Search results for: learning behaviour

Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Felix Oromena Egara, Moeketsi Mosia

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The study aimed to evaluate the impact of blended learning on mathematics retention among secondary school students. Conducted in the Isoko North Local Government Area of Delta State, Nigeria, the research involved 1,235 senior class one (SS 1) students. Employing a non-equivalent control group pre-test-post-test quasi-experimental design, a sample of 70 students was selected from two secondary schools with ICT facilities through purposive sampling. Random allocation of students into experimental and control groups was achieved through balloting within each selected school. The investigation included three assessment points: pre-Mathematics Achievement Test (MAT), post-MAT, and post-post-MAT (retention), administered systematically by the researchers. Data collection utilized the established MAT instrument, which demonstrated a high reliability score of 0.86. Statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS) version 28, with mean and standard deviation addressing study questions and analysis of covariance scrutinizing hypotheses at a significance level of .05. Results revealed significantly greater improvements in mathematics retention scores among students exposed to blended learning compared to those instructed through conventional methods. Moreover, noticeable differences in mean retention scores were observed, with male students in the blended learning group exhibiting notably higher performance. Based on these findings, recommendations were made, advocating for mathematics educators to integrate blended learning, particularly in geometry teaching, to enhance students’ retention of mathematical concepts.

Keywords: blended learning, flipped classroom model, secondary school students, station rotation model

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Authors: Rafael Dias Silva

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The school is a social institution that should promote learning situations that remain throughout life. Based on this, the teaching activities promoted in museum spaces can represent an educational strategy that contributes to the learning process in a more meaningful way. This article systematizes a series of elements that guide the use of these spaces for the scientific literacy of deaf students and as experiences of this nature are favorable for the school development through the concept of the circularity. The methodology for the didactic use of these spaces of non-formal education is one of the reflections developed in this study and how such environments can contribute to the learning in the classroom. To develop in the student the idea of ​​association making him create connections with the curricular proposal and notice how the proposed activity is articulated. It is in our interest that the experience lived in the museum be shared collaborating for the construction of a scientific literacy and cultural identity through the research.

Keywords: accessibility in museums, Brazilian sign language, deaf students, teacher training

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Authors: Bilal Arshad, Johan Barthelemy, Elliott Pilton, Pascal Perez

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Getting reliable, informative, and up-to-date information about the location, mobility, and behavioural patterns of animals will enhance our ability to research and preserve biodiversity. The fusion of infra-red sensors and camera traps offers an inexpensive way to collect wildlife data in the form of images. However, extracting useful data from these images, such as the identification and counting of animals remains a manual, time-consuming, and costly process. In this paper, we demonstrate that such information can be automatically retrieved by using state-of-the-art deep learning methods. Another major challenge that ecologists are facing is the recounting of one single animal multiple times due to that animal reappearing in other images taken by the same or other camera traps. Nonetheless, such information can be extremely useful for tracking wildlife and understanding its behaviour. To tackle the multiple count problem, we have designed a meshed network of camera traps, so they can share the captured images along with timestamps, cumulative counts, and dimensions of the animal. The proposed method takes leverage of edge computing to support real-time tracking and monitoring of wildlife. This method has been validated in the field and can be easily extended to other applications focusing on wildlife monitoring and management, where the traditional way of monitoring is expensive and time-consuming.

Keywords: computer vision, ecology, internet of things, invasive species management, wildlife management

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Authors: David Pham, Yongfeng Zhang

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Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

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Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

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Authors: Ju May Wen, Chun Hung Lin, Eric Zhi Feng Liu

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Flow theory implies that people are persuaded by happiness. By focusing on an activity, people turn a blind eye to external factors. This study explores the influence of educational board games and fundamental Chinese language teaching on students’ learning motivation and flow experience. Fifty-three students studying Chinese language fundamental courses were used in the study. These students were divided into three groups: (1) flash card teaching group; (2) educational original board game teaching group; and (3) educational Chinese board game teaching group. Chinese language teaching was integrated with the educational board game titled ‘Transportation GO.’ The students were observed playing this game as the teacher collected quantitative and qualitative data. Quantitative data was collected from the learning motivation scale and flow experience scale. Qualitative data was collected through observing, recording, and visiting. The first result found that the three groups integrated with Chinese language teaching could maintain students’ high learning motivation and high flow experience. Second, there was no significant difference between the flow experience of the flash card group and the educational original board game group. Third, there was a significant difference in the flow experience and learning motivation of the educational Chinese board game group vs. the other groups. This study suggests that the experimental model can be applied to advanced Chinese language teaching. Apart from oral and literacy skills, the study of educational board games integrated with Chinese language teaching to enforce student writing skills will be continued.

Keywords: Chinese language instruction, educational board game, learning motivation, flow experience

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Authors: Caroline Smith, Professor Debi Bhattacharya, Sion Scott

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Introduction: Oropharyngeal Dysphagia (OD) effects around 15% of older people, however it is often unrecognised and under diagnosed until they are hospitalised. There is a need for primary care healthcare practitioners (HCPs) to assume a proactive role in identifying and managing OD to prevent adverse outcomes such as aspiration pneumonia. Understanding the determinants of primary care HCPs undertaking this new behaviour provides the intervention targets for addressing. This realist review, underpinned by the Theoretical Domains Framework (TDF), aims to synthesise relevant literature and develop programme theories to understand what interventions work, how they work and under what circumstances to facilitate HCPs to prevent harm from OD. Combining realist methodology with behavioural science will permit conceptualisation of intervention components as theoretical behavioural constructs, thus informing the design of a future behaviour change intervention. Furthermore, through the TDF’s linkage to a taxonomy of behaviour change techniques, we will identify corresponding behaviour change techniques to include in this intervention. Methods & analysis: We are following the five steps for undertaking a realist review: 1) clarify the scope 2) Literature search 3) appraise and extract data 4) evidence synthesis 5) evaluation. We have searched Medline, Google scholar, PubMed, EMBASE, CINAHL, AMED, Scopus and PsycINFO databases. We are obtaining additional evidence through grey literature, snowball sampling, lateral searching and consulting the stakeholder group. Literature is being screened, evaluated and synthesised in Excel and Nvivo. We will appraise evidence in relation to its relevance and rigour. Data will be extracted and synthesised according to its relation to Initial programme theories (IPTs). IPTs were constructed after the preliminary literature search, informed by the TDF and with input from a stakeholder group of patient and public involvement advisors, general practitioners, speech and language therapists, geriatricians and pharmacists. We will follow the Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) quality and publication standards to report study results. Results: In this ongoing review our search has identified 1417 manuscripts with approximately 20% progressing to full text screening. We inductively generated 10 IPTs that hypothesise practitioners require: the knowledge to spot the signs and symptoms of OD; the skills to provide initial advice and support; and access to resources in their working environment to support them conducting these new behaviours. We mapped the 10 IPTs to 8 TDF domains and then generated a further 12 IPTs deductively using domain definitions to fulfil the remaining 6 TDF domains. Deductively generated IPTs broadened our thinking to consider domains such as ‘Emotion,’ ‘Optimism’ and ‘Social Influence’, e.g. If practitioners perceive that patients, carers and relatives expect initial advice and support, then they will be more likely to provide this, because they will feel obligated to do so. After prioritisation with stakeholders using a modified nominal group technique approach, a maximum of 10 IPTs will progress to test against the literature.

Keywords: behaviour change, deglutition disorders, primary healthcare, realist review

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Authors: Prasanna Ramachandran, Kareem Graham, Helena Kiefel, Sunit Jain, Todd DeSantis

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Microbes that reside inside the mammalian GI tract, commonly referred to as the gut microbiome, have been shown to have therapeutic effects in animal models of disease. We hypothesize that specific proteins produced by these microbes are responsible for this activity and may be used directly as therapeutics. To speed up the discovery of these key proteins from the big-data metagenomics, we have applied machine learning techniques. Using amino acid sequences of known epitopes and their corresponding binding partners, protein interaction descriptors (PID) were calculated, making a positive interaction set. A negative interaction dataset was calculated using sequences of proteins known not to interact with these same binding partners. Using Random Forest and positive and negative PID, a machine learning model was trained and used to predict interacting versus non-interacting proteins. Furthermore, the continuous variable, cosine similarity in the interaction descriptors was used to rank bacterial therapeutic candidates. Laboratory binding assays were conducted to test the candidates for their potential as therapeutics. Results from binding assays reveal the accuracy of the machine learning prediction and are subsequently used to further improve the model.

Keywords: protein-interactions, machine-learning, metagenomics, microbiome

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Authors: Undiyaundeye Florence Atube, Ugar Innocent A.

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Young children are active learners who use all their senses to build concepts and ideas from their experiences. The process of learning, the content and the outcomes, is vital for young children. They need time to explore whether they are satisfied with what is learnt. Of all levels of education, early childhood education is considered to be most critical for the social, emotional, cognitive and physical development. For this reason, the teachers for early years need to play a significant role in the teaching and learning process through the provision of a friendly environment in the school. A case study approach was used in this study. The information was gathered through various methods like class observation, field notes, documents analysis, group processes, and semi structured interviews. The group processes participants and interviewees were taken from some stakeholders such as parents, students, teachers, and head teachers from public schools, to have a broad and comprehensive analysis, informal interaction with different stakeholders and self-reflection was used to clarify aspects of varying issues and findings. The teachers’ roles in developing a child friendly environment in personal capacity to learning were found to improve a pupils learning ability. Prior to early child development education, learning experiences and pedagogical content knowledge played a vital role in engaging teachers in developing their thinking and teaching practice. Children can be helped to develop independent self-control and self-reliance with careful planning and development of the child’s experience with sensitive and appropriate interaction by the educator to propel eagerness to learn through the provision of a friendly environment.

Keywords: child friendly environment, early childhood, education and development, teaching, learning and the curriculum

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Authors: Amira Potter

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Since three years, ESPRIT has adopted project-based learning across its curricula. The philosophy behind this reform is to prepare its future engineers to become more operational once they integrate the workplace. It allows them to learn all the required skills expected from them by their future employers. This learner-centered method helps the students take responsibility for their own learning, solve real-world problems and carry out muli-faceted projects. Therefore, the teacher who used to be considered as the detainer of the knowledge has become more of a facilitator and a coach, encouraging their students’ learning process. This innovative way to English teaching has enabled the students to learn the English language differently. The target language is learnt cooperatively through group work, presentations, debating and many other communicative activities. The speaking skill in English language remains by far the most challenging skill for Tunisian students with an educational background based on Arabic as a first language and French as a second language. The student’s initial resistance to speak English in front of their classmates and the way they end up performing their work, shows the real progress they managed to achieve through PBL approach. The article will focus on the positive impact PBL has had on oral fluency among Esprit engineering students and how it has been achieved. It will also describe how speaking skill is taught and assessed at ESPRIT.

Keywords: cooperative, engineer, innovative, project-based learning

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Authors: Guantao Bai

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Public space is an indispensable and important component of the urban built environment. How to more accurately evaluate the usage characteristics of public space can help improve its spatial quality. Compared to traditional survey methods, computer vision technology based on deep learning has advantages such as dynamic observation and low cost. This study takes the public space of Shaping Park as an example and, based on deep learning computer vision technology, processes and analyzes the image data of the public space to obtain the spatial usage characteristics and spatiotemporal characteristics of the public space. Research has found that the spontaneous activity time in public spaces is relatively random with a relatively short average activity time, while social activities have a relatively stable activity time with a longer average activity time. Computer vision technology based on deep learning can effectively describe the spatial usage characteristics of the research area, making up for the shortcomings of traditional research methods and providing relevant support for creating a good public space.

Keywords: computer vision, deep learning, public spaces, using features

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Authors: Maulik Parmar, Sumeet Sandhu

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The online fashion industry experiences high product return rates. Many returns are because of size/fit mismatches -the size scale on labels can vary across brands, the size parameters may not capture all fit measurements, or the product may have manufacturing defects. Warehouse quality check of garment sizes can be semi-automated to improve speed and accuracy. This paper presents an approach for automatically measuring garment sizes from a single image of the garment -using Deep Learning to learn garment keypoints. The paper focuses on the waist size measurement of jeans and can be easily extended to other garment types and measurements. Experimental results show that this approach can greatly improve the speed and accuracy of today’s manual measurement process.

Keywords: convolutional neural networks, deep learning, distortion, garment measurements, image warping, keypoints

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Authors: Martin Dupuy, Solange Grunenwald, Pierre-Louis Colombo, Laurence Mahieu, Pomone Richard, Philippe Bartoli

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Endonasal endoscopic trans-sphenoidal surgery for pituitary tumours has become a mainstay of treatment over the last two decades. Although it is generally accepted that there is no significant difference between endoscopic versus microscopic approach for surgical outcomes (endocrine and ophthalmologic status), nasal morbidity seems to the benefit of endoscopic procedures. Minimally invasive endoscopic surgery needs an operative learning curve to achieve surgeon’s efficiency. This learning curve is now well known for surgical outcomes and complications rate, however, few data are available for nasal morbidity. The aim of our series is to document operative experience and nasal quality of life after (NQOL) endoscopic trans-sphenoidal surgery. The prospective pituitary surgical cohort consisted of 525 consecutives patients referred to our Skull Base Diseases Department. Endoscopic procedures were performed by a single neurosurgeon using an uninostril approach. NQOL was evaluated using the Sino-Nasal Test (SNOT-22), the Anterior Base Nasal Inventory (ASBNI) and the Skull Base Inventory Score (SBIS). Data were collected before surgery during hospital stay and 3 months after the surgery. The seventy first patients were compared to the latest 70 patients. There was no significant difference between comparison score before versus after surgery for SNOT-22, ASBNI and SBIS during the single surgeon’s learning curve. Our series demonstrates that in our institution there is no statistically significant learning curve for NQOL after uninostril endoscopic pituitary surgery. A careful progression through sinonasal structures with very limited mucosal incision is associated with minimal morbidity and preserves nasal function. Conservative and minimal invasive approach could be achieved early during learning curve.

Keywords: pituitary surgery, quality of life, minimal invasive surgery, learning curve, pituitary tumours, skull base surgery, endoscopic surgery

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Authors: Ashwag O. Maghraby, Nida N. Khan, Hosnia A. Ahmed, Ghufran N. Brohi, Hind F. Assouli, Jawaher S. Melibari

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The Arabic language is one of the most important languages. Learning it is so important for many people around the world because of its religious and economic importance and the real challenge lies in practicing it without grammatical or syntactical mistakes. This research focused on detecting and correcting the syntactic mistakes of Arabic syntax according to their position in the sentence and focused on two of the main syntactical rules in Arabic: Dual and Plural. It analyzes each sentence in the text, using Stanford CoreNLP morphological analyzer and machine-learning approach in order to detect the syntactical mistakes and then correct it. A prototype of the proposed system was implemented and evaluated. It uses support vector machine (SVM) algorithm to detect Arabic grammatical errors and correct them using the rule-based approach. The prototype system has a far accuracy 81%. In general, it shows a set of useful grammatical suggestions that the user may forget about while writing due to lack of familiarity with grammar or as a result of the speed of writing such as alerting the user when using a plural term to indicate one person.

Keywords: Arabic language acquisition and learning, natural language processing, morphological analyzer, part-of-speech

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Authors: H. M. Anitha, Anusha N. Rao

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The current developments in technology and ideas have given entirely new dimensions to the field of research and education. New delivery methods are proposed which is an added feature to the engineering education. Particularly, more importance is given to new teaching practices such as Information and Communication Technologies (ICT). It is vital to adopt the new ICT methods which lead to the emergence of novel structure and mode of education. The flipped classroom, think pair share and peer instruction are the latest pedagogical methods which give students to learn the course. This involves students to watch video lectures outside the classroom and solve the problems at home. Students are engaged in group discussions in the classroom. These are the active learning methods wherein the students are involved diversely to learn the course. This paper gives a comprehensive study of past and present research which is going on with flipped classroom, thinks pair share activity and peer instruction.

Keywords: flipped classroom, think pair share, peer instruction, active learning

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Authors: Mohammed Abubakar Mawoli

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Theory of Planned Behavior (TPB) is a useful tool for predicting entrepreneurial intentions among individuals or groups of people. In view of the Nigerian government’s renewed educational policies and programs to prepare Nigerian undergraduates towards self-reliance and employers of labor after graduation, it becomes pertinent to empirically examine and predict the undergraduate’s entrepreneurial intentions at graduation. Thus, this study primarily examines the undergraduates entrepreneurial intentions using TPB, which includes perceived desirability, perceived social norm, and perceived feasibility factors. In so doing, a questionnaire research method was adopted in which 219 copies of a questionnaire distributed to final year undergraduates were belonging to five departments with a total population of 487 students. A combination of relative frequency, mean standard deviation and multiple regression statistical tools were employed for data analysis. The study found that TPB components exert a significant composite effect on undergraduate’s entrepreneurial intentions. Based on individual contribution of the independent variables, Perceived Desirability is the strongest predictor of the undergraduate’s entrepreneurial intentions, while Perceived Social Norm is a strong predictor of the undergraduate’s entrepreneurial intentions. However, Perceived Feasibility is not a strong predictor of student’s entrepreneurial intentions. The study therefore, recommends that the Perceived desirability, which is formed and shaped by ones level of education and skills acquisition, be improved upon to create the expected positive impact on graduates entrepreneurial intentions and possible venture creation.

Keywords: entrepreneurship, entrepreneurship education, entrepreneurial intentions, planned behaviour, prediction, Nigeria

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Authors: Kumar Vikas

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Education is to be anticipated for Human resource development and then national development. However, in most of the developing countries, due to the inadequacy of resources it is almost unattainable to educate all of their citizens through on-campus teaching. Huge amount of money is necessary to establish the infrastructure for on-campus teaching which is out of the reach of the developing countries. In these circumstances, to educate their huge inhabitants the developing countries are to depend on open learning and distance education system. However, a question still stands: can the educators dissimulate knowledge to the learners smoothly through this new system of education? Some recent research shows that the graduates of the open and distance learning institutions in the developing countries are treated as second-grade graduates. This paper aims to identify the challenges or barriers in the development of distance and Open learning system in India and suggest possible alternatives may be followed to overcome the barriers.

Keywords: barriers, distance education, developing countries, motivation, alternative solutions

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Authors: Sandra Thompson, Paul Dervan

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Ireland’s first Technological University (TU Dublin) was established on 1st January 2019, and its creation is an exciting new milestone in Irish Higher Education. TU Dublin is now Ireland’s biggest University supporting 29,000 students across three campuses with 3,500 staff. The University aspires to create work-ready graduates who are socially responsible, open-minded global thinkers who are ambitious to change the world for the better. As graduates, they will be enterprising and daring in all their endeavors, ready to play their part in transforming the future. Feedback from Irish employers and students coupled with evidence from other authoritative sources such as the World Economic Forum points to a need for greater focus on the development of students’ employability skills as they prepare for today’s work environment. Moreover, with an increased focus on Universal Design for Learning (UDL) and inclusiveness, there is recognition that students are more than a numeric grade value. Robust grading systems have been developed to track a student’s performance around discipline knowledge but there is little or no global consensus on a definition of transversal skills nor on a unified framework to assess transversal skills. Education and industry sectors are often assessing one or two skills, and some are developing their own frameworks to capture the learner’s achievement in this area. Technological University Dublin (TU Dublin) have discovered and implemented a framework to allow students to develop, assess and record their transversal skills using transformative learning theory. The model implemented is an adaptation of Student Transformative Learning Record - STLR which originated in the University of Central Oklahoma (UCO). The purpose of this paper therefore, is to examine the views of students, staff and employers in the context of deploying a Transformative Learning model within the University to assess transversal skills. It will examine the initial impact the transformative learning model is having socially, personally and on the University as an organization. Crucially also, to identify lessons learned from the deployment in order to assist other Universities and Higher Education Institutes who may be considering a focused adoption of Transformative Learning to meet the challenge of preparing students for today’s work environment.

Keywords: assessing transversal skills, higher education, transformative learning, students

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Authors: Bashaiar Alsanaa

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Never has a revolution influenced all aspects of humanity as the communication revolution during the past two decades. This revolution, with all its advances and utilities, swept the world thus becoming an integral part of our lives, hence giving way to emerging applications at the social, economic, political, and educational levels. More specifically, such applications have changed the delivery system through which learning is acquired by students. Interaction with educators, accessibility to content, and creative delivery options are but a few facets of the new learning experience now being offered through the use of technology in the educational field. With different success rates, third world countries have tried to pace themselves with use of educational technology in advanced parts of the world. One such country is the small rich-oil state of Kuwait which has tried to adopt the e-educational model, however, an evaluation of such trial is yet to be done. This study aims to fill the void of research conducted around that topic. The study explores teachers’ acceptance of incorporating communication technologies in higher education in Kuwait. Teachers’ responses to survey questions present an overview of the e-learning experience in this country, and draw a framework through which implications and suggestions for future research can be discussed to better serve the advancement of e-education in developing countries.

Keywords: communication technologies, E-learning, Kuwait, social media

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Authors: Bashaiar Alsana

Abstract:

Never has a revolution influenced all aspects of humanity as the communication revolution during the past two decades. This revolution, with all its advances and utilities, swept the world thus becoming an integral part of our lives, hence giving way to emerging applications at the social, economic, political, and educational levels. More specifically, such applications have changed the delivery system through which learning is acquired by students. Interaction with educators, accessibility to content, and creative delivery options are but a few facets of the new learning experience now being offered through the use of technology in the educational field. With different success rates, third world countries have tried to pace themselves with use of educational technology in advanced parts of the world. One such country is the small rich-oil state of Kuwait which has tried to adopt the e-educational model, however, an evaluation of such trial is yet to be done. This study aims to fill the void of research conducted around that topic. The study explores teachers’ acceptance of incorporating communication technologies in higher education in Kuwait. Teachers’ responses to survey questions present an overview of the e-learning experience in this country, and draw a framework through which implications and suggestions for future research can be discussed to better serve the advancement of e-education in developing countries.

Keywords: communication technologies, e-learning, Kuwait, social media

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Authors: Siti Ayu Ningsih

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This study aims to find the differences outcomes in learning reading comprehension with text and film as media on Indonesian Language for foreign speaker (BIPA) learning at intermediate level. By using quantitative and qualitative research methods, the respondent of this study is a single respondent from D'Royal Morocco Integrative Islamic School in grade nine from secondary level. Quantitative method used to calculate the learning outcomes that have been given the appropriate action cycle, whereas qualitative method used to translate the findings derived from quantitative methods to be described. The technique used in this study is the observation techniques and testing work. Based on the research, it is known that the use of the text media is more effective than the film for intermediate level of Indonesian Language for foreign speaker learner. This is because, when using film the learner does not have enough time to take note the difficult vocabulary and don't have enough time to look for the meaning of the vocabulary from the dictionary. While the use of media texts shows the better effectiveness because it does not require additional time to take note the difficult words. For the words that are difficult or strange, the learner can immediately find its meaning from the dictionary. The presence of the text is also very helpful for Indonesian Language for foreign speaker learner to find the answers according to the questions more easily. By matching the vocabulary of the question into the text references.

Keywords: Indonesian language for foreign speaker, learning outcome, media, reading comprehension

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Authors: Kamélia Bouderbala, Hichem Nouira, Etienne Videcoq, Manuel Girault, Daniel Petit

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Thermal drifts caused by the power dissipated by the mechanical guiding systems constitute the main limit to enhance the accuracy of an ultra-high precision cylindricity measuring machine. For this reason, a high precision compact prototype has been designed to simulate the behaviour of the instrument. It ensures in situ calibration of four capacitive displacement probes by comparison with four laser interferometers. The set-up includes three heating wires for simulating the powers dissipated by the mechanical guiding systems, four additional heating wires located between each laser interferometer head and its respective holder, 19 Platinum resistance thermometers (Pt100) to observe the temperature evolution inside the set-up and four Pt100 sensors to monitor the ambient temperature. Both a Reduced Model (RM), based on the Modal Identification Method (MIM) was developed and optimized by comparison with the experimental results. Thereafter, time dependent tests were performed under several conditions to measure the temperature variation at 19 fixed positions in the system and compared to the calculated RM results. The RM results show good agreement with experiment and reproduce as well the temperature variations, revealing the importance of the RM proposed for the evaluation of the thermal behaviour of the system.

Keywords: modal identification method (MIM), thermal behavior and drift, dimensional metrology, measurement

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Authors: P. K. Purohit, Azad A. Mansoori, Parvaiz A. Khan, Purushottam Bhawre, Sharad C. Tripathi, A. M. Aslam, Malik A. Waheed, Shivangi Bhardwaj, A. K. Gwal

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The geomagnetic storm represents the most outstanding example of solar wind-magnetospheric interaction, which causes global disturbances in the geomagnetic field as well as the trigger ionospheric disturbances. We study the behaviour of ionospheric Total Electron Content (TEC) during the geomagnetic storms. For the present investigation we have selected 47 intense geomagnetic storms (Dst ≤ -100nT) that were observed during the solar cycle 23 i.e. during 1998-2006. We then categorized these storms into four categories depending upon their solar sources like Magnetic Cloud (MC), Co-rotating Interaction Region (CIR), SH+ICME and SH+MC. We then studied the behaviour of ionospheric TEC at a mid latitude station Usuda (36.13N, 138.36E), Japan during these storm events produced by four different solar sources. During our study we found that the smooth variations in TEC are replaced by rapid fluctuations and the value of TEC is strongly enhanced during the time of these storms belonging to all the four categories. However, the greatest enhancements in TEC are produced during those geomagnetic storms which are either caused by sheath driven magnetic cloud (SH+MC) or sheath driven ICME (SH+ICME). We also derived the correlation between the TEC enhancements produced during storms of each category with the minimum Dst. We found the strongest correlation exists for the SH+ICME category followed by SH+MC, MC and finally CIR. Since the most intense storms were either caused by SH+ICME or SH+MC while the least intense storms were caused by CIR, consequently the correlation was the strongest with SH+ICME and SH+MC and least with CIR.

Keywords: GPS, TEC, geomagnetic storm, sheath driven magnetic cloud

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Authors: Chia Wei Lim, Ning Yan

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The subject of reactor design, dealing with the transformation of chemical feedstocks into more valuable products, constitutes the central idea of chemical engineering. Despite its importance, the way it is taught to chemical engineering undergraduates has stayed virtually the same over the past several decades, even as the chemical industry increasingly leans towards the use of software for the design and daily monitoring of chemical plants. As such, there has been a widening learning gap as chemical engineering graduates transition from university to the industry since they are not exposed to effective platforms that relate the fundamental concepts taught during lectures to industrial applications. While the success of technology enhanced learning (TEL) has been demonstrated in various chemical engineering subjects, TELs in the teaching of reactor design appears to focus on the simulation of reactor processes, as opposed to arguably more important ideas such as the selection and optimization of reactor configuration for different types of reactions. This presents an opportunity for us to utilize the readily available easy-to-use MATLAB App platform to create an educational tool to aid the learning of fundamental concepts of reactor design and to link these concepts to the industrial context. Here, interactive software for the learning of reactor design has been developed to narrow the learning gap experienced by chemical engineering undergraduates. Dubbed the ReactorDesign App, it enables students to design reactors involving complex design equations for industrial applications without being overly focused on the tedious mathematical steps. With the aid of extensive visualization features, the concepts covered during lectures are explicitly utilized, allowing students to understand how these fundamental concepts are applied in the industrial context and equipping them for their careers. In addition, the software leverages the easily accessible MATLAB App platform to encourage self-directed learning. It is useful for reinforcing concepts taught, complementing homework assignments, and aiding exam revision. Accordingly, students are able to identify any lapses in understanding and clarify them accordingly. In terms of the topics covered, the app incorporates the design of different types of isothermal and non-isothermal reactors, in line with the lecture content and industrial relevance. The main features include the design of single reactors, such as batch reactors (BR), continuously stirred tank reactors (CSTR), plug flow reactors (PFR), and recycle reactors (RR), as well as multiple reactors consisting of any combination of ideal reactors. A version of the app, together with some guiding questions to aid explorative learning, was released to the undergraduates taking the reactor design module. A survey was conducted to assess its effectiveness, and an overwhelmingly positive response was received, with 89% of the respondents agreeing or strongly agreeing that the app has “helped [them] with understanding the unit” and 87% of the respondents agreeing or strongly agreeing that the app “offers learning flexibility”, compared to the conventional lecture-tutorial learning framework. In conclusion, the interactive ReactorDesign App has been developed to encourage self-directed explorative learning of the subject and demonstrate the industrial applications of the taught design concepts.

Keywords: explorative learning, reactor design, self-directed learning, technology enhanced learning

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Authors: Diego Aguirre, Olac Fuentes

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In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques.

Keywords: deep learning, image classification, supervised learning, weight initialization

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Authors: Nitza Davidovitch, Yael Yossel-Eisenbach

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The purpose of the study was to inquire whether there was a change in the shaping of undergraduate students’ digitally-oriented study pattern in the pre-Covid (2016-2017) versus post-Covid period (2022-2023), as affected by three factors: social background characteristics, high school, and academic background characteristics. These two-time points were cauterized by dramatic changes in teaching and learning at institutions of higher education. The data were collected via cross-sectional surveys at two-time points, in the 2016-2017 academic school year (N=443) and in the 2022-2023 school year (N=326). The questionnaire was distributed on social media and it includes questions on demographic background characteristics, previous studies in high school and present academic studies, and questions on learning and reading habits. Method of analysis: A. Statistical descriptive analysis, B. Mean comparison tests were conducted to analyze the variations in the mean score for the digitally-oriented learning pattern variable at two-time points (pre- and post-Covid) in relation to each of the independent variables. C. Analysis of variance was performed to test the main effects and the interactions. D. Applying linear regression, the research aimed to examine the combined effect of the independent variables on shaping students' digitally-oriented learning habits. The analysis includes four models. In all four models, the dependent variable is students’ perception of digitally oriented learning. The first model included social background variables; the second model included scholastic background as well. In the third model, the academic background variables were added, and the fourth model includes all the independent variables together with the variable of period (pre- and post-COVID). E. Factor analysis confirms using the principal component method with varimax rotation; the variables were constructed by a weighted mean of all the relevant statements merged to form a single variable denoting a shared content world. The research findings indicate a significant rise in students’ perceptions of digitally-oriented learning in the post-COVID period. From a gender perspective, the impact of COVID on shaping a digital learning pattern was much more significant for female students. The socioeconomic status perspective is eliminated when controlling for the period, and the student’s job is affected - more than all other variables. It may be assumed that the student’s work pattern mediates effects related to the convenience offered by digital learning regarding distance and time. The significant effect of scholastic background on shaping students’ digital learning patterns remained stable, even when controlling for all explanatory variables. The advantage that universities had over colleges in shaping a digital learning pattern in the pre-COVID period dissipated. Therefore, it can be said that after COVID, there was a change in how colleges shape students’ digital learning patterns in such a way that no institutional differences are evident with regard to shaping the digital learning pattern. The study shows that period has a significant independent effect on shaping students’ digital learning patterns when controlling for the explanatory variables.

Keywords: learning pattern, COVID, socioeconomic status, digital learning

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Authors: Sherrene Bogle, Verlia Bogle, Tyrone Anderson

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In recent years, organizations have become increasingly interested in the possibility of analyzing social media as a means of gaining meaningful feedback about their products and services. The aspect based sentiment analysis approach is used to predict the sentiment for Twitter datasets for Digicel and Lime, the main mobile companies in Jamaica, using supervised learning classification techniques. The results indicate an average of 82.2 percent accuracy in classifying tweets when comparing three separate classification algorithms against the purported baseline of 70 percent and an average root mean squared error of 0.31. These results indicate that the analysis of sentiment on social media in order to gain customer feedback can be a viable solution for mobile companies looking to improve business performance.

Keywords: machine learning, sentiment analysis, social media, supervised learning

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Authors: Prashant Verma

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Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

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Authors: Robin Ferguson

Abstract:

The context which this paper explores is the provision of Technical Vocational Education and Training (TVET) of southern African workers at sea on local and foreign vessels using a blended learning approach. The pedagogical challenge of providing quality education in this context is that multiple African and foreign languages and cultural norms are found amongst the all-male crew; and there are widely differing levels of education, low levels of digital literacy and limited connectivity. The methodology used is a nested case study. The study describes the mechanisms used to provide ongoing, real-time workplace TVET on two foreign vessels. Some training was done in person when the vessels came into port, however, the majority of the TVET was achieved from shore to ship using a combination of commonly available Google and Microsoft Apps and WhatsApp. Voice, video and text in multiple languages were used to accommodate different learning styles. The learning was supported by the development of learning networks using social media. This paper also reflects on the shore-based organisational change processes required to support sea learning. The conceptual framework used is the Theory of Practice Architectures (TPA) as is provides a site-ontological perspective of the sayings/thinkings, doings and relatings of this workplace training which is multiplanar as it plays out at sea and ashore, in-person and on-line. Using TPA, the overarching practice architectures and supporting structures which confound or enable these learning practices are revealed. The contribution which this paper makes is an insight into an innovative vocational pedagogy which promotes ICT-mediated learning amongst workers who suffer from low levels of literacies and limited ICT-access and who work and live in remote places. It is a pedagogy of possibility which crosses the digital divide.

Keywords: theory of practice architecture, microsoft, google, whatsapp, vocational pedagogy, mariners, distributed workplaces

Procedia PDF Downloads 72