Search results for: electronic learning

Authors: Lan Cheng, Harry Qin, Yang Wang

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Background: COVID-19 has induced the largest digital learning experiment in history. There is also emerging research evidence that students have paid a high cost of learning loss from virtual learning. University-wide survey results demonstrate that digital learning remains difficult for students who struggle with learning challenges, isolation, or a lack of resources. Large-scale efforts are therefore increasingly utilized for digital education. To better prepare students in higher education for this grand scientific and technological transformation, STEM education has been prioritized and promoted as a strategic imperative in the ongoing curriculum reform essential for unfinished learning needs and whole-person development. Building upon five key elements identified in the STEM education literature: Problem-based Learning, Community and Belonging, Technology Skills, Personalization of Learning, Connection to the External Community, this case study explores the potential of pedagogical innovation that integrates computational and experimental methodologies to support, enrich, and navigate STEM education. Objectives: The goal of this case study is to create a high-fidelity prototype design for STEM education with knowledge transfer technology that contains a Cooperative Multi-Agent System (CMAS), which has the objectives of (1) conduct assessment to reveal a virtual learning mechanism and establish strategies to facilitate scientific learning engagement, accessibility, and connection within and beyond university setting, (2) explore and validate an interactional co-creation approach embedded in project-based learning activities under the STEM learning context, which is being transformed by both digital technology and student behavior change,(3) formulate and implement the STEM-oriented campaign to guide learning network mapping, mitigate the loss of learning, enhance the learning experience, scale-up inclusive participation. Methods: This study applied a case study strategy and a methodology informed by Social Network Analysis Theory within a cross-disciplinary communication paradigm (students, peers, educators). Knowledge transfer technology is introduced to address learning challenges and to increase the efficiency of Reinforcement Learning (RL) algorithms. A co-creation learning framework was identified and investigated in a context-specific way with a learning analytic tool designed in this study. Findings: The result shows that (1) CMAS-empowered learning support reduced students’ confusion, difficulties, and gaps during problem-solving scenarios while increasing learner capacity empowerment, (2) The co-creation learning phenomenon have examined through the lens of the campaign and reveals that an interactive virtual learning environment fosters students to navigate scientific challenge independently and collaboratively, (3) The deliverables brought from the STEM educational campaign provide a methodological framework both within the context of the curriculum design and external community engagement application. Conclusion: This study brings a holistic and coherent pedagogy to cultivates students’ interest in STEM and develop them a knowledge base to integrate and apply knowledge across different STEM disciplines. Through the co-designing and cross-disciplinary educational content and campaign promotion, findings suggest factors to empower evidence-based learning practice while also piloting and tracking the impact of the scholastic value of co-creation under the dynamic learning environment. The data nested under the knowledge transfer technology situates learners’ scientific journey and could pave the way for theoretical advancement and broader scientific enervators within larger datasets, projects, and communities.

Keywords: co-creation, cross-disciplinary, knowledge transfer, STEM education, social network analysis

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Authors: Justin P. Pool, Haruyo Yoshida

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This research focuses on the effects of creating an English as a foreign language curriculum that combines online learning and classroom teaching in a complementary manner. Through pre- and post-test results, teacher observation, and learner reflection, it will be shown that learners can benefit from online programs focusing on receptive skills if combined with a communicative classroom environment that encourages learners to develop their productive skills. Much research has lamented the fact that many modern mobile assisted language learning apps do not take advantage of the affordances of modern technology by focusing only on receptive skills rather than inviting learners to interact with one another and develop communities of practice. This research takes into account the realities of the state of such apps and focuses on how to best create a curriculum that complements apps which focus on receptive skills. The research involved 15 adult learners working for a business in Japan simultaneously engaging in 1) a commercial online English language learning application that focused on reading, listening, grammar, and vocabulary and 2) a 15-week class focused on communicative language teaching, presentation skills, and mitigation of error aversion tendencies. Participants of the study experienced large gains on a standardized test, increased motivation and willingness to communicate, and asserted that they felt more confident regarding English communication. Moreover, learners continued to study independently at higher rates after the study than they had before the onset of the program. This paper will include the details of the program, reveal the improvement in test scores, share learner reflections, and critically view current evaluation models for mobile assisted language learning applications.

Keywords: adult learners, communicative language teaching, mobile assisted language learning, motivation

Procedia PDF Downloads 135

Authors: Maslin Masrom, Nik Hasnaa Nik Mahmood, Wan Normeza Wan Zakaria, Azizul Azizan, Norshaliza Kamaruddin

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Traditionally, in science education, the teacher provides facts and the students learn them. It is outmoded for today’s students to equip them with real-life situations, mainly because knowledge and life skills are acquired passively from the instructors. Inquiry-Based Science Education (IBSE) is an approach that allows students to experiment, ask questions, and develop responses based on reasoning. It has provided students and teachers with opportunities to actively engage in collaborative learning via inquiry. This approach inspires the students to become active thinkers, research for solutions, and gain life-long experience and self-confidence. Therefore, the research aims to investigate how the primary-school teacher supports students or pupils through an inquiry-based science education approach for computer science, specifically coding skills. The results are presented and described.

Keywords: inquiry-based science education, student-centered learning, computer science, primary school

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Authors: Lin Xie, Yang Wang

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An extensive body of research studies suggest that students, teachers, and supervisors can enhance the likelihood of reaching their goals by improving their communication skills. It is highly important to learn how and when to provide different kinds of feedback, e.g. anticipatory, corrective and positive) will gain better result and higher morale. The purpose of this mixed methods research is twofold: 1) To find out what factors affect effective communication among different stakeholders and how these factors affect student learning 2) What are the good practices for improving communication among different stakeholders and improve student achievement. This presentation first begins with an introduction to the recent research on Marshall’s Nonviolent Communication Techniques (NVC), including four important components: observations, feelings, needs, requests. These techniques can be effectively applied at all levels of communication. To develop an in-depth understanding of the relationship among different techniques within, this research collected, compared, and combined qualitative and quantitative data to better improve communication and support student learning.

Keywords: communication, education, language learning, goal achievement, academic success

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Authors: Frazer McDonald Ng'oma

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Online Learning in Malawi has only immersed in recent years due to the need to increase access to higher education, the need to accommodate upgrading students who wish to study on a part time basis while still continuing their work, and the COVID-19 pandemic, which forced the closure of schools resulting in academic institutions seeking alternative modes of teaching and Learning to ensure continued teaching and Learning. Realizing that this mode of Learning is becoming a norm, institutions of higher Learning have started pioneering online post-graduate programs from which they can draw lessons before fully implementing it in undergraduate programs. Online learning pedagogy has not been fully grasped and institutions are still experimenting with this mode of Learning until online Learning guiding policies are created and its standards improved. This single case descriptive qualitative research study sought to investigate online postgraduate students’ perceptions and experiences with Student to student interactive pedagogy in their programs. The results of the study are to inform institutions and educators how to structure their programs to ensure that their students get the full satisfaction. 25 Masters students in 3 recently introduced online programs at Kamuzu University of Health Sciences (KUHES), were engaged; 19 were interviewed and 6 responded to questionnaires. The findings from the students were presented and categorized in themes and subthemes that emerged from the qualitative data that was collected and analysed following Colaizzi’s framework for data analysis that resulted in themes formulation. Findings revealed that Student to student interactions occurred in the online programme during live sessions, on class Whatsapp group, in discussion boards as well as on emails. Majority of the students (n=18) felt the level of students’ interaction initiated by the institution was too much, referring to mandatory interactions activities like commenting in discussion boards and attending to live sessons. Some participants (n=7) were satisfied with the level of interaction and also pointed out that they would be fine with more program-initiated student–to–student interactions. These participants attributed having been out of school for some time as a reason for needing peer interactions citing that it is already difficult to get back to a traditional on-campus school after some time, let alone an online class where there is no physical interaction with other students. In general, majority of the participants (n=18) did not value Student to student interaction in online Learning. The students suggested that having intensive student-to-student interaction in postgraduate online studies does not need to be a high priority for the institution and they further recommended that if a lecturer decides to incorporate student-to-student activities into a class, they should be optional.

Keywords: online learning, interactions, student interactions, post graduate students

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Authors: Ulil Afidah, Muhamad Fathan Mubin, Firdha Aulia

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Code riverbanks Yogyakarta is a settlement area with middle to lower social classes. Socio-economic situation is affecting the behavior of society. This research aimed to find and explain the implementation and the assessment of character education which were done in elementary schools in Code riverside, Yogyakarta region of Indonesia. This research is a qualitative research which the subjects were the kids of Code riverbanks, Yogyakarta. The data were collected through interviews and document studies and analyzed qualitatively using the technique of interactive analysis model of Miles and Huberman. The results show that: (1) The learning process of character education was done by integrating all aspects such as democratic and interactive learning session also introducing role model to the students. 2) The assessment of character education was done by teacher based on teaching and learning process and an activity in outside the classroom that was the criterion on three aspects: Cognitive, affective and psychomotor.

Keywords: character, Code riverbanks, education, Yogyakarta

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Authors: Sagheer Ahmad

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Teaching is a sacred profession whereby students are developed in their mental abilities to cope with the challenges of the remote world. Thinkers have developed plenty of interesting ways to make the learning process quick and absorbing for the students. However, third world countries are still lacking these remote facilities in the institutions, and therefore, teaching is totally dependent upon the skills of the teachers. Skillful teachers use self-devised and stimulating ideas to grab the attention of their students. Most of the time their ideas are based on local grounds with which the students are already familiar. This self-explanatory characteristic is the base of several local ideologies to disseminate scientific knowledge to new generations. Biology is such a subject which largely bases upon hypotheses, and teaching it in an interesting way is needful to create a friendly relationship between teacher and student, and to make a fantastic learning environment. Taxonomic classification if presented as it is, may not be attractive for the secondary school students who just start learning about biology at elementary levels. Presenting this hierarchy by exemplifying Kingdom, Phylum, Class, Order, family, genus and Species as comparatives of our division into continents, countries, cities, towns, villages, homes and finally individuals could be an attention-grabbing approach to make this concept get into bones of students. Similarly, many other interesting approaches have also been adopted to teach students in a fascinating way so that learning science subjects may not be boring for them. Discussing these appealing ways of teaching students can be a valuable stimulus to refine teaching methodologies about science, thereby promoting the concept of friendly learning.

Keywords: biology, innovative approaches, taxonomic classification, teaching

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Authors: Tanmay Bisen, Aastha Shayla, Susham Biswas

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Oil spills from tankers can cause significant harm to the environment and local communities, as well as have economic consequences. Early predictions of oil spills can help to minimize these impacts. Our proposed system uses machine learning and neural networks to predict potential oil spills by monitoring data from ship Automatic Identification Systems (AIS). The model analyzes ship movements, speeds, and changes in direction to identify patterns that deviate from the norm and could indicate a potential spill. Our approach not only identifies anomalies but also predicts spills before they occur, providing early detection and mitigation measures. This can prevent or minimize damage to the reputation of the company responsible and the country where the spill takes place. The model's performance on the MV Wakashio oil spill provides insight into its ability to detect and respond to real-world oil spills, highlighting areas for improvement and further research.

Keywords: Anomaly Detection, Oil Spill Prediction, Machine Learning, Image Processing, Graph Neural Network (GNN)

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Gabriel Michel

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The aim of our research is to find solutions for adapting university teaching to today's students and companies. To achieve this, we have tried to change the posture and behavior of those involved in the learning situation by promoting other skills. There is a gap between the expectations and functioning of students and university teaching. At the same time, the business world needs employees who are obviously competent and proficient in technology, but who are also imaginative, flexible, able to communicate, learn on their own and work in groups. These skills are rarely developed as a goal at university. The flipped classroom has been one solution. Thanks to digital tools such as Moodle, for example, but the model behind them is still centered on teachers and classic learning scenarios: it makes course materials available without really involving them and encouraging them to cooperate. It's against this backdrop that we've conducted action research to explore the possibility of changing the way we learn (rather than teach) by changing the posture of both the classic student and the teacher. We hypothesized that a tool we developed would encourage autonomy, the possibility of progressing at one's own pace, collaboration and learning using all available resources(other students, course materials, those on the web and the teacher/facilitator). Experimentation with this tool was carried out with around thirty German and French first-year students at the Université de Lorraine in Metz (France). The projected changesin the groups' learning situations were as follows: - use the flipped classroom approach but with a few traditional presentations by the teacher (materials having been put on a server) and lots of collective case solving, - engage students in their learning by inviting them to set themselves a primary objective from the outset, e.g. “Assimilating 90% of the course”, and secondary objectives (like a to-do list) such as “create a new case study for Tuesday”, - encourage students to take control of their learning (knowing at all times where they stand and how far they still have to go), - develop cooperation: the tool should encourage group work, the search for common solutions and the exchange of the best solutions with other groups. Those who have advanced much faster than the others, or who already have expertise in a subject, can become tutors for the others. A student can also present a case study he or she has developed, for example, or share materials found on the web or produced by the group, as well as evaluating the productions of others, - etc… A questionnaire and analysis of assessment results showed that the test group made considerable progress compared with a similar control group. These results confirmed our hypotheses. Obviously, this tool is only effective if the organization of teaching is adapted and if teachers are willing to change the way they work.

Keywords: pedagogy, cooperation, university, learning environment

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Authors: Felix Golla

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In an era where technology increasingly intersects with education, the potential of chatbots and ChatGPT agents in enhancing student learning experiences in higher education is both significant and timely. This study explores the integration of these AI-driven tools in educational settings, emphasizing their design and functionality to meet the specific needs of students. Recognizing the gap in literature concerning student-centered AI applications in education, this research offers valuable insights into the role and efficacy of chatbots and ChatGPT agents as educational tools. Employing qualitative research methodologies, the study involved conducting semi-structured interviews with university students. These interviews were designed to gather in-depth insights into the students' experiences and expectations regarding the use of AI in learning environments. The High-Performance Cycle Model, renowned for its focus on goal setting and motivation, served as the theoretical framework guiding the analysis. This model helped in systematically categorizing and interpreting the data, revealing the nuanced perceptions and preferences of students regarding AI tools in education. The major findings of the study indicate a strong preference among students for chatbots and ChatGPT agents that offer personalized interaction, adaptive learning support, and regular, constructive feedback. These features were deemed essential for enhancing student engagement, motivation, and overall learning outcomes. Furthermore, the study revealed that students perceive these AI tools not just as passive sources of information but as active facilitators in the learning process, capable of adapting to individual learning styles and needs. In conclusion, this study underscores the transformative potential of chatbots and ChatGPT agents in higher education. It highlights the need for these AI tools to be designed with a student-centered approach, ensuring their alignment with educational objectives and student preferences. The findings contribute to the evolving discourse on AI in education, suggesting a paradigm shift towards more interactive, responsive, and personalized learning experiences. This research not only informs educators and technologists about the desirable features of educational chatbots but also opens avenues for future studies to explore the long-term impact of AI integration in academic curricula.

Keywords: chatbot design in education, high-performance cycle model application, qualitative research in AI, student-centered learning technologies

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Mohammed Hassan Alshaikhi

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Vocabulary is essential factor of learning and mastering any languages, and it helps learners to communicate with others and to be understood. The aim of this study was to examine whether semantic mapping technique was helpful in terms of improving student's English vocabulary learning comparing to the traditional technique. The students’ age was between 11 and 13 years old. There were 60 students in total who participated in this study. 30 students were in the treatment group (target vocabulary items were taught with semantic mapping). The other 30 students were in the control group (the target vocabulary items were taught by a traditional technique). A t-test was used with the results of pre-test and post-test in order to examine the outcomes of using semantic mapping when teaching vocabulary. The results showed that the vocabulary mastery in the treatment group was increased more than the control group.

Keywords: English language, learning vocabulary, Saudi teachers, semantic mapping, teaching vocabulary strategies

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Mustafa Gultekin

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It is believed that drama can be used in language classes to create a positive atmosphere for students to use the target language in an interactive way. Thus, drama has been extensively used in many settings in language classes. Although happening has been generally used as a performance art of theatre, this new kind of performance has not been widely known in language teaching area. Therefore, it can be an innovative idea to use happening in language classes, and thus a positive environment can be created for students to use the language in an interactive way. Happening can be defined as an art performance that puts emphasis on interaction in an audience. Because of its interactive feature, happening can also be used in language classes to motivate students to use the language in an interactive environment. The present study aims to explain how a happening performance can be applied to a learning environment to teach vocabulary in English. In line with this purpose, a learning environment was designed for a vocabulary presentation lesson. At the end of the performance, students were asked to compare the traditional way of teaching and happening performance in terms of effectiveness. It was found that happening performance provided the students with a more creative and interactive environment to use the language. Therefore, happening can be used in language classrooms as an innovative tool for education.

Keywords: English, happening, language learning, vocabulary teaching

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: baby care system, Internet of Things, deep learning, machine vision

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Authors: Rajaian Hoonejani Mohammad, Eshraghi Pegah, Zomorodian Zahra Sadat, Tahsildoost Mohammad

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

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

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Authors: Nhung T. Bui

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Part of the dilemma facing educational reforms in Vietnam as in other Asian contexts is how to encourage more independence in students’ learning approaches. Since 2005, the Ministry of Education and Training of Vietnam has included the students’ ability to learn independently in its national education objectives. While learner autonomy has been viewed as a goal in the teaching and learning English as a foreign language (EFL) and there has been a considerable literature on strategies to stimulate autonomy in learners, teachers’ voices have rarely been heard. Given that teachers play a central role in helping their students to be more autonomous, especially in an inherent Confucian heritage culture like Vietnam, their attitudes towards learner autonomy should be investigated before any practical implementations could be undertaken. This paper reports significant findings of a survey questionnaire with 262 lecturers of English from 5 universities in Hanoi, Vietnam giving opinions regarding the practices and prospects of learner autonomy in their classrooms. The study reveals that lecturers perceive they should be more responsible than their students in all class-related activities; they most appreciate their students’ ability to learn cooperatively and that they consider stimulating students’ interest as the most important teaching strategy to promote learner autonomy. Lecturers, then, are strongly suggested to gradually ‘empower’ their students through the application of out-of-classroom activities; of learning activities which requires collaboration and team spirit; and of activities which could boost students’ interest in learning English.

Keywords: English as a foreign language, higher education, learner autonomy, Vietnam

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Authors: Prakash Singh

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The focus of this exploratory study is on whether teachers demonstrate emphatic concern for their learners in planning, implementing and assessing learning outcomes in their regular classrooms. Empathy must be shown to all learners equally and not only for high-risk learners at the expense of other ability learners. Empathy demonstrated by teachers allows them to build a stronger bond with all their learners. This bond based on trust leads to positive outcomes for learners to be able to excel in their work. Empathic teachers must make every effort to simplify the subject matter for high risk learners so that these learners not only enjoy their learning activities but are also successful like their more able peers. A total of 87.5% of the participants agreed that empathy allows teachers to demonstrate humanistic values in their choice of learning materials for learners of different abilities. It is therefore important for teachers to select content and instructional materials that will contribute to the learners’ success in the mainstream of education. It is also imperative for teachers to demonstrate empathic skills and consequently, to be attuned to the emotions and emotional needs of their learners. Schools need to be reformed, not by simply lengthening the school day or by simply adding more content in the curriculum, but by making school more satisfying to learners. This must be consistent with their diverse learning needs and interests so that they gain a sense of power, fulfillment, and importance in their regular classrooms. Hence, teacher - pupil relationships based on empathic concern for the latter’s educational needs lays the foundation for quality education to be offered.

Keywords: emotional intelligence, empathy, learners’ emotional needs, teachers’ empathic skills

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Authors: Carlos Gordon, Patricio Encalada, Henry Lema, Diego Leon, Dennis Chicaiza

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The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms.

Keywords: autonomous navigation, machine learning, path planning, robotic operative system, open source computer vision library

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Authors: Duangkamol Thitivesa

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This paper explores the use of project work in a content-based instruction in a Rajabhat University, Thailand. The use of project is to promote kinds of learning expected of student teachers as stated by Thailand Quality Framework: TQF. The kinds of learning are grouped into five domains: Ethical and moral development, knowledge, cognitive skill, interpersonal skills and responsibility, and analytical and communication skills. The content taught in class is used to lead the student teachers to relate their previously-acquired linguistic knowledge to meaningful realizations of the language system in passages of immediate relevance to their professional interests, teaching methods in particular. Two research questions are formulate to guide this study: 1) To what degree are the five domains of learning expected of student teachers after the use of project in a content class?, and 2) What is the academic achievement of the students’ writing skills, as part of the learning domains stated by TQF, against the 70% attainment target after the use of project to enhance the skill? The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of a summative achievement test, student writing works, an observation checklist, and project diary. The scores in the summative achievement test were analyzed by mean score, standard deviation, and t-test. Project diary serves as students’ record of the language acquired during the project. List of structures and vocabulary noted in the diary has shown students’ ability to attend to, recognize, and focus on meaningful patterns of language forms.

Keywords: Thailand quality framework, project Work, writing skill, summative

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Authors: Björn de Kruijf, Arjen Edzes, Sietske Waslander

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In an ever-quickly changing society, lifelong development is seen as a solution to labor market problems by politicians and policymakers. In this paper, we investigate how policy instruments are used to increase participation in lifelong development and on which behavioral principles policy is based. Digitization, automation, and an aging population change society and the labor market accordingly. Skills that were once most sought after in the workforce can become abundantly present. For people to remain relevant in the working population, they need to continue adapting new skills useful in the current labor market. Many reports have been written that focus on the role of lifelong development in this changing society and how lifelong development can help keep people adapt and stay relevant. Inspired by these reports, governments have implemented a broad range of policies to support participation in lifelong development. The question we ask ourselves is how government policies promote participation in lifelong development. This stems from a complex interplay of policy instruments and learning. Regulation, economic and soft instruments can be combined to promote lifelong development, and different types of education further complex policies on lifelong development. Literature suggests that different stages in people’s lives might warrant different methods of learning. Governments could anticipate this in their policies. In order to influence people’s behavior, the government can tap into a broad range of sociological, psychological, and (behavioral) economic principles. The traditional economic assumption that behavior is rational is known to be only partially true, and the government can use many biases in human behavior to stimulate participation in lifelong development. In this paper, we also try to find which biases the government taps into to promote participation if they tap into any of these biases. The goal of this paper is to analyze government policies intended to promote participation in lifelong development. To do this, we develop a framework to analyze the policies on lifelong development. We specifically incorporate the role of learning and the behavioral principles underlying policy instruments in the framework. We apply this framework to the case of the Netherlands, where we examine a set of policy documents. We single out the policies the government has put in place and how they are vertically and horizontally related. Afterward, we apply the framework and classify the individual policies by policy instrument and by type of learning. We find that the Dutch government focuses on formal and non-formal learning in their policy instruments. However, the literature suggests that learning at a later age is mainly done in an informal manner through experiences.

Keywords: learning, lifelong development, policy analysis, policy instruments

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

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In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: dynamic system modeling, neural network, normal equation, second order gradient descent

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Authors: Hong Zhang

Abstract:

The main purpose of this study is to show that machine translation (MT) post-editing (PE) training can help our Chinese students learn Spanish as a second language. Our hypothesis is that they might make better use of it by learning PE skills specific for foreign language learning. We have developed PE training materials based on the data collected in a previous study. Training material included the special error types of the output of MT and the error types that our Chinese students studying Spanish could not detect in the experiment last year. This year we performed a pilot study in order to evaluate the PE training materials effectiveness and to what extent PE training helps Chinese students who study the Spanish language. We used screen recording to record these moments and made note of every action done by the students. Participants were speakers of Chinese with intermediate knowledge of Spanish. They were divided into two groups: Group A performed PE training and Group B did not. We prepared a Chinese text for both groups, and participants translated it by themselves (human translation), and then used Google Translate to translate the text and asked them to post-edit the raw MT output. Comparing the results of PE test, Group A could identify and correct the errors faster than Group B students, Group A did especially better in omission, word order, part of speech, terminology, mistranslation, official names, and formal register. From the results of this study, we can see that PE training can help Chinese students learn Spanish as a second language. In the future, we could focus on the students’ struggles during their Spanish studies and complete the PE training materials to teach Chinese students learning Spanish with machine translation.

Keywords: machine translation, post-editing, post-editing training, Chinese, Spanish, foreign language learning

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Authors: Amal Machtalay

Abstract:

In this survey, we aim to review the rapidly growing literature on numerical methods to solve different forms of mean field problems, namely mean field games (MFG), mean field controls (MFC), potential MFGs, and master equations, as well as their corresponding recent applications. Here, we distinguish two families of numerical methods: iterative methods based on mesh generation and those called mesh-free, normally related to neural networking and learning frameworks.

Keywords: mean-field games, numerical schemes, partial differential equations, complex systems, machine learning

Procedia PDF Downloads 113

Authors: David Sinfield, Thomas Cochrane, Marcos Steagall

Abstract:

While there is much hype around the potential and development of mobile virtual reality (VR), the two key critical success factors are the ease of user experience and the development of a simple user-generated content ecosystem. Educational technology history is littered with the debris of over-hyped revolutionary new technologies that failed to gain mainstream adoption or were quickly superseded. Examples include 3D television, interactive CDROMs, Second Life, and Google Glasses. However, we argue that this is the result of curriculum design that substitutes new technologies into pre-existing pedagogical strategies that are focused upon teacher-delivered content rather than exploring new pedagogical strategies that enable student-determined learning or heutagogy. Visual Communication design based learning such as Graphic Design, Illustration, Photography and Design process is heavily based on the traditional forms of the classroom environment whereby student interaction takes place both at peer level and indeed teacher based feedback. In doing so, this makes for a healthy creative learning environment, but does raise other issue in terms of student to teacher learning ratios and reduced contact time. Such issues arise when students are away from the classroom and cannot interact with their peers and teachers and thus we see a decline in creative work from the student. Using AR and VR as a means of stimulating the students and to think beyond the limitation of the studio based classroom this paper will discuss the outcomes of a student project considering the virtual classroom and the techniques involved. The Atelier learning environment is especially suited to the Visual Communication model as it deals with the creative processing of ideas that needs to be shared in a collaborative manner. This has proven to have been a successful model over the years, in the traditional form of design education, but has more recently seen a shift in thinking as we move into a more digital model of learning and indeed away from the classical classroom structure. This study focuses on the outcomes of a student design project that employed Augmented Reality and Virtual Reality technologies in order to expand the dimensions of the classroom beyond its physical limits. Augmented Reality when integrated into the learning experience can improve the learning motivation and engagement of students. This paper will outline some of the processes used and the findings from the semester-long project that took place.

Keywords: augmented reality, blogging, design in community, enhanced learning and teaching, graphic design, new technologies, virtual reality, visual communications

Procedia PDF Downloads 238

Authors: Dua Hişam, Serhat İkizoğlu

Abstract:

Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

Procedia PDF Downloads 69

Authors: Nadhifah A. Nugraha, Dida D. Damayanti, Widia Juliani

Abstract:

Airplanes manufacturing is growing along with the increasing demand from consumers. The helicopter's tail called Tailboom is a product of the helicopter division at XYZ Corp, where the Tailboom assembly line is a pull system. Based on observations of existing conditions that occur at XYZ Corp, production is still unable to meet the demands of consumers; lead time occurs greater than the plan agreed upon by the consumers. In the assembly process, each work station experiences a lack of parts and components needed to assemble components. This happens because of the delay in getting the required part information, and there is no warning about the availability of parts needed, it makes some parts unavailable in assembly warehouse. The lack of parts and components from the previous work station causes the assembly process to stop, and the assembly line also stops at the next station. In its completion, the production time was late and not on the schedule. In resolving these problems, the controlling process is needed, which is controlling the assembly line to get all components and subassembly in the right amount and at the right time. This study applies one of Just In Time tools, namely Kanban and automation, should be added as efficiently and effectively communication line becomes electronic Kanban. The problem can be solved by reducing non-value added time, such as waiting time and idle time. The proposed results of controlling the assembly line of Tailboom result in a smooth assembly line without waiting, reduced lead time, and achieving production time according to the schedule agreed with the consumers.

Keywords: kanban, e-Kanban, lead time, pull system

Procedia PDF Downloads 114

Authors: Ngong Eugene Ekia

Abstract:

Education is the foundation for the success of any society as its impact transcends across all sectors, including economics, politics, and social welfare. It is through education that individuals acquire the necessary knowledge and skills to succeed in life and contribute meaningfully to society. However, the world is changing rapidly, and it is vital for education systems to adapt to these changes to remain relevant. In this paper, we will discuss the current trends and challenges in education and management and propose solutions that can enable individuals to thrive in an ever-evolving world.

Keywords: access to education, effective teaching and learning, strong management practices, and empowering and personal development

Procedia PDF Downloads 142

Authors: A. Dinelka Panagoda, Bathiya Bandara, Chamod Wijetunga, Chathura Malinda, Lakmal Rupasinghe, Chethana Liyanapathirana

Abstract:

This research takes us forward with the concepts of Federated Learning and Software-Defined Networking (SDN) to introduce an efficient malware detection technique and provide a mitigation mechanism to give birth to a resilient and automated healthcare sector network system by also adding the feature of extended privacy preservation. Due to the daily transformation of new malware attacks on hospital Integrated Clinical Environment (ICEs), the healthcare industry is at an undefinable peak of never knowing its continuity direction. The state of blindness by the array of indispensable opportunities that new medical device inventions and their connected coordination offer daily, a factor that should be focused driven is not yet entirely understood by most healthcare operators and patients. This solution has the involvement of four clients in the form of hospital networks to build up the federated learning experimentation architectural structure with different geographical participation to reach the most reasonable accuracy rate with privacy preservation. While the logistic regression with cross-entropy conveys the detection, SDN comes in handy in the second half of the research to stack up the initial development phases of the system with malware mitigation based on policy implementation. The overall evaluation sums up with a system that proves the accuracy with the added privacy. It is no longer needed to continue with traditional centralized systems that offer almost everything but not privacy.

Keywords: software-defined network, federated learning, privacy, integrated clinical environment, decentralized learning, malware detection, malware mitigation

Procedia PDF Downloads 187