Search results for: blended and integrated learning

Authors: Jeff Chak-Fu Wong, Tony Chun Yin Yip

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The transition from secondary school to university seems exciting for many first-year students but can be more challenging than expected. Enabling instructors to know students’ learning habits and styles enhances their understanding of the students’ learning backgrounds, allows teachers to provide better support for their students, and has therefore high potential to improve teaching quality and learning, especially in any mathematics-related courses. The aim of this research is to collect students’ data using online surveys, to analyze students’ factors using learning analytics and educational data mining and to discover the characteristics of the students at risk of falling behind in their studies based on students’ previous academic backgrounds and collected data. In this paper, we use correlation-based distance methods and mutual information for measuring student factor relationships. We then develop a factor network using the Minimum Spanning Tree method and consider further study for analyzing the topological properties of these networks using social network analysis tools. Under the framework of mutual information, two graph-based feature filtering methods, i.e., unsupervised and supervised infinite feature selection algorithms, are used to analyze the results for students’ data to rank and select the appropriate subsets of features and yield effective results in identifying the factors affecting students at risk of failing. This discovered knowledge may help students as well as instructors enhance educational quality by finding out possible under-performers at the beginning of the first semester and applying more special attention to them in order to help in their learning process and improve their learning outcomes.

Keywords: students' academic performance, correlation-based distance method, social network analysis, feature selection, graph-based feature filtering method

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Authors: Aya Salama

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Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

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Authors: Shamshuddin K., Nagaraj Vannal, Diwakar Kulkarni

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This paper presents details of teaching and learning pedagogical techniques attempted for the undergraduate engineering program to improve the concentration span of students in a classroom. The details of activities such as valid statement, quiz competition, classroom paper, group work and product marketing to make the students remain active for the entire class duration and to improve presentation skills are presented. These activities shown tremendous improvement in student’s performance in academics, also in asking questions, concept understanding and interaction with the course instructor. With these pedagogical activities we are able to achieve Program outcome elements and ABET Program outcomes such as d, i, g and h which are difficult to achieve through the conventional teaching methods.

Keywords: activities, pedagogy, interactive learning, valid statement, quiz competition, classroom papers, group work, product marketing

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Authors: Eunice H. Li

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This e-Poster presents how the ‘digital backpack’ was introduced to primary school children in Ivory Coast. The idea of a ‘digital backpack’ was initiated by Mr. Thierry N’Doufou in 2012, who later designed and presented to the rest of the world in September 2014. The motivation behind the backpack was to relieve children of the heavy-weight they carry in their school backpacks. Another motivation was to promote Ivory Coast as a country where all children are brought into the digital era. Thierry N’Doufou regards education as the means by which his nation and the entire African Continent can be developed as a prosperous territory. The ‘digital backpack’ contains the entire curriculum for each class and favours a constructivist approach to learning. The children’s notes and exercises are also included in the pack. Additionally, teachers and parents are able to monitor remotely children’s activities while they are working with the ‘backpack’. Teachers are also able to issue homework, assess student’s progress and manage the student’s coursework. This means that teachers should always think the most appropriate pedagogies that can be used to help children to learn. Furthermore, teachers, parents and fellow students are able to have conversations and discussions by using web portals. It is also possible to access more apps if children would like to have additional learning activities. From the presentation in the e-Poster, it seems reasonable to conclude that the ‘digital backpack’ has potential to reach other-level of education. In this way, all will be able to benefit from this new invention.

Keywords: pedagogy, curriculum, constructivism, social constructivism, distance learning environment, ubiquitous learning environment

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Authors: Shatha Ahmad Alharthi

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Prior research suggests that reduced social interaction can negatively affect well-being and impair mental health (e.g., depression and anxiety), resulting in lower academic performance. The COVID-19 pandemic has significantly limited social interaction among Saudi Arabian school children since the government closed schools and implemented lockdown restrictions to reduce the spread of the disease. These restrictions have resulted in prolonged remote learning for middle school students with unknown consequences for perceived academic performance, mental health, and well-being. This research project explores how middle school Saudi students’ current remote learning practices affect their mental health (e.g., depression and anxiety) and well-being during the lockdown. Furthermore, the study will examine the association between social interaction, mental health, and well-being pertaining to students’ perceptions of their academic achievement. Research findings could lead to a better understanding of the role of lockdown on depression, anxiety, well-being and perceived academic performance. Research findings may also inform policy-makers or practitioners (e.g., teachers and school leaders) about the importance of facilitating increased social interactions in remote learning situations and help to identify important factors to consider when seeking to re-integrate students into a face-to-face classroom setting. Potential implications for future educational research include exploring remote learning interventions targeted at bolstering students’ mental health and academic achievement during periods of remote learning.

Keywords: depression, anxiety, academic performance, social interaction

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Authors: Yacoub Aljaffery

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As much as we think negatively about social media such as TV and smart phones, there are many positive benefits our society can get from it. Cartoons, for example, are made specifically for children. However, in this paper, we will prove how cartoon videos can have a positive impact on adults, especially college students. Since cartoons are meant to be a good learning tool for children, as well as adults, we will show our audience how they can use cartoon in teaching critical thinking and other language skills.

Keywords: social media, TV, teaching, learning, cartoon movies

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Authors: Yuan-Hsiang Chang, Zhong-Xian Peng, Li-Der Jeng

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Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs.

Keywords: connected component labeling, image processing, morphological processing, optical musical recognition

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Authors: Romisaa Ali

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The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Alvyra Galkienė, Ona Monkevičienė

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The full participation of all pupils in the overall educational process is defined by the concept of inclusive education, which is gradually evolving in education policy and practice. It includes the full participation of all pupils in a shared learning experience and educational practices that address barriers to learning. Inclusive education applying the principles of Universal Design for Learning (UDL), which includes promoting students' involvement in learning processes, guaranteeing a deep understanding of the analysed phenomena, initiating self-directed learning, and using e-tools to create a barrier-free environment, is a prerequisite for the personal success of each pupil. However, the sustainability of quality education is affected by the transformation of education systems. This was particularly evident during the period of the forced transition from contact to distance education in the COVID-19 pandemic. Research Problem: The transformation of the educational environment from real to virtual one and the loss of traditional forms of educational support highlighted the need for new research, revealing the individual profiles of teachers using UDL-based learning and the pathways of sustainable transfer of successful practices to non-conventional learning environments. Research Methods: In order to identify individual latent teacher profiles that encompass the essential components of UDL-based inclusive teaching and direct leadership of students' learning, the quantitative analysis software Mplius was used for latent profile analysis (LPA). In order to reveal proven, i.e., sustainable, pathways for the transit of the components of UDL-based inclusive learning to distance learning, latent profile transit analysis (LPTA) via Mplius was used. An online self-reported questionnaire was used for data collection. It consisted of blocks of questions designed to reveal the experiences of subject teachers in contact and distance learning settings. 1432 Lithuanian, Latvian, and Estonian subject teachers took part in the survey. Research Results: The LPA analysis revealed eight latent teacher profiles with different characteristics of UDL-based inclusive education or traditional teaching in contact teaching conditions. Only 4.1% of the subject teachers had a profile characterised by a sustained UDL approach to teaching: promoting pupils' self-directed learning; empowering pupils' engagement, understanding, independent action, and expression; promoting pupils' e-inclusion; and reducing the teacher's direct supervision of the students. Other teacher profiles were characterised by limited UDL-based inclusive education either due to the lack of one or more of its components or to the predominance of direct teacher guidance. The LPTA analysis allowed us to highlight the following transit paths of teacher profiles in the extreme conditions of the transition from contact to distance education: teachers staying in the same profile of UDL-based inclusive education (sustainable transit) or jumping to other profiles (unsustainable transit in case of barriers), and teachers from other profiles moving to this profile (ongoing transit taking advantage of the changed new possibilities in the teaching process).

Keywords: distance education, latent teacher profiles, sustainable transit, UDL

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Authors: Thomas Turner

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Due to the Covid-19 pandemic alternative ways of delivering dental education were required. As a result, many institutions moved teaching online. The impact of this is poorly understood. Is online problem-based learning (PBL) and case-based learning (CBL) effective and is it suitable in the post-pandemic era? PBL and CBL are both types of interactive, group-based learning which are growing in popularity within many dental schools. PBL was first introduced in the 1960’s and can be defined as learning which occurs from collaborative work to resolve a problem. Whereas CBL encourages learning from clinical cases, encourages application of knowledge and helps prepare learners for clinical practice. To evaluate the use of online PBL and CBL. A literature search was conducted using the CINAHL, Embase, PubMed and Web of Science databases. Literature was also identified from reference lists. Studies were only included from dental education. Seven suitable studies were identified. One of the studies found a high learner and facilitator satisfaction rate with online CBL. Interestingly one study found learners preferred CBL over PBL within an online format. A study also found, that within the context of distance learning, learners preferred a hybrid curriculum including PBL over a traditional approach. A further study pointed to the limitations of PBL within an online format, such as reduced interaction, potentially hindering the development of communication skills and the increased time and technology support required. An audience response system was also developed for use within CBL and had a high satisfaction rate. Interestingly one study found achievement of learning outcomes was correlated with the number of student and staff inputs within an online format. Whereas another study found the quantity of learner interactions were important to group performance, however the quantity of facilitator interactions was not. This review identified generally favourable evidence for the benefits of online PBL and CBL. However, there is limited high quality evidence evaluating these teaching methods within dental education and there appears to be limited evidence comparing online and faceto-face versions of these sessions. The importance of the quantity of learner interactions is evident, however the importance of the quantity of facilitator interactions appears to be questionable. An element to this may be down to the quality of interactions, rather than just quantity. Limitations of online learning regarding technological issues and time required for a session are also highlighted, however as learners and facilitators get familiar with online formats, these may become less of an issue. It is also important learners are encouraged to interact and communicate during these sessions, to allow for the development of communication skills. Interestingly CBL appeared to be preferred to PBL in an online format. This may reflect the simpler nature of CBL, however further research is required to explore this finding. Online CBL and PBL appear promising, however further research is required before online formats of these sessions are widely adopted in the post-pandemic era.

Keywords: case-based learning, online, problem-based learning, remote, virtual

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Authors: Ghazi Faisal Najmuldeen, Wahida Amat Fadzila

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The aim of this study is to investigate physical and mechanical properties of experimental particleboards manufactured from mixing the oil palm fronds particles with empty fruit bunch fibers. Variables were two blending ratios (100:0 and 70:30), press temperature (160°C and 180°C) and press time (180 and 300 s). Experimental boards with a target density of 750 kg m-3 were manufactured from these two particles and fibers blended with urea formaldehyde resin and compressed into targeted thickness. The effect of these manufacturing conditions on bending strength, internal bonding, water absorption and thickness swelling were determined. From this research, it can be concluded that hybridization of fibers with fronds particles improved some properties of particleboard. Empty fruit bunch fibers and fronds particleboard showed better modulus of rupture and internal bonding than fronds particleboards.

Keywords: oil palm fronds, empty fruit bunch, particleboards, chemistry, environment

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Authors: Anggun Yuliana Putri, Melisa Rahmadini

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The Ministry of National Development Planning in Indonesia has reported that more than eight million Indonesian children are still malnourished. Based on national statistics, and a recent ranking from NGO Save the Children, Indonesia is one of 15 countries making the fastest gains in cutting child malnutrition among 165 developing countries. According to a United Nations Children’s Fund, at least 7.6 million Indonesian children under the age of 5 or one out of every three suffer from stunted growth, a primary manifestation of malnutrition in early childhood, the report ranked Indonesia as having the fifth largest number of children under 5 suffering from stunted growth worldwide. Addressing the problem of malnutrition in Indonesia, Aksi Cepat Tanggap (ACT) Foundation, a humanitarian organization working with Carrefour, acts as donor and pursues several solutions to the problem, especially of malnourished children and infants in South Tangerang area, Indonesia. The objective of this study was to examine the community empowerment driven by ACT Foundation in order to maintain the good status continuity of child and toddler after the children malnutrition recovered. Research was conducted using qualitative approach through in-depth interview and observation to find out how the Bengkel Gizi Terpadu (Integrated Nutrion Workshop) programs make people empowered. Bengkel Gizi Terpadu (BGT) is divided into 3 sequences of activities, there were: integrated malnutrition rehabilitation; provision of health education to mothers of infants and young children; and family economic empowerment to head of household. Results showed that after empowerment process has been done through training and provision of knowledge to the mothers and families about the important of nutrition and health, there were 30 of 100 mothers who participated actively. Then, there were 45 of 100 heads of household who participated in business training were able to open a business on their own which provided and controlled by ACT as stakeholder in this program. The further findings revealed that BGT programs are able to form community health workers and provide employment opportunities to community. This study believes that integrated nutrition workshop program is the solution to maintain good nutrition among children in South Tangerang, through empowerment of parents and community members, via education and business training program. Both programs prepared parents with economic sustenance and necessary information, a pre-requisite to end malnutrition in children.

Keywords: community, empowerment, malnutrition, training

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Authors: Chaiwat Waree

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The purposes of the research were to develop online lessons to strengthen the learning process of Master's degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University; to achieve the efficiency criteria of 80/80; and to study the satisfaction of students who use online lessons to strengthen the learning process of Master’s degree students majoring in Curriculum and Instruction at Suan Sunandha Rajabhat University. The sample consisted of 40 University students studying in semester 1, academic year 2012. The sample was determined by Purposive Sampling. Selected students were from the class which the researcher was the homeroom tutor. The tutor was responsible for the teaching of learning process. Tools used in the study were online lessons, 60-point performance test, and evaluation test of satisfaction of students on online lessons. Data analysis yielded the following results; 83.66/88.29 efficiency of online lessons measured against the criteria; the comparison of performance before and after taking online lessons using t-test yielded 29.67. The statistical significance was at 0.05; the average satisfaction level of forty students on online lessons was 4.46 with standard deviation of 0.68.

Keywords: online, lessons, curriculum, instruction

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Dahiru Bawa Muhammad

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Mathematics is accorded a prime position in basic education curriculum because it is envisaged to be an important tool in preparing children for life after school as well as equipping them with skills needed for secondary and higher education. As a result of this, the subject is made compulsory from primary through secondary school and candidates are expected to offer it and pass before fulfilling the requirement for higher education. Against this backdrop, this paper overviewed the basic education programme, context of teaching and learning mathematics at basic education level in Katsina State of Nigeria, relevance of the subject to different fields of human endeavours, challenges threatening the utility of the subject as a tool for the achievement of the goals of basic education programme and concluded by recommending how teaching and learning of mathematics can be improved for even development of citizens within nation states and enhanced/mutual sustainable development of nations in the global village.

Keywords: basic education, junior secondary school education, mathematical centre

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Authors: Jihuai Wu, Zeyu Song, Zhang Lan, Liuxue Sun

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Solar energy is clean, open, and infinite, but solar radiation on the earth is fluctuating, intermittent, and unstable. So, the sustainable utilization of solar energy requires a combination of high-efficient energy conversion and low-loss energy storage technologies. Hence, a photo capacitor integrated with photo-electrical conversion and electric-chemical storage functions in single device is a cost-effective, volume-effective and functional-effective optimal choice. However, owing to the multiple components, multi-dimensional structure and multiple functions in one device, especially the mismatch of the functional modules, the overall conversion and storage efficiency of the photocapacitors is less than 13%, which seriously limits the development of the integrated system of solar conversion and energy storage. To this end, two typical photocapacitors were studied. A three-terminal photocapacitor was integrated by using perovskite solar cell as solar conversion module and symmetrical supercapacitor as energy storage module. A function portfolio management concept was proposed the relationship among various efficiencies during photovoltaic conversion and energy storage process were clarified. By harmonizing the energy matching between conversion and storage modules and seeking the maximum power points coincide and the maximum efficiency points synchronize, the overall efficiency of the photocapacitor surpassed 18 %, and Joule efficiency was closed to 90%. A voltage adjustable hybrid supercapacitor (VAHSC) was designed as energy storage module, and two Si wafers in series as solar conversion module, a three-terminal photocapacitor was fabricated. The VAHSC effectively harmonizes the energy harvest and storage modules, resulting in the current, voltage, power, and energy match between both modules. The optimal photocapacitor achieved an overall efficiency of 15.49% and Joule efficiency of 86.01%, along with excellent charge/discharge cycle stability. In addition, the Joule efficiency (ηJoule) was defined as the energy ratio of discharge/charge of the devices for the first time.

Keywords: joule efficiency, perovskite solar cell, photocapacitor, silicon solar cell, supercapacitor

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Carla Van De Sande, Jana Vandenberg

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Just like physical skills, cognitive skills grow rusty over time unless they are regularly used and practiced, so academic breaks can have negative consequences on student learning and success. The Keeping in School Shape (KiSS) program is an engaging, accessible, and cost-effective intervention that harnesses the benefits of retrieval practice by using technology to help students maintain proficiency over breaks from school by delivering a daily review problem via text message or email. A growth mindset is promoted through feedback messages encouraging students to try again if they get a problem wrong and to take on a challenging problem if they get a problem correct. This paper reports on the relationship between confidence, accuracy, and decision-making during the implementation of the KiSS Program at a large university during winter break for students enrolled in an engineering introductory Calculus course sequence.

Keywords: growth mindset, learning loss, on-the-go learning, retrieval practice

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Authors: Khaled R. Khater

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The paper theme is soil retaining structures. Cantilever secant-pile wall is triggering scientific point of curiosity. Specially the capping beams structural analysis and its interaction with secant piles as one integrated matrix. It is believed that straining actions of this integrated matrix are most probably induced due to a combination of induced line load and non-uniform horizontal pile tips displacement. The strategy that followed throughout this study starts by converting the pile head horizontal displacements generated by Plaxis-2D model to a system of concentrated line load acting per meter run along the capping beam. Then, those line loads are the input data of Staad-Pro 3D-model. Those models tailored to allow the capping beam and the secant piles interacting as one matrix, i.e. a unit. It is believed that the suggested strategy presents close to real structural simulation. The above is the paper thought and methodology. Three sand densities, one pile rigidity and one excavation depth, “h = 4.0-m,” are completely sufficient to achieve the paper’s objective.

Keywords: secant piles, capping beam, analysis, design, plaxis 2D, staad pro 3D

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Authors: Felicia K. Oluwalola

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This paper examines the relevance of technology on education. It identified the concept of technology on education, bringing real-world learning to the classroom situation, examples of where technology can be used. This study established the fact that technology facilitates students learning compared with traditional method of teaching. It was recommended that the teachers should use technology to supplement, not replace, other instructional modes. It should be used in conjunction with hands-on labs and activities that also address the concepts targeted by the technology. Also, technology should be students centered and not teachers centered.

Keywords: computer, simulation, classroom teaching, education

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Authors: Xiao-Yan Li, Takashi Hashimoto, Guanhong Li, Shuo Yang

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In Japanese, onomatopoeia is an important element in the lively expression of feelings and experiences. It is very difficult for students of Japanese to acquire onomatopoeia, especially, its nuances. In this paper, based on traditional L2 learning theories, we propose a new method to improve the efficiency of teaching the nuances – both explicit and tacit - to non-native speakers of Japanese. The method for teaching the tacit nuances of onomatopoeia consists of three elements. First is to teach the formal rules representing the explicit nuances of onomatopoeic words. Second is to have the students create new onomatopoeic words by utilizing those formal rules. The last element is to provide feedback by evaluating the onomatopoeias created. Our previous study used five-grade relative estimation. However students were confused about the five-grade system, because they could not understand the evaluation criteria only based on a figure. In this new system, then, we built an evaluation database through native speakers’ narrative interpretation. We asked Japanese native speakers to describe their awareness of the nuances of onomatopoeia in writing. Then they voted on site and defined priorities for showing to learners on the system. To verify the effectiveness of the proposed method and the learning system, we conducted a preliminary experiment involving two groups of subjects. While Group A got feedback about the appropriateness of their onomatopoeic constructions from the native speakers’ narrative interpretation, Group B got feedback just in the form of the five-grade relative estimation. A questionnaire survey administered to all of the learners clarified our learning system availability and also identified areas that should be improved. Repetitive learning of word-formation rules, creating new onomatopoeias and gaining new awareness from narrative interpretation is the total process used to teach the explicit and tacit nuances of onomatopoeia.

Keywords: onomatopoeia, tacit nuance, narrative interpretation, e-learning system, second language teaching

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Authors: Hui-Yi Wang, Shu-Mei Chen, Mei-Fang Liu

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Background and purpose: Providing clinical experience in medical education is an important discipline method where students can gradually apply their academic knowledge to clinical situations. The purpose of this study was to establish self-assessment questionnaires for students to assess their learning outcomes for two fields of physical therapy, orthopedic physical therapy, and pediatric physical therapy, in a clinical fieldwork course. Methods: The questionnaires were developed based on the core competence dimensions of the course. The content validity of the questionnaires was evaluated and established by expert meetings. Among the third-year undergraduate students who took the clinical fieldwork course, there were 49 students participated in this study. Teachers arranged for the students to study two professional fields, and each professional field conducted a three-week clinical lesson. The students filled out the self-assessment questionnaires before and after each three-week lesson. Results: The self-assessment questionnaires were established by expert meetings that there were six core competency dimensions in each of the two fields, with 20 and 21 item-questions, respectively. After each three-week clinical fieldwork, the self-rating scores in each core competency dimension were higher when compared to those before the course, indicating having better clinical abilities after the lessons. The best self-rating scores were the dimension of attitude and humanistic literacy, and the two lower scores were the dimensions of professional knowledge and skills and problem-solving critical thinking. Conclusions: This study developed questionnaires for clinical fieldwork courses to reflect students' learning outcomes, including the performance of professional knowledge, practice skills, and professional attitudes. The use of self-assessment of learning performance can help students build up their reflective competencies. Teachers can guide students to pay attention to the performance of abilities in each core dimension to enhance the effectiveness of learning through self-reflection and improvement.

Keywords: physical therapy, clinical fieldwork course, learning outcomes assessment, medical education, self-reflection ability

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

Procedia PDF Downloads 109

Authors: Nilgun Karatas, Bihter Sakin

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In this study, the relationships between the use of language learning strategies and English language exit exam success were investigated in the university EFL learners’ context. The study was conducted at Fatih University Prep School. To collect data 3 classes from the A1 module in English language classes completed a questionnaire known as the English Language Learning Strategy Inventory or ELLSI. The data for the present study were collected from the preparatory class students who are studying English as a second language at the School of Foreign Languages. The students were placed into four different levels of English, namely A1, A2, B1, and B2 level of English competency according to European Union Language Proficiency Standard, by means of their English placement test results. The Placement test was conveyed at the beginning of the spring semester in 2014-2015.The ELLSI consists of 30 strategy items which students are asked to rate from 1 (low frequency) to 5 (high frequency) according to how often they use them. The questionnaire and exit exam results were entered onto SPSS and analyzed for mean frequencies and statistical differences. Spearman and Pearson correlation were used in a detailed way. There were no statistically significant results between the frequency of strategy use and exit exam results. However, most questions correlate at a significant level with some of the questions.

Keywords: individual differences, language learning strategies, Fatih University, English language

Procedia PDF Downloads 480

Authors: Heeyoung Lee, Woosung Bin, Kangseog Seo, Hyojeong Yun, Zuog An

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Recently, a new continuous bridge system has been proposed to increase the space under the bridge and to improve aesthetic aspect of the urban area. The main feature of the proposed bridge is to connect steel I-girders and coping by means of prestressed high-strength steel bars and steel plate. The proposed bridge is able to lower the height of the bridge to ensure the workability and efficiency through a reduction of the cost of road construction. This study presents the experimental result of the full-scale connection between steel I-girders and coping under the negative bending moment. The composite behavior is thoroughly examined and discussed under the specific load levels such as service load, factored load and crack load. Structural response showed full composite action until the final load level because no relative displacement between coping and girder was observed. It was also found prestressing force into high-strength bars was able to control tensile stresses of deck slab. This indicated that cracks in deck slab can be controlled by above-mentioned prestressing force.

Keywords: coping, crack, integrated bridge, full-scale test

Procedia PDF Downloads 427

Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

Procedia PDF Downloads 87

Authors: Xi Liu, Fan Yuan

Abstract:

In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

Procedia PDF Downloads 49

Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

Procedia PDF Downloads 69