Search results for: self-regulated learning theory

Authors: Chandra Mukherjee

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The prevalent knowledge of the biological systems is based on the standard scientific perception of natural equilibrium, determination and predictability. Recently, a rethinking of concepts was presented and a new scientific perspective emerged that involves complexity theory with deterministic chaos theory, nonlinear dynamics and theory of fractals. The unpredictability of the chaotic processes probably would change our understanding of diseases and their management. The mathematical definition of chaos is defined by deterministic behavior with irregular patterns that obey mathematical equations which are critically dependent on initial conditions. The chaos theory is the branch of sciences with an interest in nonlinear dynamics, fractals, bifurcations, periodic oscillations and complexity. Recently, the biomedical interest for this scientific field made these mathematical concepts available to medical researchers and practitioners. Any biological network system is considered to have a nominal state, which is recognized as a homeostatic state. In reality, the different physiological systems are not under normal conditions in a stable state of homeostatic balance, but they are in a dynamically stable state with a chaotic behavior and complexity. Biological systems like heart rhythm and brain electrical activity are dynamical systems that can be classified as chaotic systems with sensitive dependence on initial conditions. In biological systems, the state of a disease is characterized by a loss of the complexity and chaotic behavior, and by the presence of pathological periodicity and regulatory behavior. The failure or the collapse of nonlinear dynamics is an indication of disease rather than a characteristic of health.

Keywords: HRV, HRVI, LF, HF, DII

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Authors: Gurli Olsen, Ida Bruheim Jensen

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Internationally, many young adults not in education, employment, or training (NEET) remain in temporary solutions such as labour market measures or other forms of welfare arrangements. These trends have been associated with ineffective labour market measures, an underfunded theoretical foundation for activation work, limited competence among social workers and labour market employees in using ordinary workplaces as job inclusion measures, and an overemphasis on young adults’ personal limitations such as health challenges and lack of motivation. Two competing models have been prominent in activation work: Place‐Then‐Train and Train‐Then‐Place. A traditional strategy for labour market measures has been to first motivate NEETs to sheltered work and training and then to the regular labour market (train then place). Measures such as Supported Employment (SE) and Individual Placement and Support (IPS) advocate for rapid entry into paid work at the regular labour market with close supervision and training from social workers, employees, and others (place then train). None of these models demonstrate unquestionable results. In this web of working life measures, young adults (NEETs) experience a lack of confidence in their own capabilities and coping strategies vis-á-vis labour market- and educational demands. Drawing on young adults’ own experiences, we argue that the Job Demands-Resources (JD-R) Theory can contribute to the theoretical and practical dimensions of activation work. This presentation will focus on what the JD-R theory entails and how it can be fruitful in activation work with NEETs (what and how). The overarching rationale of the JD-R theory is that an enduring balance between demands (e.g., deadlines, working hours) and resources (e.g., social support, enjoyable work tasks) is important for job performance for people in any job and potentially in other meaningful activities. Extensive research has demonstrated that a balance between demands and resources increases motivation and decreases stress. Nevertheless, we have not identified literature on the JD-R theory in activation work with young adults.

Keywords: activation work, job demands-resources theory, social work, theory development

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: Gloria Chen

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When offering conventional level-appropriate ESL classes for adults is not feasible, a multilevel adult ESL class can be formed to benefit those who need to learn English for daily function. This paper examines the rationale, the process, the contents, and the outcomes of a multilevel ESL class for adults. The action research discusses a variety of assessments, lesson plans, teaching strategies that facilitate lifelong language learning. In small towns where adult ESL learners are only a handful, often advanced students and inexperienced students have to be placed in one class. Such class might not be viewed as desirable, but with on-going assessments, careful lesson plans, and purposeful strategies, a multilevel ESL class for adults can overcome the obstacles and help learners to reach a higher level of English proficiency. This research explores some hand-on strategies, such as group rotating, cooperative learning, and modifying textbook contents for practical purpose, and evaluate their effectiveness. The data collected in this research include Needs Assessment (beginning of class term), Mid-term Self-Assessment (5 months into class term), End-of-term Student Reflection (10 months into class), and End-of-term Assessment from the Instructor (10 months into class). A descriptive analysis of the data explains the practice of this particular learning community, and reveal the areas for improvement and enrichment. This research answers the following questions: (1) How do the assessments positively help both learners and instructors? (2) How do the learning strategies prepare students to become independent, life-long English learners? (3) How do materials, grouping, and class schedule enhance the learning? The result of the research contributes to the field of teaching and learning in language, not limited in English, by (a) examining strategies of conducting a multilevel adult class, (b) involving adult language learners with various backgrounds and learning styles for reflection and feedback, and (c) improving teaching and learning strategies upon research methods and results. One unique feature of this research is how students can work together with the instructor to form a learning community, seeking and exploring resources available to them, to become lifelong language learners.

Keywords: adult language learning, assessment, multilevel, teaching strategies

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Authors: Akunna Agunwah, Kevin Gallimore, Kathryn Kinmond

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Entrepreneurship is widely associated and seen as a vehicle for economic growth; however, different scholars have studied entrepreneurship from various perspectives, resulting in multiple definitions. It is surprising to know most entrepreneurship definition does not incorporate growth as part of their definition of entrepreneurship. Economic growth is engineered by the activities of the entrepreneurs. The purpose of the present theoretical study is to explore the working practices of the successful entrepreneurs towards achieving business growth by understanding the experiences of the entrepreneur using the Theory of Planned Behaviour (TPB) as a lens. Ten successful entrepreneurs in the North West of England in various business sectors were interviewed using semi-structured interview method. The recorded audio interviews transcribed and subsequently evaluated using the thematic deductive technique (qualitative approach). The themes were examined using Theory of Planned Behaviour to ascertain the presence of the three intentional antecedents (attitude, subjective norms, and perceived behavioural control). The findings categorised in two folds, firstly, it was observed that the three intentional antecedents, which make up Theory of Planned Behaviour were evident in the transcript. Secondly, the entrepreneurs are most concerned with achieving a state of freedom and realising their visions and ambitions. Nevertheless, the entrepreneur employed these intentional antecedents to enhance business growth. In conclusion, the work presented here showed a novel way of understanding the working practices and experiences of the entrepreneur using the theory of planned behaviour in qualitative approach towards enhancing business growth. There exist few qualitative studies in entrepreneurship research. In addition, this work applies a novel approach to studying the experience of the entrepreneurs by examining the working practices of the successful entrepreneurs in the North-West England through the lens of the theory of planned behaviour. Given the findings regarding TPB as a lens in the study, the entrepreneur does not differentiate between the categories of the antecedents reasonably sees them as processes that can be utilised to enhance business growth.

Keywords: business growth, experience, interpretive, theory of planned behaviour

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Authors: Aabha Chaubey, Bani Bhattacharya

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Evaluation of the quality of teaching in engineering education in India needs to be conducted on a continuous basis to achieve the best teaching quality in technical education. Quality teaching is an influential factor in technical education which impacts largely on learning outcomes of the students. Present study is not exclusively theory-driven, but it draws on various specific concepts and constructs in the domain of technical education. These include teaching and learning in higher education, teacher effectiveness, and teacher evaluation and performance management in higher education. Student Evaluation of Education Quality (SEEQ) was proposed as one of the evaluation instruments of the quality teaching in engineering education. SEEQ is one of the popular and standard instrument widely utilized all over the world and bears the validity and reliability in educational world. The present study was designed to evaluate the teaching quality through SEEQ in the context of technical education in India, including its validity and reliability based on the collected data. The multiple dimensionality of SEEQ that is present in every teaching and learning process made it quite suitable to collect the feedback of students regarding the quality of instructions and instructor. The SEEQ comprises of 9 original constructs i.e.; learning value, teacher enthusiasm, organization, group interaction, and individual rapport, breadth of coverage, assessment, assignments and overall rating of particular course and instructor with total of 33 items. In the present study, a total of 350 samples comprising first year undergraduate students from Indian Institute of Technology, Kharagpur (IIT, Kharagpur, India) were included for the evaluation of the importance of SEEQ. They belonged to four different courses of different streams of engineering studies. The above studies depicted the validity and reliability of SEEQ was based upon the collected data. This further needs Confirmatory Factor Analysis (CFA) and Analysis of Moment structure (AMOS) for various scaled instrument like SEEQ Cronbach’s alpha which are associated with SPSS for the examination of the internal consistency. The evaluation of the effectiveness of SEEQ in CFA is implemented on the basis of fit indices such as CMIN/df, CFI, GFI, AGFI and RMSEA readings. The major findings of this study showed the fitness indices such as ChiSq = 993.664,df = 390,ChiSq/df = 2.548,GFI = 0.782,AGFI = 0.736,CFI = 0.848,RMSEA = 0.062,TLI = 0.945,RMR = 0.029,PCLOSE = 0.006. The final analysis of the fit indices presented positive construct validity and stability, on the other hand a higher reliability was also depicted which indicated towards internal consistency. Thus, the study suggests the effectivity of SEEQ as the indicator of the quality evaluation instrument in teaching-learning process in engineering education in India. Therefore, it is expected that with the continuation of this research in engineering education there remains a possibility towards the betterment of the quality of the technical education in India. It is also expected that this study will provide an empirical and theoretical logic towards locating a construct or factor related to teaching, which has the greatest impact on teaching and learning process in a particular course or stream in engineering education.

Keywords: confirmatory factor analysis, engineering education, SEEQ, teaching and learning process

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Authors: Elvira Wahyuni

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This study is a classroom action research. The general objective of this study was to find out students’ speaking ability through teaching English by using video and to find out the effectiveness of using video in teaching English to improve students’ speaking ability. The subjects of this study were 34 of the first-year students of SMAN 12 Pekanbaru who were learning English as a foreign language (EFL). Students were given pre-test before the treatment and post-test after the treatment. Quantitative data was collected by using speaking test requiring the students to respond to the recorded questions. Qualitative data was collected through observation sheets and field notes. The research finding reveals that there is a significant improvement of the students’ speaking ability through the use of video in speaking class. The qualitative data gave a description and additional information about the learning process done by the students. The research findings indicate that the use of video in teaching and learning is good in increasing learning outcome.

Keywords: English teaching, fun learning, speaking ability, video

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Authors: Bezhan Ghvaberidze

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A fuzzy multi-objective facility location-selection problem (FLSP) under uncertain and extreme environments based on possibility theory is developed. The model’s uncertain parameters in the q-rung orthopair fuzzy values are presented and transformed in the Dempster-Shaper’s belief structure environment. An objective function – distribution centers’ selection ranking index as an extension of Dempster’s extremal expectations under discrimination q-rung orthopair fuzzy information is constructed. Experts evaluate each humanitarian aid from distribution centers (HADC) against each of the uncertain factors. HADCs location problem is reduced to the bicriteria problem of partitioning the set of customers by the set of centers: (1) – Minimization of transportation costs; (2) – Maximization of centers’ selection ranking indexes. Partitioning type constraints are also constructed. For an illustration of the obtained results, a numerical example is created from the facility location-selection problem.

Keywords: FLSP, multi-objective combinatorial optimization problem, evidence theory, HADC, q-rung orthopair fuzzy set, possibility theory

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Authors: Zainab Zendana Shafii

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Lullabies have been broadly defined as songs that are sung to calm and soothe children. While this is true, this paper intends to show that lullabies exceed these functions. The paper, in exploring Nupe lullabies, examines the various functions that lullabies perform in terms of language development, cultural enrichment and also the retelling of history as it relates to the culture of the Nupe people of northern Nigeria. The theoretical framework used is the functionalist theory. This theory postulates that all cultural or social phenomena have a positive function and that all are indispensable. The functionalist theory is based on the premise that all aspects of a society—institutions, roles, norms, etc.—serve a purpose and that all are indispensable for the long-term survival of the society. To this end, this paper dissects the various lullabies in Nupeland with a view to exploring the meaning that these songs generate and why they are even sung at all. The qualitative research methodology has been used to gather materials.

Keywords: Nupe, lullabies, Nigeria, northern

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: Yong Wook Kim

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The diversity of students in regular classrooms is increasing due to expand inclusive education and increase multicultural students in South Korea. In this diverse classroom environment, the universal design for learning (UDL) has been proposed as a way to meet both the educational need and social expectation of student achievement. UDL offers a variety of practical teaching methods, one of which is a differentiating instruction. The differentiating instruction has been pointed out resource limitation, organizational resistance, and lacks easy-to-implement framework. However, through the framework provided by the UDL, differentiating instruction is able to be flexible in their implementation. In practice, the UDL and differentiating instruction are complementary, but there is still a lack of research that suggests specific implementation methods that apply both concepts at the same time. This study was conducted to investigate the effects of differentiating instruction strategies according to learner characteristics (readiness, interest, learning profile), components of differentiating instruction (content, process, performance, learning environment), especially UDL principles (representation, behavior and expression, participation) existed in differentiating instruction, and implementation of UDL-based differentiating instruction through the Planning for All Learner (PAL) and UDL Lesson Plan Cycle. It is meaningful that such a series of studies can enhance the possibility of more concrete and realistic UDL-based teaching and learning strategies in the classroom, especially in inclusive settings.

Keywords: universal design for learning, differentiating instruction, UDL lesson plan, PAL

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Authors: Christoph Ostermair

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We deal with the effect of correlation between prospects on human decision making under uncertainty as proposed by the comparatively new and promising model of “salience theory of choice under risk”. In this regard, we show that the theory entails the prediction that the inconsistency of choices, known as the Allais paradox, should not be an issue in the context of “real-life decision making”, which typically corresponds to situations of subjective uncertainty. The Allais paradox, probably the best-known anomaly regarding expected utility theory, would then essentially have no practical relevance. If, however, empiricism contradicts this prediction, salience theory might suffer a serious setback. Explanations of the model for variable human choice behavior are mostly the result of a particular mechanism that does not come to play under perfect correlation. Hence, if it turns out that correlation between prospects – as typically found in real-world applications – does not influence human decision making in the expected way, this might to a large extent cost the theory its explanatory power. The empirical literature regarding the Allais paradox under subjective uncertainty is so far rather moderate. Beyond that, the results are hard to maintain as an argument, as the presentation formats commonly employed, supposably have generated so-called event-splitting effects, thereby distorting subjects’ choice behavior. In our own incentivized experimental study, we control for such effects by means of two different choice settings. We find significant event-splitting effects in both settings, thereby supporting the suspicion that the so far existing empirical results related to Allais paradoxes under subjective uncertainty may not be able to answer the question at hand. Nevertheless, we find that the basic tendency behind the Allais paradox, which is a particular switch of the preference relation due to a modified common consequence, shared by two prospects, is still existent both under an event-splitting and a coalesced presentation format. Yet, the modal choice pattern is in line with the prediction of salience theory. As a consequence, the effect of correlation, as proposed by the model, might - if anything - only weaken the systematic choice pattern behind the Allais paradox.

Keywords: Allais paradox, common consequence effect, models of decision making under risk and uncertainty, salience theory

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Authors: Laura Zizka, Gaby Probst

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The Covid-19 pandemic has affected the way Higher Education Institutions (HEIs) have given their courses. From emergency remote where all students and faculty were immediately confined to home teaching and learning, the continuing evolving sanitary situation obliged HEIs to adopt other methods of teaching and learning from blended courses that included both synchronous and asynchronous courses and activities to hy-flex models where some students were on campus while others followed the course simultaneously online. Each semester brought new challenges for HEIs and, subsequently, additional emotional reactions. This paper investigates the affective side of teaching and learning in various online modalities and its toll on students and faculty members over the past three semesters. The findings confirm that students and faculty who have more self-efficacy, flexibility, and resilience reported positive emotions and embraced the opportunities that these past semesters have offered. While HEIs have begun a new semester in an attempt to return to ‘normal’ face-to-face courses, this paper posits that there are lessons to be learned from these past three semesters. The opportunities that arose from the challenge of the pandemic should be considered when moving forward by focusing on a greater emphasis on the affective aspect of teaching and learning in HEIs worldwide.

Keywords: effective teaching and learning, higher education, engagement, interaction, motivation

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Authors: Estevan B. Costa, Armando M. Toda, Marcell A. A. Mesquita, Jacques D. Brancher

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The advances in technology in the last five years allowed an improvement in the educational area, as the increasing in the development of educational software. One of the techniques that emerged in this lapse is called Gamification, which is the utilization of video game mechanics outside its bounds. Recent studies involving this technique provided positive results in the application of these concepts in many areas as marketing, health and education. In the last area there are studies that cover from elementary to higher education, with many variations to adequate to the educators methodologies. Among higher education, focusing on IT courses, data structures are an important subject taught in many of these courses, as they are base for many systems. Based on the exposed this paper exposes the development of an interactive web learning environment, called DSLEP (Data Structure Learning Platform), to aid students in higher education IT courses. The system includes basic concepts seen on this subject such as stacks, queues, lists, arrays, trees and was implemented to ease the insertion of new structures. It was also implemented with gamification concepts, such as points, levels, and leader boards, to engage students in the search for knowledge and stimulate self-learning.

Keywords: gamification, Interactive learning environment, data structures, e-learning

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Authors: Jenjira Chinnawong, Phusit Phukamchanoad

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The objective of this study is to explore the guidelines for the development of community classroom for research and academic services in Ranong province. By interviewing leaders involved in the development of learning resources, research, and community services, it was found that the leaders' perceptions in the development of learning resources, research, and community services in Ranong, was at the highest level. They perceived at every step on policies of community classroom implementation, research, and community services in Ranong. Leaders' perceptions were at the moderate level in terms of analysis of problems related to procedures of community classroom management, research and community services in Ranong especially in the planning and implementation of the examination, improvement, and development of learning sources to be in good condition and ready to serve the visitors. Their participation in the development of community classroom, research, and community services in Ranong was at a high level, particularly in the participation in monitoring and evaluation of the development of learning resources as well as in reporting on the result of the development of learning resources. The most important thing in the development of community classroom, research and community services in Ranong is the necessity to integrate the three principles of knowledge building in teaching, research and academic services in order to create the identity of the local and community classroom for those who are interested to visit to learn more about the useful knowledge. As a result, community classroom, research, and community services were well-known both inside and outside the university.

Keywords: community classroom, learning resources, development, participation

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Authors: H. Afsari, H. Shokouhmand

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When stack is placed in a thermoacoustic resonator in a cryocooler, one extremity of the stack heats up while the other cools down due to the thermoacoustic effect. In the present, with expression a formula by linear theory, will see this temperature difference depends on what factors. The computed temperature difference is compared to the one predicted by the formula. These discrepancies can not be attributed to non-linear effects, rather they exist because of thermal effects. Two correction factors are introduced for close up results among linear theory and computed and use these correction factors to modified linear theory. In fact, this formula, is optimized by GA (Genetic Algorithm). Finally, results are shown at different Mach numbers and stack location in resonator.

Keywords: heat transfer, thermoacoustic cryocooler, stack, resonator, mach number, genetic algorithm

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Authors: Ruthanne Tobin

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The shift to a personalized learning (PL) curriculum in Canada represents an innovative approach to teaching and learning that is also evident in various initiatives across the 32-nation OECD. The premise behind PL is that empowering individual learners to have more input into how they access and construct knowledge, and express their understanding of it, will result in more meaningful school experiences and academic success. In this paper presentation, the author reports on a document analysis of the new curriculum in the province of British Columbia. Three theoretical frameworks are used to analyze the new curriculum. Framework 1 focuses on five dominant aspects (FDA) of PL at the classroom level. Framework 2 focuses on conceptualizing and enacting personalized learning (CEPL) within three spheres of influence. Framework 3 focuses on the integration of three types of knowledge (content, technological, and pedagogical). Analysis is ongoing, but preliminary findings suggest that the new curriculum addresses framework 1 quite well, which identifies five areas of personalized learning: 1) assessment for learning; 2) effective teaching and learning; 3) curriculum entitlement (choice); 4) school organization; and 5) “beyond the classroom walls” (learning in the community). Framework 2 appears to be less well developed in the new curriculum. This framework speaks to the dynamics of PL within three spheres of interaction: 1) nested agency, comprised of overarching constraints [and enablers] from policy makers, school administrators and community; 2) relational agency, which refers to a capacity for professionals to develop a network of expertise to serve shared goals; and 3) students’ personalized learning experience, which integrates differentiation with self-regulation strategies. Framework 3 appears to be well executed in the new PL curriculum, as it employs the theoretical model of technological, pedagogical content knowledge (TPACK) in which there are three interdependent bodies of knowledge. Notable within this framework is the emphasis on the pairing of technologies with excellent pedagogies to significantly assist students and teachers. This work will be of high relevance to educators interested in innovative school reform.

Keywords: curriculum reform, K-12 school change, innovations in education, personalized learning

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Cemil Akcay, Goksun Yerlikaya

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Buildings are the living spaces of people with an active role in every aspect of life in today's world. While some structures have survived from the early ages, most of the buildings that completed their lifetime have not transported to the present day. Nowadays, buildings that do not meet the social, economic, and safety requirements of the age return to life with a transformation process. This transformation is called urban transformation. Urban transformation is the renewal of the areas with a risk of disaster and the technological infrastructure required by the structure. The transformation aims to prevent damage to earthquakes and other disasters by rebuilding buildings that have completed their non-earthquake-resistant economic life. It is essential to decide on other issues related to conversion and transformation in places where most of the building stock should transform into the first-degree earthquake belt, such as Istanbul. In urban transformation, property owners, local authority, and contractor must deal at a common point. Considering that hundreds of thousands of property owners are sometimes in the areas of transformation, it is evident how difficult it is to make the deal and decide. For the optimization of these decisions, the use of game theory is foreseeing. The main problem in this study is that the urban transformation is carried out in place, or the building or buildings are transport to a different location. There are many stakeholders in the Istanbul University Cerrahpaşa Medical Faculty Campus, which is planned to be carried out in the process of urban transformation, was tried to solve the game theory applications. An analysis of the decisions given on a real urban transformation project and the logical suitability of decisions taken without the use of game theory were also supervised using game theory. In each step of this study, many decision-makers are classifying according to a specific logical sequence, and in the game trees that emerged as a result of this classification, Nash balances were tried to observe, and optimum decisions were determined. All decisions taken for this project have been subjected to two significant differentiated comparisons using game theory, and as decisions are taken without the use of game theory, and according to the results, solutions for the decision phase of the urban transformation process introduced. The game theory model developed from beginning to the end of the urban transformation process, particularly as a solution to the difficulty of making rational decisions in large-scale projects with many participants in the decision-making process. The use of a decision-making mechanism can provide an optimum answer to the demands of the stakeholders. In today's world for the construction sector, it is also seeing that the game theory is a non-surprising consequence of the fact that it is the most critical issues of planning and making the right decision in future years.

Keywords: urban transformation, the game theory, decision making, multi-actor project

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Sally Heyeon Hwang

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Within decision theory, there are normative principles that dictate how one should act in addition to empirical theories of actual behavior. As a normative guide to one’s actual behavior, evidential or causal decision-theoretic equations allow one to identify outcomes with maximal utility values. The choice that each person makes, however, will, of course, differ according to varying assignments of weight and probability values. Regarding these different choices, it remains a subject of considerable philosophical controversy whether individual subjects have the capacity to exercise free will with respect to the assignment of probabilities, or whether instead the assignment is in some way constrained. A version of this question is given a precise form in Richard Jeffrey’s assumption that free will is necessary for Newcomb’s paradox to count as a decision problem. This paper will argue, against Jeffrey, that decision theory does not require the assumption of libertarian freedom. One of the hallmarks of decision-making is its application across a wide variety of contexts; the implications of a background assumption of free will is similarly varied. One constant across the contexts of decision is that there are always at least two levels of choice for a given agent, depending on the degree of prior constraint. Within the context of Newcomb’s problem, when the predictor is attempting to guess the choice the agent will make, he or she is analyzing the determined aspects of the agent such as past characteristics, experiences, and knowledge. On the other hand, as David Lewis’ backtracking argument concerning the relationship between past and present events brings to light, there are similarly varied ways in which the past can actually be dependent on the present. One implication of this argument is that even in deterministic settings, an agent can have more free will than it may seem. This paper will thus argue against the view that a stable background assumption of free will or determinism in decision theory is necessary, arguing instead for a compatibilist decision theory yielding a novel treatment of Newcomb’s problem.

Keywords: decision theory, compatibilism, free will, Newcomb’s problem

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Authors: Diego Liberati

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Standard molecular modeling is traditionally done through Schroedinger equations via the help of powerful tools helping to manage them atom by atom, often needing High Performance Computing. Here, a full portfolio of new tools, conjugating statistical inference in the so called eXplainable Artificial Intelligence framework (in the form of Machine Learning of understandable rules) to the more traditional modeling and simulation control theory of mixed dynamic logic hybrid processes, is offered as quite a general purpose even if making an example to a popular chemical physics set of problems.

Keywords: understandable rules ML, k-means, PCA, PieceWise Affine Auto Regression with eXogenous input

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Authors: Taghreed Alghamdi, Wendy Hall, David Millard

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Massive Open Online Courses (MOOCs) have recently gained widespread interest in the academic world, starting a wide range of discussion of a number of issues. One of these issues, using MOOCs in teaching and learning in the higher education by integrating MOOCs’ contents with traditional face-to-face activities in blended learning format, is called blended MOOCs (bMOOCs) and is intended not to replace traditional learning but to enhance students learning. Most research on MOOCs has focused on students’ perception and institutional threats whereas there is a lack of published research on academics’ experiences and practices. Thus, the first aim of the study is to develop a classification of blended MOOCs models by conducting a systematic literature review, classifying 19 different case studies, and identifying the broad types of bMOOCs models namely: Supplementary Model and Integrated Model. Thus, the analyses phase will emphasize on these different types of bMOOCs models in terms of adopting MOOCs by lecturers. The second aim of the study is to improve the understanding of lecturers’ acceptance of bMOOCs by investigate the factors that influence academics’ acceptance of using MOOCs in traditional learning by distributing an online survey to lecturers who participate in MOOCs platforms. These factors can help institutions to encourage their lecturers to integrate MOOCs with their traditional courses in universities.

Keywords: acceptance, blended learning, blended MOOCs, higher education, lecturers, MOOCs, professors

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Authors: S. Alfalah, J. Falah, T. Alfalah, M. Elfalah, O. Falah

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The field of education is an ever-evolving area constantly enriched by newly discovered techniques provided by active research in all areas of technologies. The recent years have witnessed the introduction of a number of promising technologies and applications to enhance the teaching and learning experience. Virtual Reality (VR) applications are considered one of the evolving methods that have contributed to enhancing education in many fields. VR creates an artificial environment, using computer hardware and software, which is similar to the real world. This simulation provides a solution to improve the delivery of materials, which facilitates the teaching process by providing a useful aid to instructors, and enhances the learning experience by providing a beneficial learning aid. In order to assure future utilization of such systems, students’ perceptions were examined toward utilizing VR as an educational tool in the Faculty of Information Technology (IT) in The University of Jordan. A questionnaire was administered to IT undergraduates investigating students’ opinions about the potential opportunities that VR technology could offer and its implications as learning and teaching aid. The results confirmed the end users’ willingness to adopt VR systems as a learning aid. The result of this research forms a solid base for investing in a VR system for IT education.

Keywords: information, technology, virtual reality, education

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Anita Yus

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Several Elementary Schools (SD) ‘favored’ by parents, especially those live in big cities in Indonesia, implicitly demand each child enrolled in the first grade of SD to be able to read, write and calculate. This condition urges the parents to push the teachers in PAUD (Kindergarten) to train their children to read, write, and calculate so they have a set of knowledge. According to Piaget, each child is capable of acquiring knowledge when he is given the opportunity to interact with his environment (things, people, and atmosphere). Teachers can make the interaction occur. There are several learning approaches suitable for the characteristics and needs of child’s growth. This paper talks about a research result conducted to investigate how twelve teachers of early childhood program comprehend the constructivist theory of Piaget, and how they inquire, how the children acquire and construct a number of knowledge through occurred interactions. This is a qualitative research with an observation method followed up by a focus group discussion (FGD). The research result shows that there is a reciprocal interaction between the behaviors of teachers and children affected by the size of the classroom and learning source, teaching experiences, education background, teachers’ attitude and motivation, as well as the way the teachers interpret and support the children’s needs. The teachers involved in this research came up with varied perspective on how knowledge acquired by children at first and how they construct it. This research brings a new perspective in understanding children as scientists.

Keywords: constructivist approach, young children as a scientist, teacher practice, teacher education

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Mohammad Abdullah-Al-Wadud

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In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.

Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty

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Authors: Babak Safaei, Ahmad Ghanbari, Arash Rahmani

Abstract:

In the present study, the free vibration characteristics of double-walled carbon nanotubes (DWCNTs) are investigated. The small-scale effects are taken into account using the Eringen’s nonlocal elasticity theory. The nonlocal elasticity equations are implemented into the different classical beam theories namely as Euler-Bernoulli beam theory (EBT), Timoshenko beam theory (TBT), Reddy beam theory (RBT), and Levinson beam theory (LBT) to analyze the free vibrations of DWCNTs in which each wall of the nanotubes is considered as individual beam with van der Waals interaction forces. Generalized differential quadrature (GDQ) method is utilized to discretize the governing differential equations of each nonlocal beam model along with four commonly used boundary conditions. Then molecular dynamics (MD) simulation is performed for a series of armchair and zigzag DWCNTs with different aspect ratios and boundary conditions, the results of which are matched with those of nonlocal beam models to extract the appropriate values of the nonlocal parameter corresponding to each type of chirality, nonlocal beam model and boundary condition. It is found that the present nonlocal beam models with their proposed correct values of nonlocal parameter have good capability to predict the vibrational behavior of DWCNTs, especially for higher aspect ratios.

Keywords: double-walled carbon nanotubes, nonlocal continuum elasticity, free vibrations, molecular dynamics simulation, generalized differential quadrature method

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Authors: Bryony Pound

Abstract:

This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.

Keywords: benefits, greenspace, learning, restoration

Procedia PDF Downloads 127