Search results for: learning management systems

Authors: Xue Wu

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Since the 1980s, a series of policies and practices have been implemented in China in response to the unprecedented rate of ageing population. The paper provides a detailed narrative of what later-life learning policy discourses have been advocated and gives a description on relevant practical issues during the past three decades. The research process based on the discourse approach with a systematic review of the government-issued documents. It finds that the main practices taken by central government at various levels were making University of the Aged (UA) available in all urban and rural regions to consolidate the newly student enrollments; focusing social-recreational, leisure and cultural activities on 55-75 age group; and utilizing various methods including voluntary works and tourism to improve older adults’ physical and mental wellness. Although there were greater achievements with 30 years of development, many problems still exist. Finding reveals that the curriculum should be modified to meet the needs of the local development, to promote older adults’ contact and contribution to the community, and to enhance technical competences of those in rural areas involving in agricultural production. Central government should also integrate resources from all sectors of the society for further developing later-life learning in China. The result of this paper highlights the value to promote community-based later-life learning for building a society for active ageing and ageing in place.

Keywords: ageing population, China, later-life learning, policy, University of the Aged

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Authors: Fatemeh Miraki

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The use of authentic materials in English Language Teaching (ELT) has become increasingly prominent as educators recognize the value of exposing learners to real-world language use and cultural contexts. The integration of authentic materials in ELT aligns with the understanding that language learning is most effective when situated within authentic contexts (Richards & Rodgers, 2001). Tomlinson (1998) highlights the significance of authentic materials in ELT by research indicating that they offer learners exposure to genuine language use and cultural contexts. Tomlinson's work emphasizes the importance of creating meaningful learning experiences through the use of authentic materials. Research by Dörnyei (2001) underscores the potential of authentic materials to enhance students' intrinsic motivation through their relevance to real-life language use. The goal of this review paper is to explore the use of authentic materials in English Language Teaching (ELT) and its impact on language learning. It also discusses best practices for selecting and integrating such authentic materials into ELT curriculum, highlighting the benefits and challenges of using authentic materials to enhance student engagement, motivation, and language proficiency. Drawing on current research and practical examples, this paper provides insights into how teachers can effectively navigate the world of authentic materials to create dynamic and meaningful learning experiences for 21st century ELT learners. The findings of this study advocates for a shift towards embracing authentic materials within the ELT classroom, acknowledging their profound impact on language proficiency, intercultural competence, and learner engagement. It showed the transformative potential of authentic materials, educators can undergo a vibrant and immersive language learning experience, enriched with real-world application and cultural authenticity.

Keywords: authentic materials, ELT Classroom, ELT curriculum, students’ engagement

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Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

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Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

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Authors: Mengqiu Deng, Xiao Peng, Yang Zhao

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The development of local energy systems requires the collective involvement of different actors from various levels of society. However, the stakeholder analysis of local energy systems still has been under-developed. This paper proposes an multi-agent system (MAS) framework to facilitate the development of stakeholder analysis of local energy systems. The framework takes into account the most influencing stakeholders, including prosumers/consumers, system operators, energy companies and government bodies. Different stakeholders are modeled based on agent architectures for example the belief-desire-intention (BDI) to better reflect their motivations and interests in participating in local energy systems. The agent models of different stakeholders are then integrated in one model of the whole energy system. An illustrative case study is provided to elaborate how to develop a quantitative agent model for different stakeholders, as well as to demonstrate the practicability of the proposed framework. The findings from the case study indicate that the suggested framework and agent model can serve as analytical instruments for enhancing the government’s policy-making process by offering a systematic view of stakeholder interconnections in local energy systems.

Keywords: multi-agent system, BDI agent, local energy systems, stakeholders

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Authors: Seon-Hack Hong, Tae-Il Choi

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Interoperability is defined as systems, components, and devices developed by different entities smoothly exchanging information and functioning organically without mutual consultation, being able to communicate with each other and computer systems of the same type or different types, and exchanging information or the ability of two or more systems to exchange information and use the information exchanged without extra effort. Insufficiencies such as duplication of functions when developing systems and applications due to lack of interoperability in the electric power system and low efficiency due to a lack of mutual information transmission system between the inside of the application program and the design is improved, and the seamless linkage of newly developed systems is improved. Since it is necessary to secure interoperability for this purpose, we designed the smart grid-based interoperability standard model in this paper.

Keywords: interoperability, power system, common information model, SCADA, IEEE2030, Zephyr

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Authors: Jeffrey Siphiwe Mkhize

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Twelve years of schooling for most South African children, particularly those children from disadvantaged past, are confronted with numerous and diverse challenges. These challenges range from infrastructural limitations, language of teaching, poor resources and varying family backgrounds. Likewise, schools are categorized to signify schools’ geographic location, poverty lines, societal class and type of students that the school are likely to enroll. Such categorization perpetuates particular lines of identities that are indirectly reinforced by the same system that seeks to redress. South African universities prefer point systems to determine students’ suitability to gain access to their programmes. Once students are admitted based on the qualifying points there is an assumed equity in the manner in which they receive tuition. They are assumed as equal; noting the widened access to South African universities as means to redress past inequalities. Given the challenges, inequalities, it is necessary to view higher education as a site for knowledge construction that is accessible to all students. Epistemological access is key to all students irrespective of their socio-economic status. This paper seeks to contribute to the discourse of student engagement using lecturer-student relationship as a lens to understand this phenomenon. Data were generated using South African Survey of Student Engagement, focus group interviews, semi-structured one-on-one-interviews as well as document analysis. The focus was on students registered for the first year of a Bachelor of Education degree as well as lecturers that teach high risk modules in this qualification at the same level. The findings suggest that lecturers are challenged by overcrowded classrooms and over-enrolled modules; this challenge hampers their good intentions to become more efficient and innovative in their teaching. Students lack confidence in approaching lecturers for assistance. Collaborative learning has stronger results and students believe in self-support to deal with their challenges based on their individual strengths. Collaborative learning is key to student academic performance.

Keywords: collaborative learning, consultations, student engagement, student performance

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Benhenni Abdellaziz

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Irrigation is a limiting factor for agricultural production and socio-economic development of many countries in arid and semiarid in the world. However, the sustainability of irrigation systems requires a rational management of the water resource that is becoming increasingly rare in these regions. The objective of this work is to apply a geographic information system (GIS) coupled to a model for calculating crop water requirements (CROPWATER) for the management of irrigation water in irrigated area and offer managers with an effective tool to better manage water resources in these areas. The application area of GIS is the irrigated perimeter of Western Middle Cheliff which is located in a semi-arid region (Middle Cheliff). The scope in question is a considerable agrarian dynamics and an increased need for irrigation of most crops.

Keywords: geographical information, irrigation, economical, use rational

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Authors: Bruno Vilić Belina, Jadranko Matuško

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The rapid development of cyber-physical systems significantly influences modern control systems introducing a whole new range of applications of control systems but also putting them under new challenges to ensure their resiliency to possible cyber attacks, either in the form of data integrity attacks or deception attacks. This paper presents a model predictive approach to the control of cyber-physical systems robust to cyber attacks. We assume that a cyber attack can be modelled as an additive disturbance that acts in the measuring channel. For such a system, we designed a tube-based predictive controller based. The performance of the designed controller has been verified in Matlab/Simulink environment.

Keywords: control systems, cyber attacks, resiliency, robustness, tube based model predictive control

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Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: Santosh Kumar Sharma

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In view of management education with special reference to India, it has been noted that students have deviations between their career anchors and domain of specialization. As a consequence, they face problems in their summer internships and placements in the corporate sector. Eventually, they either change their career track or leave the management profession, which is a serious concern from the perspective of human capital. However, there is no substantial literature in the given context. Therefore, the present study contributes to the global discourse of management education and its spillover effect on human resource management. The objective of the present study is to analyze the deviation between career anchors and domain specialization with reference to management education in India. The present study is exploratory in nature, wherein data has been collected from a significant number of post-graduate students who are pursuing management education from a premium business school in India, followed by descriptive analysis. The present research contributes to the professional development of management students from the perspective of human capital, which is eventually related to various factors of the Indian economy.

Keywords: India, management education, domain specialization, placements

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Authors: Aurelia Samuel

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Socio-economic modelling approaches linked to water management have contributed to impact assessments of agricultural policies and management practices on water quality at catchment level. With an increasing interest in informing water management policy that considers complex links between socioeconomic factors, climate change, agricultural production, and water quality, several models have been developed and applied in the literature to capture these relationships. This paper offers an overview of socio-economic approaches that have been incorporated within an integrated framework. It also highlights how data gaps on socio-economic factors have been addressed using forecasting techniques. Findings of the review show that while integrated frameworks have the potential to account for complexities within dynamic systems, they generally do not provide direct, measurable financial impact of socio-economic factors on biophysical water parameters that affect water quality. The paper concludes with a recommendation that modelling framework is kept simple to make it more transparent and easier to capture the most important relationship.

Keywords: financial impact, integrated framework, socio-economic modelling, water quality

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Authors: Muhalim Muhalim

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Drawing on Lakoff’s theory of metaphor conceptualization, this article explores the conceptualization of language two writing (L2W) of ten students-teachers in Indonesia via metaphors. The ten postgraduate English language teaching students and at the same time (former) English teachers received seven days of intervention in teaching and learning L2. Using introspective log and focus group discussion, the results illuminate us that all participants are unanimous on perceiving L2W as process-oriented rather than product-oriented activity. Specifically, the metaphor conceptualizations exhibit three categories of process-oriented L2W: deliberate process, learning process, and problem-solving process. However, it has to be clarified from the outset that this categorization is not rigid because some of the properties of metaphors might belong to other categories. Results of the study and implications for English language teaching will be further discussed.

Keywords: metaphor conceptualisation, second language, learning writing, teaching writing

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Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

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Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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Authors: Ailene B. Anonuevo

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The aim of the study was to evaluate the quality of life presently available for the IP students in selected schools in the Division of Panabo City. This further explores their future dreams and current status in classes and examines some implications relative to their studies. The study adopted the mixed methodology and used a survey research design as the operational framework for data gathering. Data were collected by self-administered questionnaires and interviews with sixty students from three schools in Panabo City. In addition, this study describes the learners’ background and school climate as variables that might influence their performance in school. The study revealed that an IP student needs extra attention due to their unfavorable learning environment. The study also found out that like any other students, IP learners yearns for a brighter future with the support of our government.

Keywords: IP learners, learning landscape, school climate, quality of life

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Authors: Prajwal Prakash Vasisht, Sharath Rajamurthy, Nishanth Dara

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In a supervised machine learning approach, metrics such as precision, accuracy, and coverage can be calculated using ground truth labels to help in model tuning, evaluation, and selection. In an unsupervised setting, however, where the data has no ground truth, there are few interpretable metrics that can guide us to do the same. Our approach creates a framework to adapt the Intersection over Union metric, referred to as Adapted IoU, usually used to evaluate supervised learning models, into the unsupervised domain, which solves the problem by factoring in subject matter expertise and intuition about the ideal output from the model. This metric essentially provides a scale that allows us to compare the performance across numerous unsupervised models or tune hyper-parameters and compare different versions of the same model.

Keywords: general metric, unsupervised learning, classification, intersection over union

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Authors: Cindy Davis

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Higher education is changing globally and there is increasing pressure from professional social work accreditation bodies for academic programs to demonstrate how students have successfully met mandatory graduate competencies. As professional accreditation organizations increase their demand for evidence of graduate competencies, strategies to document and recording learning outcomes becomes increasingly challenging for academics and students. Studies in higher education have found support for the pedagogical value of ePortfolios, a flexible personal learning space that is owned by the student and include opportunity for assessment, feedback and reflection as well as a virtual space to store evidence of demonstration of professional competencies and graduate attributes. Examples of institutional uses of ePortfolios include e-administration of a diverse student population, assessment of student learning, and the demonstration of graduate attributes attained and future student career preparation. The current paper presents a case study on the introduction of ePortfolios for social work graduates in Australia as part of an institutional approach to technology-enhanced learning and e-learning. Social work graduates were required to submit an ePortfolio hosted on PebblePad. The PebblePad platform was selected because it places the student at the center of their learning whilst providing powerful tools for staff to structure, guide and assess that learning. The ePortofolio included documentation and evidence of how the student met each graduate competency as set out by the social work accreditation body in Australia (AASW). This digital resource played a key role in the process of external professional accreditation by clearly documenting and evidencing how students met required graduate competencies. In addition, student feedback revealed a positive outcome on how this resource provided them with a consolidation of their learning experiences and assisted them in obtaining employment post-graduation. There were also significant institutional factors that were key to successful implementation such as investment in the digital technology, capacity building amongst academics, and technical support for staff and students.

Keywords: accreditation, social work, teaching, technology

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Authors: Theodorou Kyriaki, Ypsilantis George

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Vocabulary learning has attracted a lot of attention in recent years, contrary to the neglected part of the past. Along with the interest in finding successful vocabulary teaching strategies, many scholars focused on locating learning strategies used by language learners. As a result, more and more studies in the area of language pedagogy have been investigating the use of strategies in vocabulary learning by different types of learners. A common instrument in this field is the questionnaire, a tool of work that was enriched by questions involving current technology, and it was further implemented to a sample of 300 Greek students whose age varied from 9 and 17 years. Strategies located were grouped into the three categories of memory, cognitive, and compensatory type and associations between these dependent variables were investigated. In addition, relations between dependent and independent variables (such as age, sex, type of school, cultural background, and grade in English) were pursued to investigate the impact on strategy selection. Finally, results were compared to findings of other studies in the same field to contribute to a hypothesis of ethnic differences in strategy selection. Results initially discuss preferred strategies of all participants and further indicate that: a) technology affects strategy selection while b) differences between ethnic groups are not statistically significant. A number of successful strategies are presented, resulting from correlations of strategy selection and final school grade in English.

Keywords: acquisition of English, internet technology, research among Greek students, vocabulary learning strategies

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Authors: Abdullah Abu-Tineh, Youmen Chaaban

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This study examines the development of a professional development (PD) model for teacher growth and learning that is embedded into the school context. The School based Support Program (SBSP), designed for the Qatari context, targets the practices, knowledge and skills of both school leadership and teachers in an attempt to improve student learning outcomes. Key aspects of the model include the development of learning communities among teachers, strong leadership that supports school improvement activities, and the use of research-based PD to improve teacher practices and student achievement. This paper further presents findings from an evaluation of this PD program. Based on an adaptation of Guskey’s evaluation of PD models, 100 teachers at the participating schools were selected for classroom observations and 40 took part in in-depth interviews to examine changed classroom practices. The impact of the PD program on student learning was also examined. Teachers’ practices and their students’ achievement in English, Arabic, mathematics and science were measured at the beginning and at the end of the intervention.

Keywords: initiative, professional development, school based support Program (SBSP), school reform

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Authors: Emin Gundogar, Aysegul Yilmaz

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Strategic performance control is a significant procedure in management. There are various methods to improve this procedure. This study introduces an information system that is developed to score performance for municipal management. The application of the system is clarified by exemplifying municipal processes.

Keywords: management information system, municipal management, performance control

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Authors: Alok Kumar, Raviraj Patil

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The ability to respond to an incident of a disastrous event in a vulnerable area is very crucial an aspect of emergency management. The ability to constantly predict the likelihood of an event along with its severity in an area and react to those significant events which are likely to have a high impact allows the authorities to respond by allocating resources optimally in a timely manner. It provides for measuring, monitoring, and modeling facilities that integrate underlying systems into one solution to improve operational efficiency, planning, and coordination. We were particularly involved in this innovative incubation work on the current state of research and development in collaboration. technologies & systems for a disaster.

Keywords: predictive analytics, advanced analytics, area flood likelihood model, area flood severity model, level of impact model, mortality score, economic loss score, resource allocation, crew allocation

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Authors: Luis Rodrigo Valencia Pérez, Juan Manuel Peña Aguilar, Adelina Morita Alexander, Alberto Lamadrid Alvarez, Héctor Fernando Valencia Pérez

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Financial education is among the areas of opportunity in the Spanish-speaking from an early age to high school, through mobile devices such as cell phones and tablets using ludic and fun applications like interactive games, children can learn money management and investment through time, thereby fostering the habit of saving and/or sound management of cash and family business resources, having interaction with an uncontrolled environment such as the involvement of other players in the external decisions of the environment in which the game is play. The application proposed in Phase 1 (design and development) was designed in multi-user environments, under methodologies of hybrid programming for any platform on the market and designed under CMMI standards that allow for quality production over time, following up on these improvements counting with continuous user feedback and usage statistics.

Keywords: mobile educational games, ludic games, children, multiuser, design and software development

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Authors: Teh Kean Kheng, Low Soon Yee, Burra Venkata Durga Kumar

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In 2022, as the future world becomes increasingly computer-related, more individuals are attempting to study coding for themselves or in school. This is because they have discovered the value of learning code and the benefits it will provide them. But learning coding is difficult for most people. Even senior programmers that have experience for a decade year still need help from the online source while coding. The reason causing this is that coding is not like talking to other people; it has the specific syntax to make the computer understand what we want it to do, so coding will be hard for normal people if they don’t have contact in this field before. Coding is hard. If a user wants to learn bash code with bash prompt, it will be harder because if we look at the bash prompt, we will find that it is just an empty box and waiting for a user to tell the computer what we want to do, if we don’t refer to the internet, we will not know what we can do with the prompt. From here, we can conclude that the bash prompt is not user-friendly for new users who are learning bash code. Our goal in writing this paper is to give an idea to implement a user-friendly Bash prompt in Ubuntu OS using Artificial Intelligent (AI) to lower the threshold of learning in Bash code, to make the user use their own words and concept to write and learn Bash code.

Keywords: user-friendly, bash code, artificial intelligence, threshold, semantic similarity, lexical similarity

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Authors: Sinchai Poolklai

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The purpose of this research paper was aimed to examine the level of satisfaction of the faculty members who participated in the ASEAN camp, Chantaburi, Thailand. The population of this study included all the faculty members of Suan Sunandha Rajabhat University who participated in the training and activities of the ASEAN camp during March, 2014. Among a total of 200 faculty members who answered the questionnaire, the data was complied by using SPSS program. Percentage, mean and standard deviation were utilized in analyzing the data. The findings revealed that the average mean of satisfaction was 4.37, and standard deviation was 0.7810. Moreover, the mean average can be used to rank the level of satisfaction from each of the following factors: lower cost, less time consuming, faster delivery, more effective learning, and lower environment impact.

Keywords: ASEAN camp, e-learning, satisfaction, application

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Authors: Jennifer Nutton, Gayle Ployer, Ky Scott, Jenny Morgan

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Working in a circle within higher education creates a decolonial space of mutual respect, responsibility, and reciprocity that facilitates collaborative learning and deep connections among learners and instructors. This approach is beyond simply facilitating a group in a circle but opens the door to creating a sacred space connecting each member to the land, to the Indigenous peoples who have taken care of the lands since time immemorial, to one another, and to one’s own positionality. These deep connections not only center human knowledges and relationships but also acknowledges responsibilities to land. Working in a circle as a relational pedagogical praxis also disrupts institutional power dynamics by creating a space of collaborative learning and deep connections in the classroom. Inherent within circle work is to facilitate connections not just academically but emotionally, physically, culturally, and spiritually. Recent literature supports the use of online talking circles, finding that it can offer a more relational and experiential learning environment, which is often absent in the virtual world and has been made more evident and necessary since the pandemic. These deeper experiences of learning and connection, rooted in both knowledge and the land, can then be shared with openness and vulnerability with one another, facilitating growth and change. This process of beginning with the land is critical to ensure we have the grounding to obstruct the ongoing realities of colonialism. The authors, who identify as both Indigenous and non-Indigenous, as both educators and learners, reflect on their teaching and learning experiences in circle. They share a relational pedagogical praxis framework that has been successful in educating future social workers, environmental activists, and leaders in social and human services, health, legal and political fields.

Keywords: circle work, relational pedagogies, decolonization, distance education

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Authors: Thien Van Ngo

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

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

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Manirath Wongsim

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AIS plays an important role in business management, strategic and can provide assistance in all phases of decision making. Thus, many organisations needs to be seen as well adopting AIS, which is critical to a company in order to organise, manage and operate process in all sections. In order to implement AIS successfully, it is important to understand the underlying factors that influence the AIS adoption. Therefore, this research intends to study this perspective of factors influence and impact on AIS adoption’s success. The model has been designed to illustrate factors influences in AIS adoption. It also attempts to identify the critical success factors that organisations should focus on, to ensure the adoption on accounting process. This framework will be developed from case studies by collecting qualitative and quantitative data. Case study and survey methodology were adopted for this research. Case studies in two Thai- organisations were carried out. The results of the two main case studies suggested 9 factors that may have impact on in AIS adoption. Survey instrument was developed based on the findings from case studies. Two large-scale surveys were sent to selected members of Thailand Accountant, and Thailand Computer Society to further develop and test the research framework. The top three critical factors for ensuring AIS adoption were: top management commitment, steering committees, and Technical capability of AIS personnel. That is, it is now clear which factors impact in AIS adoption, and which of those factors are critical success factors for ensuring AIS adoption successes

Keywords: accounting information system, accounting information systems adoption, and inflecting AIS adoption

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

Abstract:

Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

Procedia PDF Downloads 77

Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

Abstract:

The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

Procedia PDF Downloads 176