Search results for: learning technology

Authors: Narinee Sophatsathit, Yuwadee Nitutorn

Abstract:

The purpose of this study was to investigate the attitudes of the staff in the Faculty of Science and Technology towards the e-office documentary system of the Faculty of Science and Technology, Suan Sunandha Rajabhat University. The subjects of this study included 98 staffs of the Faculty of Science and Technology, Suan Sunandha Rajabhat University. Questionnaires were used to ask the attitude of the staffs towards the implementation of e-office system. The results showed that most of the users of e-office are female with the age between 31-40 years old, master degree of education and in the academic positions. They have working experiences between 1-5 years and reported the time of using e-office between 8:30-12:30 during the weekday with the frequency of 3-5 times/day. Most of them reported their opinions on the e-office at high level (x=3.84) and problems and obstacles in using e-office at high level, as well (x=3.63).

Keywords: attitudes, e-office, staff, documentary system

Procedia PDF Downloads 265

Authors: Hasna Mekkaoui Alaoui, Mariem Mekkaoui Alaoui

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In a context marked by a growing and pressing demand for online training within Moroccan universities, massive open online courses (Moocs) are undergoing constant evolution, amplified by the widespread use of digital technology and accentuated by the Coronavirus pandemic. However, despite their growing popularity and expansion, these courses are still lacking in terms of tools, enabling teachers and researchers to carry out a fine-grained analysis of the learning processes taking place within them. What's more, the circulation and sharing of knowledge within these environments is becoming increasingly important. The crucial aspect of traceability emerges here, as MOOCs record and generate traces from the most minute to the most visible. This leads us to consider traceability as a valuable approach in the field of educational research, where the trace is envisaged as a research tool in its own right. In this exploratory research project, we are looking at aspects of community knowledge sharing based on traces observed in the "Communication des organisations" Mooc. Focusing in particular on the mediating trace and its impact in identifying knowledge circulation processes in this learning space, we have mobilized the traces of video capsules as an index of knowledge circulation in the Mooc device. Our study uses a methodological approach based on thematic analysis, and although the results show that learners reproduce knowledge from different video vignettes in almost identical ways, they do not limit themselves to the knowledge provided to them. This research offers concrete perspectives for improving the dynamics of online devices, with a potentially positive impact on the quality of online university teaching.

Keywords: circulation, index, digital environments, mediation., trace

Procedia PDF Downloads 64

Authors: Vishnu Pratap Singh Kirar

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Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm.

Keywords: neural network, backpropagation, local minima, fast convergence rate

Procedia PDF Downloads 498

Authors: Yoones Tavoosy

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This present study concentrates on the organizational implication the Iranian students with learning difficulties (LD) experience when they write an English essay. Particularly, the present study aims at exploring students' structural problems in EFL essay writing. A mixed method research design was employed including a questionnaire and a semi-structured in-depth interview. Technical Data Analysis of findings exposed that students experience a number of difficulties in the structure of EFL essay writing. Discussion and implications of these findings are presented respectively.

Keywords: Iranian students, learning difficulties, organizational implication, writing

Procedia PDF Downloads 222

Authors: Melvin Chan, Chew Lee Teo

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There has been a growing emphasis in elevating the teachers’ proficiency and competencies through continuing professional development (CPD) opportunities. In this era of a Volatile, Uncertain, Complex, Ambiguous (VUCA) world, teachers are expected to be collaborative designers, critical thinkers and creative builders. However, many of the CPD structures are still revolving in the model of transmission, which stands in contradiction to the cultivation of future-ready teachers for the innovative world of emerging technologies. This article puts forward the framing of CPD through a Principle-Based, Technological-Driven Knowledge Building Approach grounded in the essence of andragogy and progressive learning theories where growth is best exemplified through an authentic immersion in a social/community experience-based setting. Putting this Knowledge Building Professional Development Model (KBPDM) in operation via a Professional Learning Team (PLT) situated in a Secondary School in Singapore, research findings reveal that the intervention has led to a fundamental change in the learning paradigm of the teachers, henceforth equipping and empowering them successfully in their pedagogical design and practices for a 21^st century classroom experience. This article concludes with the possibility in leveraging the Learning Analytics to deepen the CPD experiences for educators.

Keywords: continual professional development, knowledge building, learning paradigm, principle-based

Procedia PDF Downloads 130

Authors: Donald C. Lantu, Henndy Ginting, M. Yorga Permana, Dany M. A. Ramdlany

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We constructed a training program for top-talents of a Bank with Scharmer Theory-U as the model. In this training program, we implemented the action learning perspective, as it is claimed to be the most effective one currently available. In the process, participants were encouraged to be more involved, especially compared to traditional lecturing. The goal of this study is to assess the effectiveness of this particular training. The program consists of six days non-residential workshop within two months. Between each workshop, the participants were involved in the works of action learning group. They were challenged by dealing with the real problem related to their tasks at work. The participants of the program were 30 best talents who were chosen according to their yearly performance. Using paired difference statistical test in the behavioral assessment, we found that the training was not effective to increase participants’ leadership competencies. For the future development program, we suggested to modify the goals of the program toward the next stage of development.

Keywords: action learning, behavior, leadership development, Theory-U

Procedia PDF Downloads 195

Authors: Hiba Mezaache

Abstract:

The current study aimed to know the impact of using information and communication technology on the process of re-engineering small and medium enterprises, as the world witnessed the speed development of the latter in its field of work and the diversity of its objectives and programs, that also made its process important for the growth and development of the institution and also gaining the flexibility to face the changes that may occur in the environment of work, so in order to know the impact of information and communication technology on the success of this process, we prepared an electronic questionnaire that included (70) items, and we also used the SPSS statistical calendar to analyze the data obtained. In the end of our study, our conclusion was that there was a positive correlation between the four dimensions of information and communication technology, i.e., hardware and equipment, software, communication networks, databases, and the re-engineering process, in addition to the fact that the studied institutions attach great importance to formal communication, for its positive advantages that it achieves in reducing time and effort and costs in performing the business. We could also say that communication technology contributes to the process of formulating objectives related to the re-engineering strategy. Finally, we recommend the necessity of empowering workers to use information technology and communication more in enterprises, and to integrate them more into the activity of the enterprise by involving them in the decision-making process, and also to keep pace with the development in the field of software, hardware, and technological equipment.

Keywords: information and communication technology, re-engineering, small and medium enterprises, the impact

Procedia PDF Downloads 179

Authors: A. Ishag, E. Witruk, C. Altmayer

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Motivation and attitudes are considered as hypothetical psychological constructs in explaining the process of second language learning. Gardner (1985) – who first systematically investigated the motivational factors in second language acquisition – found that L2 achievement is related not only to the individual learner’s linguistic aptitude or general intelligence but also to the learner’s motivation and interest in learning the target language. Traditionally language learning motivation can be divided into two types: integrative motivation – the desire to integrate oneself with the target culture; and instrumental motivation – the desire to learn a language in order to meet a specific language requirement such as for employment. One of the Gardner’s main ideas is that the integrative motivation plays an important role in second language acquisition. It is directly and positively related to second language achievement more than instrumental motivation. However, the significance of integrative motivation reflects a rather controversial set of findings. On the other hand, Students’ attitudes towards the target language, its speakers and the learning context may all play some part in explaining their success in learning a language. Accordingly, the present study aims at exploring the significance of motivational and attitudinal factors in learning foreign languages, namely English and German among Sudanese undergraduate students from a psycholinguistic and interdisciplinary perspective. The sample composed of 221 students from the English and German language departments respectively at the University of Khartoum in Sudan. The results indicate that English language’s learners are instrumentally motivated and that German language’s learners have positive attitudes towards the German language community and culture. Furthermore, there are statistical significant differences in the attitudes toward the two languages due to gender; where female students have more positive attitudes than their male counterparts. However, there are no differences along the variables of academic grade and study level. Finally, the reasons of studying the English or German language have also been indicated.

Keywords: motivation and attitudes, foreign language learning, english language, german language

Procedia PDF Downloads 683

Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

Procedia PDF Downloads 429

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 80

Authors: Olabisi Adedigba

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It is a fundamental truth that Information Communication Technology (ICT) lies at the very heart of our today’s society and determines its development. The use of ICT has given a boost to the educational and mental development of an average pupil of this age far above their counterparts who lived centuries ago. Nevertheless, the present age children stand the risk of the scourge of this technology if proactive measures are not taken urgently to arrest the damages of its negative use on them. One of the measures that can be taken is supervision of children’s use of ICT. This research therefore investigated parents’ views and supervision of their children’s use of Information Communication Technology. Descriptive design was adopted for this study. 300 parents were randomly selected. “Parents’ Views and Supervision of Children’s Use of ICT” was used to collect data for the study. Data collected were analyzed using percentage, mean, standard deviation and t-test. The result revealed that parents’ view of their children’s use of ICT is negative while supervision of their children’s use of ICT is low. Recommendations were thus offered that schools and other stakeholders should educate parents on children’s proper utilization of ICT and parents are urged to maintain adequate supervision on their children use of ICT.

Keywords: appraisal of parents’ views and supervision, children’s use, information communication technology, t-test

Procedia PDF Downloads 510

Authors: Vânia Graça, Paula Quadros-Flores, Altina Ramos

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The use of Digital Technologies in teaching and learning processes is currently a reality, namely in initial teacher training. This study aims at knowing the digital reality of students in initial teacher training in order to improve training in the educational use of ICT and to promote digital technology integration strategies in an educational context. It is part of the IFITIC Project "Innovate with ICT in Initial Teacher Training to Promote Methodological Renewal in Pre-school Education and in the 1st and 2nd Basic Education Cycle" which involves the School of Education, Polytechnic of Porto and Institute of Education, University of Minho. The Project aims at rethinking educational practice with ICT in the initial training of future teachers in order to promote methodological innovation in Pre-school Education and in the 1st and 2nd Cycles of Basic Education. A qualitative methodology was used, in which a questionnaire survey was applied to teachers in initial training. For data analysis, the techniques of content analysis with the support of NVivo software were used. The results point to the following aspects: a) future teachers recognize that they have more technical knowledge about ICT than pedagogical knowledge. This result makes sense if we consider the objective of Basic Education, so that the gaps can be filled in the Master's Course by students who wish to follow the teaching; b) the respondents are aware that the integration of digital resources contributes positively to students' learning and to the life of children and young people, which also promotes preparation in life; c) to be a teacher in the digital age there is a need for the development of digital literacy, lifelong learning and the adoption of new ways of teaching how to learn. Thus, this study aims to contribute to a reflection on the teaching profession in the digital age.

Keywords: digital technologies, initial teacher training, pedagogical use of ICT, skills

Procedia PDF Downloads 122

Authors: Chen-Ya Juan

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Problem-Based Learning uses vague questions to guide student thinking and enhance their self-learning and collaboration. Most teachers implement PBL in a physical classroom, where teachers can monitor and evaluate students’ learning progress and guide them to search resources for answers. However, the prevalence of the Covid-19 in the world had changed from physical teaching to distance teaching. This instruction used many cases and applied Problem-Based Learning combined on the distance teaching via the internet for college students. This study involved an experimental group with PBL and a control group without PBL. The teacher divided all students in PBL class into eight groups, and 7~8 students in each group. The teacher assigned different cases for each group of the PBL class. Three stages of instruction were developed, including background knowledge of Learning, case analysis, and solving problems for each case. This study used a quantitative research method, a two-sample t-test, to find a significant difference in groups with PBL and without PBL. Findings indicated that PBL incased the average score of special education knowledge. The average score was improved by 20.46% in the PBL group and 15.4% without PBL. Results didn’t show significant differences (0.589>0.05) in special education professional knowledge. However, the feedback of the PBL students implied learning more about the application, problem-solving skills, and critical thinking. PBL students were more likely to apply professional knowledge on the actual case, find questions, resources, and answers. Most of them understood the importance of collaboration, working as a team, and communicating with other team members. The suggestions of this study included that (a) different web-based teaching instruments influenced student’s Learning; (b) it is difficult to monitor online PBL progress; (c) online PBL should be implemented flexible and multi-oriented; (d) although PBL did not show a significant difference on the group with PBL and without PBL, it did increase student’s problem-solving skills and critical thinking.

Keywords: problem-based learning, college students, distance learning, case analysis, problem-solving

Procedia PDF Downloads 130

Authors: Radhika Ranjan Roy

Abstract:

Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

Procedia PDF Downloads 78

Authors: Luis F. Recalde, Daniela A. Bastidas, Dayana E. Gallegos, Patricia N. Constante, Victor H. Andaluz

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This article presents a non-immersive virtual lab-oratory to emulate the behavior of the Mitsubishi Melfa RV 2SDB robotic arm, allowing students and users to acquire skills and experience related to real robots, augmenting the access and learning of robotics in Universidad de las Fuerzas Armadas (ESPE). It was developed using the mathematical model of the robotic arm, thus defining the parameters for virtual recreation. The environment, interaction, and behavior of the robotic arm were developed in a graphic engine (Unity3D) to emulate learning tasks such as in a robotics laboratory. In the virtual system, four inputs were developed for the movement of the robot arm; further, to program the robot, a user interface was created where the user selects the trajectory such as point to point, line, arc, or circle. Finally, the hypothesis of the industrial robotic learning process is validated through the level of knowledge acquired after using the system.

Keywords: virtual learning, robot arm, non-immersive reality, mathematical model

Procedia PDF Downloads 100

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 70

Authors: Thuy T. T. Tran, Hamish Coates, Sophie Arkoudis

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Evaluation instruments of teaching are abundant; however, these do not prompt any enhancement in the quality of teaching, not least because these instruments are framed only by teacher-centered conceptions of teaching. There is a need for more sophisticated teaching evaluation measures that focus on student learning and multi-stakeholder involvement. This study aims to develop such an evaluation instrument for Vietnamese higher education. The study uses several kinds of methods. The instrument was initially drafted through in-depth review of research, paying close attention to Vietnamese higher education. Draft evaluation instruments were produced and reviewed by 34 experts. The outcomes of this qualitative and quantitative data reveal an instrument that highlights the value of a multisource student-centered approach, and the rich integration of contextual and cultural traits where Confucian values are emphasized. The validation affirms that evaluating teaching in such way will facilitate the continuous learning growth of all stakeholders involved.

Keywords: multi stakeholders, quality teaching, student learning, teaching evaluation

Procedia PDF Downloads 310

Authors: Niall J. English

Abstract:

Under- and post-graduate training can benefit from a more active learning style, and most particularly so in engineering. Despite this, outreach to young children in primary and secondary schools is less-developed in terms of its documented effectiveness, especially given new emphasis placed within the third level and advanced research program’s on Education and Public Engagement (EPE). Bearing this in mind, outreach and school visits form the basis to ascertain how active learning, careers stimulus and EPE initiatives for young children can inform the university sector, helping to improve future engineering-teaching standards, and enhancing both quality and practicalities of the teaching-and-learning experience. Indeed, engineering-education EPE/outreach work has been demonstrated to lead to several tangible benefits and improved outcomes, such as greater engagement and interest with science/engineering for school-children, careers awareness, enabling teachers with strong contributions to technical knowledge of engineering subjects, and providing development of general professional skills for engineering, e.g., communication and teamwork. This intervention involved active learning in ‘buzz’ groups for young children of concepts in gas engineering, observing their peer interactions to develop university-level lessons on activity learning. In addition, at the secondary level, careers-outreach efforts have led to statistical determinations of motivations towards engineering education and training, which aids in the redesign of engineering curricula for more active learning.

Keywords: outreach, education and public engagement, careers, peer interactions

Procedia PDF Downloads 120

Authors: Uvais Qidwai, Amor Moursi, Mohamed Tahar, Malek Hamad, Hamad Alansi

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This paper focuses on the development and implementation of an advanced plant health monitoring system with an AI backbone and IoT sensory network. Our approach involves addressing the critical environmental factors essential for preserving a plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, pH, water levels, and humidity, as well as the presence of essential nutrients like nitrogen, phosphorus, and potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. The captured data is then compared against a reference database containing different health statuses. This comparative analysis is implemented using an AI deep learning model, which enables us to generate accurate assessments of plant health status. By combining the AI-based decision-making approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.

Keywords: deep learning image model, IoT sensing, cloud-based analysis, remote monitoring app, computer vision, fuzzy control

Procedia PDF Downloads 54

Authors: M. Duran Toksari, Berrin Ucarkus

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In this paper, we propose two algorithms to optimally solve makespan and total completion time scheduling problems with learning effect and job dependent delivery times in a single machine environment. The delivery time is the extra time to eliminate adverse effect between the main processing and delivery to the customer. In this paper, we introduce the job dependent delivery times for some single machine scheduling problems with position dependent learning effect, which are makespan are total completion. The results with respect to two algorithms proposed for solving of the each problem are compared with LINGO solutions for 50-jobs, 100-jobs and 150-jobs problems. The proposed algorithms can find the same results in shorter time.

Keywords: delivery Times, learning effect, makespan, scheduling, total completion time

Procedia PDF Downloads 469

Authors: Peter Clutterbuck, Terry Rowlands, Owen Seamons

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In this paper we describe one critical research program within a complex, ongoing multi-year project (2010 to 2014 inclusive) with the overall goal to improve the learning outcomes for first year undergraduate commerce/business students within an Information Systems (IS) subject with very large enrolment. The single research program described in this paper is the analysis of student attitudes and decision making in relation to the availability of formative assessment feedback via Web-based real time conferencing and document exchange software (Adobe Connect). The formative assessment feedback between teaching staff and students is in respect of an authentic problem-based, team-completed assignment. The analysis of student attitudes and decision making is investigated via both qualitative (firstly) and quantitative (secondly) application of the Theory of Planned Behavior (TPB) with a two statistically-significant and separate trial samples of the enrolled students. The initial qualitative TPB investigation revealed that perceived self-efficacy, improved time-management, and lecturer-student relationship building were the major factors in shaping an overall favorable student attitude to online feedback, whilst some students expressed valid concerns with perceived control limitations identified within the online feedback protocols. The subsequent quantitative TPB investigation then confirmed that attitude towards usage, subjective norms surrounding usage, and perceived behavioral control of usage were all significant in shaping student intention to use the online feedback protocol, with these three variables explaining 63 percent of the variance in the behavioral intention to use the online feedback protocol. The identification in this research of perceived behavioral control as a significant determinant in student usage of a specific technology component within a virtual learning environment (VLE) suggests that VLEs could now be viewed not as a single, atomic entity, but as a spectrum of technology offerings ranging from the mature and simple (e.g., email, Web downloads) to the cutting-edge and challenging (e.g., Web conferencing and real-time document exchange). That is, that all VLEs should not be considered the same. The results of this research suggest that tertiary students have the technological sophistication to assess a VLE in this more selective manner.

Keywords: formative assessment feedback, virtual learning environment, theory of planned behavior, perceived behavioral control

Procedia PDF Downloads 398

Authors: Ziying Wu, Danfeng Yan

Abstract:

Multi-Access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based Vehicle-Aware Multi-Access Edge Computing Network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

Keywords: multi-access edge computing, computation offloading, 5th generation, vehicle-aware, deep reinforcement learning, deep q-network

Procedia PDF Downloads 118

Authors: Masud Karim, Md. Solaiman Mia, Saifuddin Md. Tareeq, Md. Hasanuzzaman

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Autism Spectrum Disorder (ASD) is a neurodevelopment disorder. Such disorder is found in childhood life. Children with ASD have less capabilities in communication and social skills. Therapies are used to develop communication and social skills. Recently researchers have been trying to use robots in such therapies. In this paper, we have presented social skill learning test cases for children with ASD. Autism conditions are measured in 30 children in a special school. Among them, twelve children are selected who have equal ASD conditions. Then six children participated in training with humans, and another six children participated in training with robots. The learning session continued for one week and three hours each day. We have taken an assessment test before the learning sessions. After completing the learning sessions, we have taken another assessment test. We have found better performances from children who have participated in robotic sessions rather than the children who have participated in human sessions.

Keywords: children with ASD, NAO robot, human-robot interaction, social skills

Procedia PDF Downloads 88

Authors: Adhi Susilo

Abstract:

This paper addresses the roles of distance education in the agricultural extension workers’ education. Agriculture plays an important role in both poverty reduction and economic growth. The technology of agriculture in the developing world should change continuously to keep pace with rising populations and rapidly changing social, economic, and environmental conditions. Therefore, agricultural extension workers should have several competencies in order to carry out their duties properly. One of the essential competencies that they must possess is the professional competency that is directly related to their duties in carrying out extension activities. Such competency can be acquired through studying at Universitas Terbuka (UT). With its distance learning system, agricultural extension workers can study at UT without leaving their duties. This paper presenting sociological analysis and lessons learnt from the specific context of Indonesia. Diversities in geographic, demographic, social cultural and economic conditions of the country provide specific challenges for its distance education practice and the process of social transformation to which distance education can contribute. Extension officers used distance education for personal benefits and increased professional productivity. An increase in awareness is important for the further adoption of distance learning for extension purposes. Organizations in both the public and private sector must work to increase knowledge of ICTs for the benefit of stakeholders. The use of ICTs can increase productivity for extensions officers and expand educational opportunities for learners. The use of distance education by extension to disseminate educational materials around the world is widespread. Increasing awareness and use of distance learning can lead to more productive relationships between extension officers and agricultural stakeholders.

Keywords: agricultural extension, demographic and geographic condition, distance education, ICTs

Procedia PDF Downloads 515

Authors: Silvia Zanazzi

Abstract:

Artificial intelligence (AI) has become an integral part of our world, transitioning from scientific exploration to practical applications that impact daily life. The emergence of generative AI is reshaping education, prompting new questions about the role of teachers, the nature of learning, and the overall purpose of schooling. While AI offers the potential for optimizing teaching and learning processes, concerns about discrimination and bias arising from training data and algorithmic decisions persist. There is a risk of a disconnect between the rapid development of AI and the goals of building inclusive educational environments. The prevailing discourse on AI in education often prioritizes efficiency and individual skill acquisition. This narrow focus can undermine the importance of collaborative learning and shared experiences. A growing body of research challenges this perspective, advocating for AI that enhances, rather than replaces, human interaction in education. This study aims to examine the relationship between AI and education critically. Reviewing existing research will identify both AI implementation’s potential benefits and risks. The goal is to develop a framework that supports the ethical and effective integration of AI into education, ensuring it serves the needs of all learners. The theoretical reflection will be developed based on a review of national and international scientific literature on artificial intelligence in education. The primary objective is to curate a selection of critical contributions from diverse disciplinary perspectives and/or an inter- and transdisciplinary viewpoint, providing a state-of-the-art overview and a critical analysis of potential future developments. Subsequently, the thematic analysis of these contributions will enable the creation of a framework for understanding and critically analyzing the role of artificial intelligence in schools and education, highlighting promising directions and potential pitfalls. The expected results are (1) a classification of the cognitive biases present in representations of AI in education and the associated risks and (2) a categorization of potentially beneficial interactions between AI applications and teaching and learning processes, including those already in use or under development. While not exhaustive, the proposed framework will serve as a guide for critically exploring the complexity of AI in education. It will help to reframe dystopian visions often associated with technology and facilitate discussions on fostering synergies that balance the ‘dream’ of quality education for all with the realities of AI implementation. The discourse on artificial intelligence in education, highlighting reductionist models rooted in fragmented and utilitarian views of knowledge, has the merit of stimulating the construction of alternative perspectives that can ‘return’ teaching and learning to education, human growth, and the well-being of individuals and communities.

Keywords: education, artificial intelligence, teaching, learning

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Authors: Natalie Frandsen

Abstract:

Pursuing post-secondary education is a milestone for many Canadian youths. This transition involves many changes and opportunities for growth. However, this may also be a period where challenges arise. Perhaps not surprisingly, mental health challenges for post-secondary students are common. This poses difficulties for students and instructors. Common mental-health-related symptoms (e.g., low motivation, fatigue, inability to concentrate) can affect academic performance, and instructors may need to provide accommodations for these students without the necessary expertise. ‘Distance education’ has been growing and gaining momentum in Canada for three decades. As a consequence of the COVID-19 pandemic, post-secondary institutions have been required to deliver courses using ‘remote’ methods (i.e., various online delivery modalities). The learning challenges and subsequent academic performance issues experienced by students with mental-health-related disabilities studying online are not well understood. However, we can postulate potential factors drawing from learning theories, the relationship between mental-health-related symptoms and academic performance, and learning design. Identifying barriers and opportunities to academic performance is an essential step in ensuring that students with mental-health-related disabilities are able to achieve their academic goals. Completing post-secondary education provides graduates with more employment opportunities. It is imperative that our post-secondary institutions take a holistic view of learning by providing learning and mental health support while reducing structural barriers. Health-promoting universities and colleges infuse health into their daily operations and academic mandates. Acknowledged in this Charter is the notion that all sectors must take an active role in favour of health, social justice, and equity for all. Drawing from mental health promotion and Universal Design for Learning (UDL) frameworks, relevant adult learning concepts, and critical digital pedagogy, considerations for mental-health-promoting, online learning community development will be summarized. The education sector has the opportunity to create and foster equitable and mental health-promoting learning environments. This is of particular importance during a global pandemic when the mental health of students is being disproportionately impacted.

Keywords: academic performance, community, mental health promotion, online learning

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Authors: Fariha Asif

Abstract:

The Saudi EFL learners face a number of problems in EFL learning, anxiety is the most potent one among those. It means that its resolution can lead to better language skills in Saudi students. That’s why, the study is carried out and is considered to be of interest to the Saudi language learners, educators and the policy makers because of the potentially negative impact that anxiety has on English language learning. The purpose of the study is to explore the factors that cause language anxiety in the Saudi EFL learners while learning speaking skills and the influence it casts on communication in the target language. The investigation of the anxiety-producing factors that arise while learning to communicate in the target language will hopefully broaden the insight into the issue of language anxiety and will help language teachers in making the classroom environment less stressful. The study seeks to answer the questions such as what are the psycholinguistic factors that cause language anxiety among ESL/EFL learners in learning and speaking English Language, especially in the context of the Saudi students. What are the socio-cultural factors that cause language anxiety among Saudi EFL learners in learning and speaking English Language? How is anxiety manifested in the language learning of the Saudi EFL learners? And which strategies can be used to successfully cope with language anxiety? The scope of the study is limited to the college and university English Teachers and subject specialists (males and females) in public sectors colleges and universities in Saudi Arabia. Some of the key findings of the study are:, Anxiety plays an important role in English as foreign language learning for the Saudi EFL learners. Some teachers believe that anxiety bears negatives effects for the learners, while some others think that anxiety serves a positive outcome for the learners by giving them an extra bit of motivation to do their best in English language learning. Language teachers seem to have consensus that L1 interference is one of the major factors that cause anxiety among the Saudi EFL learners. Most of the Saudi EFL learners are found to have fear of making mistakes. They don’t take initiative and opt to keep quiet and don’t respond fearing that they would make mistakes and this would ruin their image in front of their peers. Discouraging classroom environment is also counted as one of the major anxiety causing factors. The teachers, who don’t encourage learners positively, make them anxious and they start avoiding class participation. It is also found that English language teachers have their important role to minimize the negative effects of anxiety in the classes. The teachers’ positive encouragement can do wonders in this regard. A positive, motivating and encouraging class environment is essential to produce desired results in English language learning for the Saudi EFL learners.

Keywords: factors, psychology, speaking, EFL

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Authors: Krishang Surapaneni

Abstract:

The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

Procedia PDF Downloads 76

Authors: Dulanjali Ranasinghe, Isuru Supasan, Kaushalya Premachandra, Ranjan Dissanayake, Ajith Rajapaksha, Eustace Fernando

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Proteomics studies of organisms are considered to be significantly information-rich compared to their genomic counterparts because proteomes of organisms represent the expressed state of all proteins of an organism at a given time. In modern top-down and bottom-up proteomics workflows, the primary analysis methods employed are gel–based methods such as two-dimensional (2D) electrophoresis and mass spectrometry based methods. Machine learning (ML) and artificial intelligence (AI) have been used increasingly in modern biological data analyses. In particular, the fields of genomics, DNA sequencing, and bioinformatics have seen an incremental trend in the usage of ML and AI techniques in recent years. The use of aforesaid techniques in the field of proteomics studies is only beginning to be materialised now. Although there is a wealth of information available in the scientific literature pertaining to proteomics workflows, no comprehensive review addresses various aspects of the combined use of proteomics and machine learning. The objective of this review is to provide a comprehensive outlook on the application of machine learning into the known proteomics workflows in order to extract more meaningful information that could be useful in a plethora of applications such as medicine, agriculture, and biotechnology.

Keywords: proteomics, machine learning, gel-based proteomics, mass spectrometry

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Authors: Katarzyna Olcoń, Rose M. Pulliam, Dorie J. Gilbert

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This article examines the impact of learning about the history of racial oppression on U.S. university students who participated in a Ghana study abroad which involved visiting the former slave dungeons. Relying on ethnographic observations, individual interviews, and written journals of 27 students (predominantly White and Latino/a and social work majors), we identified four themes: (1) the suffering and resilience of African and African descent people; (2) ‘it’s still happening today’; (3) ‘you don’t learn about that in school’; and (4) remembrance, equity, and healing.

Keywords: racial oppression, anti-racism pedagogy, student learning, social work education, study abroad

Procedia PDF Downloads 120