study demonstrated that advanced ML and DL techniques could uncover nuanced relationships and potential cellular responses to exercise and dietary supplements, which is not evident through traditional methods. These findings advocate for the integration of advanced analytical techniques in nutritional science and exercise physiology, paving the way for personalized health interventions in managing obesity and diabetes.

Keywords: aerobic training, cinnamon extract, insulin resistance, irisin, leptin, convolutional neural networks, exercise physiology, support vector machines, random forest

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Authors: Samia Hassan, Fouzia Latif

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In many countries, education is still far from being a knowledge industry in the sense of own practices that are not yet being transformed by knowledge about the efficacy of those practices. The core question of this paper is why students get bored in class? Have we balanced between the creation and advancement of an engaging learning community and effective learning environment? And between, giving kids confidence to achieve their maximum and potential goals, we sand managing student’s behavior. We conclude that creating a positive learning environment enhances opportunities for young children to feel safe, secure, and to supported in order to do their best learning. Many factors can use in classrooms aid to the positive environment like course content, class preparation, and behavior.

Keywords: effective, environment, learning, positive

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Authors: Mohammed T. Taher, Usman Ghani, Ahmed S. Khan

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This paper compares the findings of two studies conducted to determine the effectiveness of simulation-based, hands-on and feedback mechanism on students learning by answering the following questions: 1). Does the use of simulation improve students’ learning outcomes? 2). How do students perceive the instructional design features embedded in the simulation program such as exploration and scaffolding support in learning new concepts? 3.) What is the effect of feedback mechanisms on students’ learning in the use of simulation-based labs? The paper also discusses the other aspects of findings which reveal that simulation by itself is not very effective in promoting student learning. Simulation becomes effective when it is followed by hands-on activity and feedback mechanisms. Furthermore, the paper presents recommendations for improving student learning through the use of simulation-based, hands-on, and feedback-based teaching methodologies.

Keywords: simulation-based teaching, hands-on learning, feedback-based learning, scaffolding

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Authors: Brinda Sharma, Saakshi S. Sarpotdar

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Deep-fat frying is popular standard method that has been studied basically to clarify the complicated mechanisms of fat decomposition at high temperatures and to assess their effects on human health. The aim of this paper is to point out the application of method engineering that has been recently improved our understanding of the fundamental principles and mechanisms concerned at different scales and different times throughout the process: pretreatment, frying, and cooling. It covers the several aspects of deep-fat drying. New results regarding the understanding of the frying method that are obtained as a results of major breakthroughs in on-line instrumentation (heat, steam flux, and native pressure sensors), within the methodology of microstructural and imaging analysis (NMR, MRI, SEM) and in software system tools for the simulation of coupled transfer and transport phenomena. Such advances have opened the approach for the creation of significant information of the behavior of varied materials and to the event of latest tools to manage frying operations via final product quality in real conditions. Lastly, this paper promotes an integrated approach to the frying method as well as numerous competencies like those of chemists, engineers, toxicologists, nutritionists, and materials scientists also as of the occupation and industrial sectors.

Keywords: frying, cooling, imaging analysis (NMR, MRI, SEM), deep-fat frying

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Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

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Authors: T. Zaki, Fathi S. Soliman

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Desulfurization process is required by most, if not all refineries, to achieve ultra-low sulfur fuel, that contains less than 10 ppm sulfur. A lot of research works and many effective technologies have been studied to achieve deep desulfurization process in moderate reaction environment, such as adsorption desulfurization (ADS), oxidative desulfurization (ODS), biodesulfurization and extraction desulfurization (EDS). Extraction desulfurization using deep eutectic solvents (DESs) is considered as simple, cheap, highly efficient and environmentally friend process. In this work, four DESs were designed and synthesized. Choline chloride (ChCl) was selected as typical hydrogen bond acceptors (HBA), and ethylene glycol (EG), glycerol (Gl), urea (Ur) and thiourea (Tu) were selected as hydrogen bond donors (HBD), from which a series of deep eutectic solvents were synthesized. The experimental data showed that the synthesized DESs showed desulfurization affinities towards the thiophene species in cyclohexane solvent. Ethylene glycol molecules showed more affinity to create hydrogen bond with thiophene instead of choline chloride. Accordingly, ethylene glycol choline chloride DES has the highest extraction efficiency.

Keywords: DES, desulfurization, green solvent, extraction

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Authors: Susan E Orlovich

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Ongoing, effective teacher professional learning is acknowledged as a critical influence on teacher practice. However, it is unclear whether the elements of effective professional learning result in transformed teacher practice in the classroom. This research project is interested in 4C teacher professional learning. The professional learning practices to assist teachers in transforming their practice to integrate the 4C capabilities seldom feature in the academic literature. The 4Cs are a shorthand way of representing the concepts of communication, collaboration, creativity, and critical reflection and refer to the capabilities needed for deeper learning, personal growth, and effective participation in society. The New South Wales curriculum review (2020) acknowledges that identifying, teaching, and assessing the 4C capabilities are areas of challenge for teachers. However, it also recognises that it is essential for teachers to build the confidence and capacity to understand, teach and assess the capabilities necessary for learners to thrive in the 21st century. This qualitative research project explores the professional learning experiences of sixteen teachers in four different primaries (K-6) settings in Sydney, Australia, who are learning to integrate, teach and assess the 4Cs. The project draws on the Theory of Practice Architecture as a framework to analyse and interpret teachers' experiences in each site. The sixteen participants in the study are teachers from four primary settings and include early career, experienced, and teachers in leadership roles (including the principal). In addition, some of the participants are also teachers who are learning within a Community of Practice (CoP) as their school setting is engaged in a 4C professional learning, Community of Practice. Qualitative and arts-informed research methods are utilised to examine the cultural-discursive, social-political, and material-economic practice arrangements of the site, explore how these arrangements may have shaped the professional learning experiences of teachers, and in turn, influence the teaching practices of the 4Cs in the setting. The research is in the data analysis stage (October 2022), with preliminary findings pending. The research objective is to investigate the elements of the professional learning experiences undertaken by teachers to teach the 4Cs in the primary setting. The lens of practice architectures theory is used to identify the influence of the practice architectures on critical praxis in each site and examine how the practice arrangements enable or constrain the teaching of 4C capabilities. This research aims to offer deep insight into the practice arrangements which may enable or constrain teacher professional learning in the 4Cs. Such insight from this study may contribute to a better understanding of the practices that enable teachers to transform their practice to achieve the integration, teaching, and assessment of the 4C capabilities.

Keywords: 4Cs, communication, collaboration, creativity, critical reflection, teacher professional learning

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Authors: Habsah Mohamad Sabli, Mohammad Fardillah Wahi

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The main aim of this research study is to better understand and measure students' perceptions towards the effectiveness of mobile learning. This paper reports on the results of a survey of three hundred nineteen students at Polytechnic Mukah Sarawak (PMU) about their perception to the use of mobile technology in education. An analysis of the quantitative survey findings is presented focusing on the ramification for mobile-learning (m-learning) practices in higher learning and teaching environments. In this paper we present our research findings about the level of perception and gender correlations with perceived ease of use and perceived usefulness using M-Learning in learning activities among students in Polytechnic Mukah (PMU). Based on gender respondent, were 150 female (47.0%) and 169 male (53.0%). The survey findings further revealed that perception of students are in moderately high and agree for using m-learning. The perceived ease of use and perceived usefulness is significant with weak correlations between students to adapt m-learning for active learning activities. The outcome of this research can benefit the decision makers of higher institution in Mukah Sarawak regard to way to enhance m-learning and promote effective teaching and learning activities as well as strengthening the quality of learning delivery.

Keywords: M-learning, student attitudes, student perception, mobile technology

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Authors: Thien Van Ngo

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The engineering design process is a systematic approach to solving problems. It involves identifying a problem, brainstorming solutions, prototyping and testing solutions, and evaluating the results. The engineering design process can be used to teach students how to solve problems in a creative and innovative way. The research aim of this study was to investigate the effectiveness of using the engineering design process to enhance student learning outcomes in a physics course. A mixed research method was used in this study. The quantitative data were collected using a pretest-posttest control group design. The qualitative data were collected using semi-structured interviews. The sample was 150 first-year students in the Department of Mechanical Engineering Technology at Cao Thang Technical College in Vietnam in the 2022-2023 school year. The quantitative data were collected using a pretest-posttest control group design. The pretest was administered to both groups at the beginning of the study. The posttest was administered to both groups at the end of the study. The qualitative data were collected using semi-structured interviews with a sample of eight students in the experimental group. The interviews were conducted after the posttest. The quantitative data were analyzed using independent sample T-tests. The qualitative data were analyzed using thematic analysis. The quantitative data showed that students in the experimental group, who were taught using the engineering design process, had significantly higher post-test scores on physics problem-solving than students in the control group, who were taught using the conventional method. The qualitative data showed that students in the experimental group were more motivated and engaged in the learning process than students in the control group. Students in the experimental group also reported that they found the engineering design process to be a more effective way of learning physics. The findings of this study suggest that the engineering design process can be an effective way of enhancing student learning outcomes in physics courses. The engineering design process engages students in the learning process and helps them to develop problem-solving skills.

Keywords: engineering design process, problem-solving, learning outcome of physics, students’ physics competencies, deep learning

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Authors: K. Kannapathi-Naidoo

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The higher education landscape has undergone changes in the past decade, with concepts such as blended learning, online learning, and hybrid models appearing more frequently in research and practice. The year 2020 marked a mass migration from face-to-face learning and more traditional forms of education to online learning in higher education institutions across the globe due to the Covid-19 pandemic. As a result, contact learning students and lecturing staff alike were thrust into the world of online learning at an unprecedented pace. Traditional modes of learning had to be amended, and pedagogical strategies required adjustments. This study was located within a compulsory first-year academic literacy module in a higher education institution. The study aimed to explore students’ learning preferences between online, face-face, and blended learning within the context of academic literacy. Data was collected through online qualitative questionnaires administered to 150 first-year students, which were then analysed thematically. The findings of the study revealed that 48.5% of the participants preferred a blended learning approach to academic literacy. The main themes that emerged in support of their preference were best of both worlds, flexibility, productivity, and lecturer accessibility. As a result, this paper advocates for the blended learning approach for academic literacy skills-based modules.

Keywords: academic literacy, blended learning, online learning, student learning preferences

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Authors: Kian Jon Chua, Islam Md Raisul

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Findings on a research study conducted for a group of year-2 engineering students participating in a flipped classroom (FC) experience that is judiciously incorporated into project-based learning (PBL) module are presented. The chief purpose of the research is to identify whether if the incorporation of flipped classroom approach to project-based learning indeed yields a positive learning experience for engineering students. Results are presented and compared from the two classes of students – one is subjected to a traditional PBL learning mode while the other undergoes a hybrid PBL-FC learning format. Some themes related to active learning, problem-solving ability, teacher as facilitator, and degree of self-efficacy are also discussed. This paper hopes to provide new knowledge and insights relating to the introduction of flipped classroom learning to a project-based engineering module. Some potential study limitations and future directions to address them are also presented.

Keywords: hybrid project-based learning, flipped classroom, problem-solving, active learning

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Authors: Huie-Wen Lin

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This study integrated various teaching factors -based on the idea of a flipped classroom- in a financial management course. The study’s aim was to establish an effective teaching implementation strategy and evaluation mechanism with respect to learning outcomes, which can serve as a reference for the future modification of teaching methods. This study implemented a teaching method in five stages and estimated the learning efficiencies of 22 students (in the teaching scenario and over two semesters). Subsequently, data envelopment analysis (DEA) was used to compare, for each student, between the learning efficiencies before and after participation in the flipped classroom -in the first and second semesters, respectively- to identify the crucial external factors influencing learning efficiency. According to the results, the average overall student learning efficiency increased from 0.901 in the first semester to 0.967 in the second semester, which demonstrate that the flipped classroom approach can improve teaching effectiveness and learning outcomes. The results also revealed a difference in learning efficiency between male and female students.

Keywords: data envelopment analysis, flipped classroom, learning outcome, teaching and learning

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Authors: Ng Siew Keow, Cheah Seeh Lee

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Students’ strong engagement in learning increases their motivation and satisfaction to learn, be resilient to combat academic stress. Engagement in learning is even crucial in the open distance learning (ODL) setting, where the adult students are learning remotely, lessons and learning materials are mostly delivered via online platforms. This study aimed to explore the relationship between learning engagement and perceived academic stress levels of adult students who enrolled in ODL learning mode. In this descriptive correlation study during the 2021-2022 academic years, 101 adult students from Wawasan Open University, Malaysia (WOU) were recruited through convenient sampling. The adult students’ online learning engagement levels and perceived academic stress levels were identified through the self-report Online Student Engagement Scale (OSE) and the Perception of Academic Stress Scale (PASS). The Pearson correlation coefficient test revealed a significant positive relationship between online student engagement and perceived academic stress (r= 0.316, p<0.01). The higher scores on PASS indicated lower levels of perceived academic stress. The findings of the study supported the assumption of the importance of engagement in learning in promoting psychological well-being as well as sustainability in online learning in the open distance learning context.

Keywords: student engagement, academic stress, open distance learning, online learning

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Authors: Reza Khani, Ziba Hosseini

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Ever since the importance of language learning strategy instruction (LLS) has been distinguished, there has been growing interest on how to teach LLS in language learning classrooms. So thus this study attempted to implement language strategy instruction based on CALLA approach for Iranian EFL learners in a real classroom setting. The study was testing the hypothesis that strategy instruction result in improved linguistic strategy of students. The participant of the study were 240 EFL learners who received language learning instruction for four months. The data collected using Oxford strategy inventory for language learning. The results indicated the instruction had statistically significant effect on language strategy use of intervention group who received instruction.

Keywords: CALLA, language learning strategy, language learning strategy instruction, Iranian EFL language strategy

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Authors: Dhanuj M. Gandikota

Abstract:

Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

Abstract:

Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

Procedia PDF Downloads 307