Search results for: problem-based learning approach

Authors: Stephanie K. Douglas, Gordon R. Haley

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Organizations face increasing disruptions, changes, and uncertainties through the rapid shifts in the economy and business environment. A capacity for resilience is necessary for organizations to survive and thrive in such adverse conditions. Learning is an essential component of an organization's capability for building resilience. Strategic human resource management is a principal component of learning and organizational resilience. To achieve organizational resilience, human resource management strategies must support individual knowledge, skills, and ability development through organizational learning. This study aimed to contribute to the comprehensive knowledge of the relationship between strategic human resource management and organizational learning to build organizational resilience. The organizational learning dimensions of knowledge acquisition, knowledge distribution, knowledge interpretation, and organizational memory can be fostered through human resource management strategies and then aggregated to the organizational level to build resilience.

Keywords: human resource development, human resource management, organizational learning, organizational resilience

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Authors: Chong Zhang

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There is widespread debate about the nationalistic top-down approach to citizenship education. Employing the notion of cultural citizenship as a useful theoretical lens, citizenship education research tends to focus on the process of subjectivity construction among students’ citizenship learning process. As the Communist Party of China (CPC) plays a dominant role in cultivating citizens through ideological and political education (IaPE) in Chinese universities, the research problem herein focuses on the dynamics and complexity of how Chinese university students construct their subjectivities regarding citizenship learning through IaPE, mediated by the interaction between the state and university teachers. Drawing on questionnaire data from 212 students and interview data from 25 students in one university in China, this paper examines the ways in which students understand and respond to dominant discourses. Its findings reveal there is a deficit of citizenship learning in IaPE, and that students feel ideologically pressurized. From its analysis of social contexts’ influence, the article suggests Chinese higher education students act as either mild changemakers or active self-motivators to enact complex subjectivities, in that they must involve themselves in IaPE for personal academic and career development, yet adopt covert strategies to realise their self-conscious citizenship learning expectations. These strategies take the form of passive and active freedoms, ranging from obediently completing basic curriculum requirements and distancing themselves by studying abroad, to actively searching for learning opportunities from other courses and social media. This paper contributes to the research on citizenship education by recognizing the complexities of how subjectivities are formed in formal university settings.

Keywords: university students, citizenship learning, cultural citizenship, subjectivity, Chinese higher education

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Authors: Soheila Sadeghi

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Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: Maureen Lilian Klos

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In this paper, the notion of autonomy in language teaching and learning is explored with a view to designing particular subject-specific academic literacy at higher education level, for mostly English second or third language learners at the Nelson Mandela University, Port Elizabeth, South Africa. These courses that are contextualized in subject-specific fields studied by students in Arts, Education and Social Science Faculties aim to facilitate learners in the manipulation of cognitively demanding academic texts. However, classroom contact time for these courses is limited to one ninety sessions per week. Thus, learners need to be autonomously responsible for developing their own skills when manipulating and negotiating appropriate academic textual conventions. Thus, a model was designed to allow for gradual learner independence in language learning skills. Learners experience of the model was investigated using the Phenomenological Research Approach. Data in the form of individual written reflections and transcripts of unstructured group interviews were analyzed for themes and sub-themes. These findings are discussed in the article with a view to addressing the practical concerns of the learners in this case study.

Keywords: academic literacies, autonomy, language learning and teaching, subject-specific language

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Horng-Jyh Chen, Yi-Fang Chen, Chien-Liang Lin

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In this paper, the authors try to investigate the determinants of behavioral intention of the blended e-learning course for senior students at the Active Ageing University in Taiwan. Due to lower proficiency in the use of computers and less experience on learning styles of the blended e-learning course for senior students will be expected quite different from those for most young students. After more than five weeks course for two years the questionnaire survey is executed to collect data for statistical analysis in order to understand the determinants of the behavioral intention for senior students. The object of this study is at one of the Active Ageing University in Taiwan total of 84 senior students in the blended e-learning for the ceramics teaching course. The research results show that only the perceived usefulness of the blended e-learning course has significant positive relationship with the behavioral intention.

Keywords: Active Aging University, blended e-learning, ceramics teaching course, behavioral intention

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Authors: Alper Tolga Kumtepe, Erdem Erdogdu, M. Recep Okur, Eda Kaypak, Ozlem Kaya, Serap Ugur, Deniz Dincer, Hakan Yildirim

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Interaction is one of the most important components of open and distance learning. According to Moore, who proposed one of the keystones on interaction types, there are three basic types of interaction: learner-teacher, learner-content, and learner-learner. From these interaction types, learner-content interaction, without doubt, can be identified as the most fundamental one on which all education is based. Efficacy, efficiency, and attraction of open and distance learning systems can be achieved by the practice of effective learner-content interaction. With the development of new technologies, interactive e-learning materials have been commonly used as a resource in open and distance learning, along with the printed books. The intellectual engagement of the learners with the content that is course materials may also affect their satisfaction for the open and distance learning practices in general. Learner satisfaction holds an important place in open and distance learning since it will eventually contribute to the achievement of learning outcomes. Using the learner-content interaction activities in course materials, Anadolu University, by its Open Education system, tries to involve learners in deep and meaningful learning practices. Especially, during the e-learning material design and production processes, identifying appropriate learner-content interaction activities within the context of learning outcomes holds a big importance. Considering the lack of studies adopting this approach, as well as its being a study on the use of e-learning materials in Open Education system, this research holds a big value in open and distance learning literature. In this respect, the present study aimed to investigate a) which learner-content interaction activities included in interactive courses are the most effective in learners’ achievement of verbal information learning outcomes and b) to what extent distance learners are satisfied with these learner-content interaction activities. For this study, the quasi-experimental research design was adopted. The 120 participants of the study were from Anadolu University Open Education Faculty students living in Eskişehir. The students were divided into 6 groups randomly. While 5 of these groups received different learner-content interaction activities as a part of the experiment, the other group served as the control group. The data were collected mainly through two instruments: pre-test and post-test. In addition to those tests, learners’ perceived learning was assessed with an item at the end of the program. The data collected from pre-test and post-test were analyzed by ANOVA, and in the light of the findings of this approximately 24-month study, suggestions for the further design of e-learning materials within the context of learner-content interaction activities will be provided at the conference. The current study is planned to be an antecedent for the following studies that will examine the effects of activities on other learning domains.

Keywords: interaction, distance education, interactivity, online courses

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Authors: Bulatbayeva A. A., Maxutova I. O., Ergalieva A. N.

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This article presents a study of the practice of self-assessment of the quality of training cadets in a military higher specialized educational institution. The research was carried out by means of a questionnaire survey aimed at identifying the degree of satisfaction of cadets with the organization of the educational process, quality of teaching, the quality of the organization of independent work, and the system of their assessment. In general, the results of the study are of an intermediate nature. Proven tools will be incorporated into the planning and effective management of the learning process. The results of the study can be useful for the administrators and managers of the military education system for teachers of military higher educational institutions for adjusting the content and technologies of training future specialists. The publication was prepared as part of applied grant research for 2020-2022 by order of the Ministry of Education and Science of the Republic of Kazakhstan on the topic "Development of a comprehensive methodology for assessing the quality of education of graduates of military special educational institutions."

Keywords: teaching quality, quality satisfaction, learning management, quality management, process approach, classroom learning, interactive technologies, teaching quality

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Authors: Raminder Kaur

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This paper considers the emerging area of integrating Virtual Reality (VR) technologies into the teaching of Visual Anthropology, Research Methods, and the Anthropology of Contemporary India in the University of Sussex. If deployed in a critical and self-reflexive manner, there are several advantages to VR-based immersive learning: (i) Based on data available for British schools, it has been noted that ‘Learning through experience can boost knowledge retention by up to 75%’. (ii) It can tutor students to learn with and from virtual worlds, devising new collaborative methods where suited. (iii) It can foster inclusive learning by aiding students with SEN and disabilities who may not be able to explore such areas in the physical world. (iv) It can inspire and instill confidence in students with anxieties about approaching new subjects, realms, or regions. (v) It augments our provision of ‘smart classrooms’ synchronised to the kinds of emerging immersive learning environments that students come from in schools.

Keywords: virtual reality, anthropology, immersive learning, university

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Authors: Glenn A. Palmer, Lorenzo Bowman, Juanita Johnson-Bailey

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The ubiquitous economic upheaval that has gripped the global environment in the past few years displaced many workers through unemployment or underemployment. Globally, this disruption has caused many adult workers to seek additional education or skills to remain competitive, and acquire the ability and options to find gainful employment. While many learners have availed themselves of some opportunities to be retrained and retooled at locations within their communities, others have explored those options through the online learning environment. This paper examines the empirical research that explores the various strategies that are used in the adult online learning community that could also foster transformative learning.

Keywords: online learning, transformational learning, adult education, economic crisis, unemployment

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Authors: Jasmin Cowin, Amany Alkhayat

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This paper will concentrate on a comparative analysis of both the advantages and limitations of using digital learning resources (DLRs). DLRs covered will be Virtual Reality (VR), Mobile Learning (M-learning) and Computer-Assisted Language Learning (CALL) together with their subset, Mobile Assisted Language Learning (MALL) in language education. In addition, best practices for language teaching and the application of established language teaching methodologies such as Communicative Language Teaching (CLT), the audio-lingual method, or community language learning will be explored. Education has changed dramatically since the eruption of the pandemic. Traditional face-to-face education was disrupted on a global scale. The rise of distance learning brought new digital tools to the forefront, especially web conferencing tools, digital storytelling apps, test authoring tools, and VR platforms. Language educators raced to vet, learn, and implement multiple technology resources suited for language acquisition. Yet, questions remain on how to harness new technologies, digital tools, and their ubiquitous availability while using established methods and methodologies in language learning paired with best teaching practices. In M-learning language, learners employ portable computing devices such as smartphones or tablets. CALL is a language teaching approach using computers and other technologies through presenting, reinforcing, and assessing language materials to be learned or to create environments where teachers and learners can meaningfully interact. In VR, a computer-generated simulation enables learner interaction with a 3D environment via screen, smartphone, or a head mounted display. Research supports that VR for language learning is effective in terms of exploration, communication, engagement, and motivation. Students are able to relate through role play activities, interact with 3D objects and activities such as field trips. VR lends itself to group language exercises in the classroom with target language practice in an immersive, virtual environment. Students, teachers, schools, language institutes, and institutions benefit from specialized support to help them acquire second language proficiency and content knowledge that builds on their cultural and linguistic assets. Through the purposeful application of different language methodologies and teaching approaches, language learners can not only make cultural and linguistic connections in DLRs but also practice grammar drills, play memory games or flourish in authentic settings.

Keywords: language teaching methodologies, computer-assisted language learning, mobile learning, virtual reality

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Authors: Chaima Babi, Said Gadri

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The rapid increase in fake news continues to grow at a very fast rate; this requires implementing efficient techniques that allow testing the re-liability of online content. For that, the current research strives to illuminate the fake news problem using deep learning DL and machine learning ML ap-proaches. We have developed the traditional LSTM (Long short-term memory), and the bidirectional BiLSTM model. A such process is to perform a training task on almost of samples of the dataset, validate the model on a subset called the test set to provide an unbiased evaluation of the final model fit on the training dataset, then compute the accuracy of detecting classifica-tion and comparing the results. For the programming stage, we used Tensor-Flow and Keras libraries on Python to support Graphical Processing Units (GPUs) that are being used for developing deep learning applications.

Keywords: machine learning, deep learning, natural language, fake news, Bi-LSTM, LSTM, multiclass classification

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Authors: Ken Hampshire, Thomas Mazzuchi, Shahram Sarkani

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As a subset of metaheuristics, nature-inspired optimization algorithms such as particle swarm optimization (PSO) have shown promise both in solving intractable problems and in their extensibility to novel problem formulations due to their general approach requiring few assumptions. Unfortunately, single instantiations of algorithms require detailed tuning of parameters and cannot be proven to be best suited to a particular illustrative problem on account of the “no free lunch” (NFL) theorem. Using these algorithms in real-world problems requires exquisite knowledge of the many techniques and is not conducive to reconciling the various approaches to given classes of problems. This research aims to present a unified view of PSO-based approaches from the perspective of relevant systems engineering problems, with the express purpose of then eliciting the best solution for any problem formulation in an ensemble learning bucket of models approach. The central hypothesis of the research is that extending the PSO algorithms found in the literature to real-world optimization problems requires a general ensemble-based method for all problem formulations but a specific implementation and solution for any instance. The main results are a problem-based literature survey and a general method to find more globally optimal solutions for any systems engineering optimization problem.

Keywords: particle swarm optimization, nature-inspired optimization, metaheuristics, systems engineering, ensemble learning

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Authors: R. W. A. V. A. Wijenayake, P. M. R. N. Fernando, S. Nilesh, M. D. G. M. S. Diddeniya, M. Weligodapola, P. Shamila

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The study is designed to examine the impact of a lifelong learning mindset on the career success of accounting and finance professionals in the western province of Sri Lanka. The learning mindset impacts the career success of accounting and finance professionals. The main objective of this study is to identify how the lifelong-learning mindset impacts on the career success of accounting and finance professionals. The lifelong learning mindset is the desire to learn new things and curiosity, resilience, and strategic thinking are the selected constructs to measure the lifelong learning mindset. Career success refers to certain objectives and emotional measures of improvement in one’s work life. The related variables of career success are measured through the number of promotions that have been granted in his/her work life. Positivism is the research paradigm, and the deductive approach is involved as this study relies on testing an existing theory. To conduct the study, the accounting and finance professionals in the western province in Sri Lanka were selected because most reputed international and local companies and specifically, headquarters of most of the companies are in western province. The responses cannot be collected from the whole population. Therefore, this study used a simple random sampling method, and the sample size was 120. Therefore, to identify the impact, 5-point Likert scale is used to perform this quantitative data. Required data gathered through an online questionnaire and the final outputs of the study will offer certain important recommendations to several parties such as universities, undergraduates, companies, and the policymakers to improve, help mentally and financially and motivate the students and the employees to continue their studies without ceasing after completion of their degree.

Keywords: career success, curiosity, lifelong learning mindset, resilience, strategic thinking

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Authors: Onwumere O. J.

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The teaching of stress at any level of education could be a daunting task for the second language teacher because most times, they are bereft of the right approach to use in teaching it even at the fact is that, teaching it. But the fact is that teaching stress even at the middle basic could be interesting if the right approach is employed. To this end, the researcher was of the view that grouping could be a very good strategy to employ in order to sustain the interest of the learner and that the use at drums would be a good way to concretise the teaching of stress at this level. He was able to do this by discussing stress, grouping as a good technique, and the use of drums in teaching stress. To establish the fact that the use of drums would be very effective, four research questions contained in a questionnaire were structured. Three hundred (300) teachers of English in four tertiary institutions, three secondary schools and three primary schools in Nigeria were used. Based on the data analysis and findings, suggestions were given on how teachers and learners could use drums to make the teaching and learning of stress enjoyable for both teachers and learners at the middle basic of education.

Keywords: concretise, grouping, right approach, second language

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Authors: Tarandeep Kaur

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Generation Y and Z nursing students have a much higher need for technology-based stimulation than previous generations, as they may find traditional methods of education boring and disinterested. These generations prefer experiential learning and the use of advanced technology for enhanced learning. Therefore, nursing educators must acquire knowledge to make better use of technology and technological tools for instruction. Millennials and generation are digital natives, optimistic, assertive, want engagement, instant feedback, and collaborative approach. The integration of technology and the efficacy of its use can be challenging for nursing educators. The SAMR (substitution, augmentation, modification, and redefinition) model designed and developed by Dr. Ruben Puentedura can help nursing educators to engage their students in different levels of technology integration for effective learning. Nursing educators should understand that technology use in the classroom must be purposeful. The influx of technology in nursing education is ever-changing; therefore, nursing educators have to constantly enhance and develop technical skills to keep up with the emerging technology in the schools as well as hospitals. In the Saskatchewan Collaborative Bachelor of Nursing (SCBSCN) program at Saskatchewan polytechnic, we use technology at various levels using the SAMR model in our program, including low and high-fidelity simulation labs. We are also exploring futuristic options of using virtual reality and gaming in our classrooms as an innovative way to motivate, increase critical thinking, create active learning, provide immediate feedback, improve student retention and create collaboration.

Keywords: generations, nursing, SAMR, technology

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Authors: Ajda Osifo, Fatma Elshafie

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Mobile-assisted language learning favors social-constructivist and connectivist theories to learning and adaptive approaches to teaching. It offers many opportunities to differentiated instruction in meaningful ways as it enables learners to become more collaborative, engaged and independent through additional dimensions such as web-based media, virtual learning environments, online publishing to an imagined audience and digitally mediated communication. MALL applications can be a tool for the teacher to personalize and adjust instruction according to the learners’ needs and give continuous feedback to improve learning and performance in the process, which support differentiated instruction practices. This paper explores the utilization of Mobile Assisted Language Learning applications as a supporting tool for effective differentiation in the language classroom. It reports overall experience in terms of implementing MALL to shape and apply differentiated instruction and expand learning options. This session is structured in three main parts: first, a review of literature and effective practice of academically responsive instruction will be discussed. Second, samples of differentiated tasks, activities, projects and learner work will be demonstrated with relevant learning outcomes and learners’ survey results. Finally, project findings and conclusions will be given.

Keywords: academically responsive instruction, differentiation, mobile learning, mobile-assisted language learning

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Authors: Paul Koffitse Agobia

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In 2020 the Ministry of Education in Ghana and the National Council for Curriculum and Assessment (NaCCA) rolled out a new curriculum, Common Core Programme (CCP) for Basic 7 to 10, that lays emphasis on character building and values which are important to the Ghanaian society by providing education that will produce character–minded learners, with problem solving skills, who can play active roles in dealing with the increasing challenges facing Ghana and the global society. Therefore, learning and teaching approaches that prioritise the use of digital learning resources and active learning are recommended. The new challenge facing Ghanaian teachers is the ability to use new technologies together with the appropriate content pedagogical knowledge to help learners develop, aside the communication skills in French, the essential 21st century skills as recommended in the new curriculum. This article focusses on the pedagogical approaches that are recommended by NaCCA. The study seeks to examine French language teachers’ understanding of the recommended pedagogical approaches and how they use digital learning resources in class to foster the development of these essential skills and values. 54 respondents, comprised 30 teachers and 24 head teachers, were selected in 6 Junior High schools in rural districts (both private and public) and 6 from Junior High schools in an urban setting. The schools were selected in three regions: Volta, Central and Western regions. A class observation checklist and an interview guide were used to collect data for the study. The study reveals that some teachers adopt teaching techniques that do not promote active learning. They demonstrate little understanding of the core competences and values, therefore, fail to integrate them in their lessons. However, some other teachers, despite their lack of understanding of learning and teaching philosophies, adopted techniques that can help learners develop some of the core competences and values. In most schools, digital learning resources are not utilized, though teachers have smartphones or laptops.

Keywords: active learning, core competences, digital learning resources, pedagogical approach, values.

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Authors: Matteo Calabrese, Martin Cimmino, Dimos Kapetis, Martina Manfrin, Donato Concilio, Giuseppe Toscano, Giovanni Ciandrini, Giancarlo Paccapeli, Gianluca Giarratana, Marco Siciliano, Andrea Forlani, Alberto Carrotta

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In this paper we describe a machine learning methodology for Predictive Maintenance (PdM) applied on woodworking industrial machines. PdM is a prominent strategy consisting of all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the challenges with PdM approach is to design and develop of an embedded smart system to enable the health status of the machine. The proposed approach allows screening simultaneously multiple connected machines, thus providing real-time monitoring that can be adopted with maintenance management. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime of woodworking machines. The effectiveness of the methodology is demonstrated by testing an independent sample of additional woodworking machines without presenting machine down event.

Keywords: predictive maintenance, machine learning, connected machines, artificial intelligence

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Authors: Jalal Maqbool, Gyu Myoung Lee

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Using smart devices for context aware mobile learning is becoming increasingly popular. This has led to mobile learning technology becoming an indispensable part of today’s learning environment and platforms. However, some fundamental issues remain - namely, mobile learning still lacks the ability to truly understand human reaction and user behaviour. This is due to the fact that current mobile learning systems are passive and not aware of learners’ changing contextual situations. They rely on static information about mobile learners. In addition, current mobile learning platforms lack the capability to incorporate dynamic contextual situations into learners’ preferences. Thus, this thesis aims to address these issues highlighted by designing a context aware framework which is able to sense learner’s contextual situations, handle data dynamically, and which can use contextual information to suggest bespoke learning content according to a learner’s preferences. This is to be underpinned by a robust recommendation system, which has the capability to perform these functions, thus providing learners with a truly context-aware mobile learning experience, delivering learning contents using smart devices and adapting to learning preferences as and when it is required. In addition, part of designing an algorithm for the recommendation engine has to be based on learner and application needs, personal characteristics and circumstances, as well as being able to comprehend human cognitive processes which would enable the technology to interact effectively and deliver mobile learning content which is relevant, according to the learner’s contextual situations. The concept of this proposed project is to provide a new method of smart learning, based on a capable recommendation engine for providing an intuitive mobile learning model based on learner actions.

Keywords: aware, context, learning, mobile

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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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

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

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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

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Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

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Authors: Jarek Krajewski, David Daxberger

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We propose a stress detection method based on an RGB camera using heart rate detection, also known as Photoplethysmography Imaging (PPGI). This technique focuses on the measurement of the small changes in skin colour caused by blood perfusion. A stationary lab setting with simulated video conferences is chosen using constant light conditions and a sampling rate of 30 fps. The ground truth measurement of heart rate is conducted with a common PPG system. The proposed approach for pulse peak detection is based on a machine learning-based approach, applying brute force feature extraction for the prediction of heart rate pulses. The statistical analysis showed good agreement (correlation r = .79, p<0.05) between the reference heart rate system and the proposed method. Based on these findings, the proposed method could provide a reliable, low-cost, and contactless way of measuring HR parameters in daily-life environments.

Keywords: heart rate, PPGI, machine learning, brute force feature extraction

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Authors: Wajeehah Aayeshah, Joy Higgs

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The term “transcending pedagogies” has been created to refer to teaching and learning strategies that transcend the mode of student enrolment, the needs of different students, and different learning spaces. The value of such pedagogies in the current arena when learning spaces, technologies and preferences are more volatile than ever before, is a key focus of this paper. The paper will examine current and emerging pedagogies that transcend the learning spaces and enrollment modes of on campus, distance, virtual and workplace learning contexts. A further point of interest is how academics in professional and higher education settings interpret and implement pedagogies in the current global conversation space and re-creation of higher education. This study questioned how the notion and practice of transcending pedagogies enables us to re-imagine and reshape university curricula. It explored the nature of teaching and learning spaces and those professional and higher education (current and emerging) pedagogies that can be implemented across these spaces. We set out to identify how transcending pedagogies can assist students in learning to deal with complexity, uncertainty and change in the practice worlds and better appeal to students who are making decisions on where to enrol. The data for this study was collected through in-depth interviews and focus groups with academics and policy makers within academia.

Keywords: Transcending Pedagogies, teaching and learning strategies, learning spaces, pedagogies

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Authors: Jennifer Fane, Mike Gray, Melissa Sager

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Children with diagnosed or suspected learning disabilities frequently experience significant skill gaps in foundational learning areas such as reading, writing, and math. Remedial one-to-one instruction is a highly effective means of supporting children with learning differences in building these foundational skills and closing the learning gap between them and their same-age peers. However, due to the learning challenges children with learning disabilities face, and ensuing challenges with self-confidence, many children with learning differences struggle with motivation and self-regulation within remedial one-to-one learning environments - despite the benefits of these sessions. Socially Assistive Robots (SARs) are an innovative educational technology tool that has been trialled in a range of educational settings to support diverse learning needs. Yet, little is known about the impact of SARs on the learning of children with learning differences in a one-to-one remedial instructional setting. This study sought to explore the impact of SARs on the engagement of children (n=9) with learning differences attending one-to-one remedial instruction sessions at a non-profit remedial education provider. The study used a mixed-methods design to explore learner engagement during learning tasks both with and without the use of a SAR to investigate how the use of SARs impacts student learning. The study took place over five weeks, with each session within the study followed the same procedure with the SAR acting as a teaching assistant when in use. Data from the study included analysis of time-sample video segments of the instructional sessions, instructor recorded information about the student’s progress towards their session learning goal and student self-reported mood and energy levels before and after the session. Analysis of the findings indicates that the use of SARs resulted in fewer instances of off-task behaviour and less need for instructor re-direction during learning tasks, allowing students to work in more sustained ways towards their learning goals. This initial research indicates that the use of SARs does have a material and measurable impact on learner engagement for children with learning differences and that further exploration of the impact of SARs during one-to-one remedial instruction is warranted.

Keywords: engagement, learning differences, learning disabilities, instruction, social robotics.

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Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

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Authors: Elham Sharifineyestani, Mohammad Farshchin

Abstract:

Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

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Authors: Yi-Hsuan Gloria Lo

Abstract:

The educational objective of vocational high schools (VHSs) is to equip VHS students with practical skills and knowledge that can be applied in the job-related market. However, with the increasing number of technological universities over the past two decades, the majority of VHS students have chosen to receive higher education rather than enter the job market. VHS English education has been confronting a dilemma: Should an English for specific purposes (ESP) approach, which aligns with the educational goal of VHS education, be taken or should an English for general purposes (EGP) approach, which prepares VHS students for advanced studies in universities, be followed? While ESP theorists proposed that that ESP can be taught to secondary learners, little was known about VHS students’ perspective on this ESP-versus-EGP dilemma. Scant research has investigated different facets of students’ needs (necessities, wants, and lacks) for both ESP and EGP in terms of the four language skills and the factors that contribute to any differences. To address the gap in the literature, 100 VHS students responded to statements related to their necessities, wants, and lacks in learning ESP and EGP on a 6-point Likert scale. Six VHS students were interviewed to tap into the reasons for different facets of the needs for learning EGP and ESP. The statistical analysis indicates that at this stage of learning English, VHS subjects believed that EGP was more necessary than ESP; EGP was more desirable than ESP. However, they reported that they were more lacking in ESP than in EGP learning. Regarding EGP, the results show that the VHS subjects rated speaking as their most necessary skill, speaking as the most desirable skill, and writing as the most lacking skill. A significant difference was found between perceived learning necessities and lacks and between perceived wants and lacks. No statistical difference was found between necessities and wants. In the aspect of ESP, the results indicate that the VHS subjects marked reading as their most necessary skill, speaking as the most desirable skill, and writing as the most lacking skill. A significant difference exists between their perceived necessities and lacks and between their wants and lacks. However, there is no statistically significant difference between their perceived lacks and wants. Despite the lack of a significant difference between learning necessities and wants, the qualitative interview data reveal that the reasons for their perceived necessities and wants were different. The findings of the study confirm previous research that demonstrates that ‘needs’ is a multiple and conflicting construct. What VHS students felt most lacking was not necessarily what they believed they should learn or would like to learn. Although no statistical difference was found, different reasons were attributed to their perceived necessities and wants. Both theoretical and practical implications have been drawn and discussed for ESP research in general and teaching ESP in VHSs in particular.

Keywords: vocational high schools (VHSs), English for General Purposes (EGP), English for Specific Purposes (ESP), needs analysis

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Authors: Sara Fallahi

Abstract:

This paper reviews the importance of content familiarity of receptive skills and offers solutions to the issue of content unfamiliarity in language learning materials. Presently, language learning materials are mainly comprised of global issues and target language speakers’ culture(s) in receptive skills. This might leadlearners to focus on content rather than the language. As a solution, materials on receptive skills can be developed with a focus on learners’culture and social concerns, especially in the beginner levels of learning. Language learners often learn their target language through the receptive skills of listening and reading before language production ensues through speaking and writing. Students’ journey from receptive skills to productive skills is mainly concentrated on by teachers. There are barriers to language learning, such as time and energy, that can hinder learners’ understanding and ability to build the required background knowledge of the content. This is generated due to learners’ unfamiliarity with the skill’s content. Therefore, materials that improve content familiarity will help learners improve their language comprehension, learning, and usage. This presentation will conclude with practical solutions to help teachers and learners more authentically integrate language and culture to elevate language learning.

Keywords: language learning, listening content, reading content, content familiarity, ESL books, language learning books, cultural familiarity

Procedia PDF Downloads 109