Search results for: learning with happiness

Authors: Sumalee Tientongdee

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In this study, it was aimed to investigate Physics achievement of the pre-service science teacher studying in general science program at Suan Sunandha Rajabhat University, Bangkok, Thailand. The program has provided the new curriculum that focuses on 21st-century skills development. Active learning approaches are used to teach in all subjects. One of the active learning approaches Peer Instruction, or PI was used in this study to teach physics of waves as a compulsory course. It was conducted in the second semester from January to May of 2017. The concept test was given to evaluate pre-service science teachers’ understanding in concept of waves. Problem-solving assessment form was used to evaluate their problem-solving skill. The results indicated that after they had learned through Peer Instruction in physics of waves course, their concepts in physics of waves was significantly higher at 0.05 confident levels. Their problem-solving skill from the whole class was at the highest level. Based on the group interview on the opinions of using Peer Instruction in Physics class, they mostly felt that it was very useful and helping them understand more about physics, especially for female students.

Keywords: peer instruction, physics of waves, pre-service science teacher, Suan Sunandha Rajabhat university

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Authors: Yong Chen

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To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules.

Keywords: sc/snRNA-seq, memory formation, deep learning, gene module, causal inference

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Authors: Siyao Zhu, Yifang Xu

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After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. The hands-free requirement from the first responders excludes the use of tedious manual control and operation. In unknown, unstructured, and obstructed environments, natural-language-based supervision is not amenable for first responders to formulate, and is difficult for robots to understand. Brain-computer interface is a promising option to overcome the limitations. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: consensus assessment, electroencephalogram, emergency response, human-robot collaboration, intention recognition, search and rescue

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Authors: Jaya Bishnu Pradhan

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Weaving mat is one of the common activities performed in different community generally in the rural part of Nepal. Mat weavers’ practice mathematical ideas and concepts implicitly in order to perform their job. This study is intended to uncover the mathematical ideas embedded in mat weaving that can help teachers and students for the teaching and learning of school geometry. The ethnographic methodology was used to uncover and describe the beliefs, values, understanding, perceptions, and attitudes of the mat weavers towards mathematical ideas and concepts in the process of mat weaving. A total of 4 mat weavers, two mathematics teachers and 12 students from grade level 6-8, who are used to participate in weaving, were selected for the study. The whole process of the mat weaving was observed in a natural setting. The classroom observation and in-depth interview were taken with the participants with the help of interview guidelines and observation checklist. The data obtained from the field were categorized according to the themes regarding mathematical ideas embedded in the weaving activities, and its possibilities in teaching learning of school geometry. In this study, the mathematical activities in different sectors of their lives, their ways of understanding the natural phenomena, and their ethnomathematical knowledge were analyzed with the notions of pluralism. From the field data, it was found that the mat weaver exhibited sophisticated geometrical ideas in the process of construction of frame of mat. They used x-test method for confirming if the mat is rectangular. Mat also provides a good opportunity to understand the space geometry. A rectangular form of mat may be rolled up when it is not in use and can be converted to a cylindrical form, which usually can be used as larder so as to reserve food grains. From the observation of the situations, this cultural experience enables students to calculate volume, curved surface area and total surface area of the cylinder. The possibilities of incorporation of these cultural activities and its pedagogical use were observed in mathematics classroom. It is argued that it is possible to use mat weaving activities in the teaching and learning of school geometry.

Keywords: ethnography, ethnomathematics, geometry, mat weaving, school pedagogy

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Ioannis Andrianakis, Manolis Falelakis, Maria Pavlidou, Konstantinos Papakonstantinou, Ermioni Avramidou, Dimitrios Kalogiannis, Nikolaos Milios, Katerina Bountakidou, Kiriakos Chatzidimitriou, Panagiotis Panagiotopoulos

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Artistic events, such as concerts and performances, are challenging to organize because they involve many people with different skill sets. Small and medium venues often struggle to afford the costs and overheads of booking and hosting remote artists, especially if they lack sponsors or subsidies. This limits the opportunities for both venues and artists, especially those outside of big cities. However, more and more research shows that audiences prefer smaller-scale events and concerts, which benefit local economies and communities. To address this challenge, our project “PerformAnts: Digital Event Manager-Organizer” aims to develop a smart digital tool that automates and optimizes the processes and costs of live shows and tours. By using machine learning, applying best practices and training users through workshops, our platform offers a comprehensive solution for a growing market, enhances the mobility of artists and the accessibility of venues and allows professionals to focus on the creative aspects of concert production.

Keywords: event organization, creative industries, event promotion, machine learning

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Authors: Piyush Sharma, Devi Prasad Sharma, Sulabh Bansal

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Diseases are fairly common in fruits and vegetables because of the changing climatic and environmental circumstances. Crop diseases, which are frequently difficult to control, interfere with the growth and output of the crops. Accurate disease detection and timely disease control measures are required to guarantee high production standards and good quality. In India, apples are a common crop that may be afflicted by a variety of diseases on the fruit, stem, and leaves. It is fungi, bacteria, and viruses that trigger the early symptoms of leaf diseases. In order to assist farmers and take the appropriate action, it is important to develop an automated system that can be used to detect the type of illnesses. Machine learning-based image processing can be used to: this research suggested a system that can automatically identify diseases in apple fruit and apple plants. Hence, this research utilizes the hybrid SVM-RBN model. As a consequence, the model may produce results that are more effective in terms of accuracy, precision, recall, and F1 Score, with respective values of 96%, 99%, 94%, and 93%.

Keywords: fruit plant disease, crop disease, machine learning, image processing, SVM-RBN

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Authors: Leah Li Echiverri, Haoyu Shang, Yue Li

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Wenzhou-Kean University (WKU) is a Sino-US Cooperative University in China. It practices the English Immersion Program (EIP), where all the courses are taught in English. Class discussions and presentations are pervasively interwoven in designing students’ learning experiences. This WKU model has brought positive influences on students and is in some way ahead of traditional college English majors. However, literature to support the perceptions on the positive outcomes of this teaching and learning model remain scarce. The distinctive profile of Chinese-ESL students in an English Medium of Instruction (EMI) environment contributes further to the scarcity of literature compared to existing studies conducted among ESL learners in Western educational settings. Hence, the study investigated the students’ perceptions towards the English Immersion Program and determine how it influences Chinese-ESL students’ academic performance (AP). This research can provide empirical data that would be helpful to educators, teaching practitioners, university administrators, and other researchers in making informed decisions when developing curricular reforms, instructional and pedagogical methods, and university-wide support programs using this educational model. The purpose of the study was to establish the relationship between the English Immersion Program and Academic Performance among Chinese-ESL students enrolled at WKU for the academic year 2020-2021. Course length, immersion location, course type, and instructional design were the constructs of the English immersion program. English language learning, learning efficiency, and class participation were used to measure academic performance. Descriptive-correlational design was used in this cross-sectional research project. A quantitative approach for data analysis was applied to determine the relationship between the English immersion program and Chinese-ESL students’ academic performance. The research was conducted at WKU; a Chinese-American jointly established higher educational institution located in Wenzhou, Zhejiang province. Convenience, random, and snowball sampling of 283 students, a response rate of 10.5%, were applied to represent the WKU student population. The questionnaire was posted through the survey website named Wenjuanxing and shared to QQ or WeChat. Cronbach’s alpha was used to test the reliability of the research instrument. Findings revealed that when professors integrate technology (PowerPoint, videos, and audios) in teaching, students pay more attention. This contributes to the acquisition of more professional knowledge in their major courses. As to course immersion, students perceive WKU as a good place to study, providing them a high degree of confidence to talk with their professors in English. This also contributes to their English fluency and better pronunciation in their communication. In the construct of designing instruction, the use of pictures, video clips, and professors’ non-verbal communication, and demonstration of concern for students encouraged students to be more active in-class participation. Findings on course length and academic performance indicated that students’ perception regarding taking courses during fall and spring terms can moderately contribute to their academic performance. In conclusion, the findings revealed a significantly strong positive relationship between course type, immersion location, instructional design, and academic performance.

Keywords: class participation, English immersion program, English language learning, learning efficiency

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Authors: Katarzyna Pasternak

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Experiencing the meaning of life is widely recognised as a vital element of well-being and central human motivation. Studies have shown that a higher meaning of life is associated, among other things, with a higher quality of life, higher levels of happiness and better declared health. The subject of the study is the meaning in life measured with The Meaning in Life Questionnaire and the presence of such emotions as nostalgia, awe and hope, and the content of imaginations measured after temporal mental simulations in Americans and Poles. The respondents had to imagine themselves in future, in 40 years and describe two events that would take place at that time. Next, participants assessed the importance of the events described by them, recognised whether during their journey through time they felt awe, hope and nostalgia, and answered the questionnaire examining the meaning in life. 204 (102 from Poland 102 from the USA ) people aged 21 to 60 participated in the study. The study checked whether there were differences in the content of the imaginations of the respondents from Poland and USA, and whether there were statistically significant difference between the declared sense of meaning in life among participants from both countries. The result of the study hane shown that there were no differences in the overall result obtained by the participants in The Meaning in Life Questionnaire , while there were statistically significant differences among the subscales of the questionnaire. It turned out that Americans have a higher presence of meaning in life than Poles, but they obtained lower results in searching of meaning in life. Studies have also shown that there was a statistically significant difference between Poles and Americans in feeling awe after a mental simulation. Poles felt higher level of awe. Images about the future differed between Poles and Americans. Poles judged that the events they described were very important to them. Interestingly, the content of American participants’ imaginations was dominated by topics related to the future of the world, ecology and world peace. There were also ideas about nice moments spent with friends and family. Among Poles, ideas related to professional career and development as well as family events dominated. Research shows that despite the lack of differences in the general meaning in life, Poles are more focused on searching for meaning in life than Americans. The study shows interesting differences between the two cultures.

Keywords: meaning in life, mental simulations, imaginations, temporal mental simulations, future, cultural differences

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Authors: Durvi Yogesh Vagani

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This research paper explores the effectiveness of Research-informed teaching and Case-based teaching in enhancing the retention and recall of memory during discussions among university students. Additionally, it investigates the impact of using Artificial Intelligence (AI) tools on the quality of research conducted by students and its correlation with better recollection. The study hypothesizes that Case-based teaching will lead to greater recall and storage of information. The research gap in the use of AI in educational settings, particularly with actual participants, is addressed by leveraging a multi-method approach. The hypothesis is that the use of AI, such as ChatGPT and Bard, would lead to better retention and recall of information. Before commencing the study, participants' attention levels and IQ were assessed using the Digit Span Test and the Wechsler Adult Intelligence Scale, respectively, to ensure comparability among participants. Subsequently, participants were divided into four conditions, each group receiving identical information presented in different formats based on their assigned condition. Following this, participants engaged in a group discussion on the given topic. Their responses were then evaluated against a checklist. Finally, participants completed a brief test to measure their recall ability after the discussion. Preliminary findings suggest that students who utilize AI tools for learning demonstrate improved grasping of information and are more likely to integrate relevant information into discussions compared to providing extraneous details. Furthermore, Case-based teaching fosters greater attention and recall during discussions, while Research-informed teaching leads to greater knowledge for application. By addressing the research gap in AI application in education, this study contributes to a deeper understanding of effective teaching methodologies and the role of technology in student learning outcomes. The implication of the present research is to tailor teaching methods based on the subject matter. Case-based teaching facilitates application-based teaching, and research-based teaching can be beneficial for theory-heavy topics. Integrating AI in education. Combining AI with research-based teaching may optimize instructional strategies and deepen learning experiences. This research suggests tailoring teaching methods in psychology based on subject matter. Case-based teaching suits practical subjects, facilitating application, while research-based teaching aids understanding of theory-heavy topics. Integrating AI in education could enhance learning outcomes, offering detailed information tailored to students' needs.

Keywords: artificial intelligence, attention, case-based teaching, memory recall, memory retention, research-informed teaching

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

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This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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Authors: Liliana Villavicencio, Yohn Garcia, Pallavi Singh, Luis Fernando Cruz, Wilfrido Moreno

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Psychophysiological adaptive automation is a concept that combines human physiological data and computer algorithms to create personalized interfaces and experiences for users. This approach aims to enhance human learning by adapting to individual needs and preferences and optimizing the interaction between humans and machines. According to neurosciences, the working memory demand during the student learning process is modified when the student is learning a new subject or topic, managing and/or fulfilling a specific task goal. A sudden increase in working memory demand modifies the level of students’ attention, engagement, and cognitive load. The proposed psychophysiological adaptive automation system will adapt the task requirements to optimize cognitive load, the process output variable, by monitoring the student's brain activity. Cognitive load changes according to the student’s previous knowledge, the type of task, the difficulty level of the task, and the overall psychophysiological state of the student. Scaling the measured cognitive load as low, medium, or high; the system will assign a task difficulty level to the next task according to the ratio between the previous-task difficulty level and student stress. For instance, if a student becomes stressed or overwhelmed during a particular task, the system detects this through signal measurements such as brain waves, heart rate variability, or any other psychophysiological variables analyzed to adjust the task difficulty level. The control of engagement and stress are considered internal variables for the hypermedia system which selects between three different types of instructional material. This work assesses the feasibility of a fuzzy controller to track a student's physiological responses and adjust the learning content and pace accordingly. Using an industrial automation approach, the proposed fuzzy logic controller is based on linguistic rules that complement the instrumentation of the system to monitor and control the delivery of instructional material to the students. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the delivery of academic content based on the working memory demand without compromising students’ health. This work has a potential application in the instructional design of virtual reality environments for training and education.

Keywords: fuzzy logic controller, hypermedia control system, personalized education, psychophysiological adaptive automation

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Authors: Rasha Elhawari

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Heritage language learners (HLL) are part of the linguistic reality in Foreign Language Learning (FLL). These learners present several characteristics that are different from non-heritage language learners. They have a personal connection with the language and their motivation to learn the language is partly because of this personal connection. In Canada there is a large diversity in the foreign language learning classroom; the Arabic language classroom is no exception. The Arabic HLL is unique for more than one reason. First, is the fact that the Arabic language is spoken across twenty-two Arab countries across the Arab World. Across the Arab World there is a standard variation and a local dialect that co-exist side by side, i.e. diaglossia exists in a strong and unique way as a feature of Arabic. Second, Arabic is the language that all Muslims across the Muslim World use for their prayers. This raises a number of points when we consider Arabic as a Heritage Language; namely the role of diaglossia, culture and religion. The fact that there is a group of leaners that can be regarded as HLL who are not of Arabic speaking background but are Muslims and use the language for religious purposes is unique, thus course developers and language instructors need take this into consideration. The paper takes a closer look at this distinction and establishes sub-groups the Arabic HLLs in a language and/or culture specific way related mainly to the Arabic HLL. It looks at the learners at the beginners’ Arabic class at the undergraduate university level over a period of three years in order to define this learner. Learners belong to different groups and backgrounds but they all share common characteristics. The paper presents a detailed look at the learner types present at this class in order to help prepare and develop material for this specific learner group. The paper shows that separate HLL and non-HLL courses, especially at the introductory and intermediate level, is successful in resolving some of the pedagogical problems that occur in the Arabic as a Foreign Language classroom. In conclusion, the paper recommends the development of HLL courses at the early levels of language learning. It calls for a change in the pedagogical practices to overcome some of the challenges learner in the introductory Arabic class can face.

Keywords: Arabic, Heritage Language, langauge learner, teaching

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Authors: Arash Khosravi, Mohamad Nazir Ahmad

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Today’s economy is a knowledge-based economy in which knowledge is a crucial facilitator to individuals, as well as being an instigator of success. Due to the impact of globalization, universities face new challenges and opportunities. Accordingly, they ought to be more innovative and have their own competitive advantages. One of the most important goals of universities is the promotion of students as professional knowledge workers. Therefore, knowledge sharing and transferring at tertiary level between students and supervisors is vital in universities, as it decreases the budget and provides an affordable way of doing research. Knowledge-sharing impact factors can be categorized into three groups, namely: organizational, individual and technical factors. There are some individual barriers to knowledge sharing, namely: lack of time and trust, lack of communication skills and social networks. IT systems such as e-learning, blogs and portals can increase knowledge sharing capability. However, it must be stated that IT systems are only tools and not solutions. Individuals are still responsible for sharing information and knowledge. This paper proposes new research model to examine the effect of individual factors and organisational factors, namely: learning strategy, trust culture, supervisory support, as well as technological factor on knowledge sharing in a research supervision process at the University of Technology Malaysia.

Keywords: knowledge management, knowledge sharing, research supervision, knowledge transferring

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Authors: Paul Flynn, Luke O’Donnell

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There is a pressing societal need for educators in formal and non-formal settings to develop pedagogical frameworks, programmes, and interventions that support the development of transversal skills for life beyond the classroom. These skills include communication, collaboration, interpersonal relationship building, problem-solving, and planning, and organizational skills; or lifeworld literacies encountered first hand. This is particularly true for young people aged between 15-18. This demographic represents both the future of society and those best positioned to take advantage of well-designed, structured educational supports within and across formal and non-formal settings. Secondary school history has been identified as an appropriate area of study which deftly develops many of those transversal skills so crucial to positive societal engagement. However, in the formal context, students often challenge history’s relevance to their own lived experience and dismiss it as a study option. In response to such challenges, teachers will often design stimulating lessons which are often well-received. That said, some students continue to question modern-day connections, presenting a persistent and pervasive classroom distraction. The continuing decline in numbers opting to study second-level history indicates an erosion of what should be a critical opportunity to develop all-important lifeworld literacies within formal education. In contrast, students readily acknowledge relevance in non-formal settings where many participants meaningfully engage with history by way of student-focused activities. Furthermore, many do so without predesigned pedagogical aids which support transversal skills development as embodied learning outcomes. As this paper will present, there is a dearth of work pertaining to the circular subject of history and its embodied learning outcomes, including lifeworld literacies, in formal and non-formal settings. While frequently challenging to reconcile formal (often defined by strict curricula and examination processes), and non-formal engagement with history, opportunities do exist. In the Irish context, this is exemplified by a popular university outreach programme: breaking the SEAL. This programme supports second-level history students as they fulfill curriculum requirements in completing a research study report. This report is a student-led research project pulling on communication skills, collaboration with peers and teachers, interpersonal relationships, problem-solving, and planning and organizational skills. Completion of this process has been widely recognized as excellent preparation not only for higher education (third level) but work-life demands as well. Within a formal education setting, the RSR harnesses non-formal learning virtues and exposes students to limited aspects of independent learning that relate to a professional work setting –a lifeworld literacy. Breaking the SEAL provides opportunities for students to enhance their lifeworld literacy by engaging in an independent research and learning process within the protective security of the classroom and its teacher. This paper will highlight the critical role this programme plays in preparing participating students (n=315) for life after compulsory education and presents examples of how lifeworld literacies may be developed through a scaffolded process of historical research and reporting anchored in non-formal contexts.

Keywords: history, education, literacy, transversal skills

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Authors: Wenzheng Fu, Yue Fu, Zhijiang Dong, Yujian Fu

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The integration of artificial intelligence (AI) and machine learning (ML) into project management is transforming how engineering projects are executed, monitored, and controlled. This paper provides a comprehensive survey of AI and ML applications in project management, systematically categorizing their use in key areas such as project data analytics, monitoring, tracking, scheduling, and reporting. As project management becomes increasingly data-driven, AI and ML offer powerful tools for improving decision-making, optimizing resource allocation, and predicting risks, leading to enhanced project outcomes. The review highlights recent research that demonstrates the ability of AI and ML to automate routine tasks, provide predictive insights, and support dynamic decision-making, which in turn increases project efficiency and reduces the likelihood of costly delays. This paper also examines the emerging trends and future opportunities in AI-driven project management, such as the growing emphasis on transparency, ethical governance, and data privacy concerns. The research suggests that AI and ML will continue to shape the future of project management by driving further automation and offering intelligent solutions for real-time project control. Additionally, the review underscores the need for ongoing innovation and the development of governance frameworks to ensure responsible AI deployment in project management. The significance of this review lies in its comprehensive analysis of AI and ML’s current contributions to project management, providing valuable insights for both researchers and practitioners. By offering a structured overview of AI applications across various project phases, this paper serves as a guide for the adoption of intelligent systems, helping organizations achieve greater efficiency, adaptability, and resilience in an increasingly complex project management landscape.

Keywords: artificial intelligence, decision support systems, machine learning, project management, resource optimization, risk prediction

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Authors: Ioannis Souris

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In this paper, we would like to present how we taught German through playwriting and analyze the usefulness of this method for teaching languages and cultivating a sense of political agency in students and teachers alike. Last academic year, we worked at the German Saturday School in Greenwich, London. This school offers Saturday German lessons to children whose parents are German, living in London. The lessons are two hours long, and the children’s level of German varies according to how often or how much German is spoken at home or how often the families visit Germany (as well as other factors which will be discussed in more detail in the paper). The directors of the school provide teachers with learning material and course books, but they strongly encourage individual input on lesson structure and methods of teaching German. The class we taught consisted of six eight-to-nine-year-olds. Midway into the academic year, we ran out of teaching material, and we, therefore, decided to write a play. In the paper, we would like to explore the process we followed in creating or writing this play and how this encouraged the children to collaborate and exercise their skills in writing, storytelling, speaking, and opinion-sharing. We want to examine the impact this project had on the children who wrote and performed the play, the wider community of the Saturday school, and the development of our language teaching practice. We found, for instance, that some students, who were quiet or shy, became very open and outspoken in the process of writing and performing the play. They took the initiative and led the process, putting us, their teachers, in the role of simple observers or facilitators. When we showed the play in front of the school, the other children and teachers, as audience members, also became part of the process as they commented on the plot, language, and characters and gave feedback on further development. In the paper, we will discuss how this teaching project fits into recent developments in the research of creativity and the teaching of languages and how engagement with creative approaches to teaching has the potential to question and subvert traditional notions of ‘lesson’, ‘teacher’, and ‘student’. From the moment a questioning of norms takes place, we inadvertently raise questions about politics, agency, and resistance. We will conclude the paper with a definition of what we mean by ‘political agency’ within the context of our teaching project and education, in general, and why inspiring creativity and imagination within teaching can be considered a political act. Finally, our aim in this paper will be to propose the possibility of analyzing teaching languages through creativity and political agency theories.

Keywords: innovation in language teaching and learning, language acquisition and learning, language curriculum development, language education

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Authors: Farouk A. N. Bouhadiba

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This work addresses the issue of the LMD(Licence, Master, Doctorat) in Algeria and the impact it has had on education quality in terms of educational affect and effect. It starts with a brief introduction to the financial means, the educational settings, and the social environment in place when the LMD was institutionalized in Algeria (2003-2004). Some factors for the success or failure of this top-down institutional endeavor are examined and analyzed. These include – among other factors - the teacher/student attitudes, apprehensions, and motivations on the one hand and the institutional euphoria for the LMD in Algeria on the other hand. Some issues at stake are discussed. More specifically, the professional versus the student affect on today’s attitudes, interests, and values is examined as a result of nearly two decades of LMD teaching and learning in Algerian universities. We shall then present some official curricula that, in terms of content, reflect the spirit, principles, and architectures of the LMD but which, in reality, are partially, if not fully, set aside when it comes to teaching practices, learning behaviors, motivation, and evaluation. The discussion on effect highlights attitudinal, developmental, and social markers that are indicative of the extent to which Education Quality in Algeria has been positively or negatively affected by the implementation of the LMD.

Keywords: LMD bachelor's masters doctorat, affects and effects, education quality, Algeria

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Authors: Narumon Jiwattanasuk, Phrakrupalad Pannavoravat, Pataraporn Sirikanchana

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Mindfulness has become a perspective addressing positive wellbeing these days. The aims of this paper are to analyze the problems of executive meditation practitioners at the Buddhamahametta Foundation in Thailand and to provide recommendations on the process to develop peaceful wellbeing in executive meditation practitioners by applying the principles of the four foundations of mindfulness. This study is particularly focused on executives because there is not much research focusing on the well-being development of executives, and the researcher recognizes that executives can be an example within their organizations. This would be a significant influence on their employees and their families to be interested in practicing mindfulness. This improvement will then grow from an individual to the surrounding community such as family, workplace, society, and the nation. This would lead to happiness at the national level, which is the expectation of this research. The paper highlights mindfulness practices that can be performed on a daily basis. This study is qualitative research, and there are 10 key participants who are executives from various sectors such as hospitality, healthcare, retail, power energy, and so on. Three mindfulness-based courses were conducted over a period of 8 months, and in-depth interviews were done before the first course as well as at the end of every course. In total, four in-depth interviews were conducted. The information collected from the interviews was analyzed in order to create the process to develop peaceful well-being. Focus group discussions with the mindfulness specialists were conducted to help develop the mindfulness program as well. As a result of this research, it is found that the executives faced the following problems: stress, negative thinking loops, losing temper, seeking acceptance, worry about uncontrollable external factors, unable to control their words, and weight gain. The cultivation of the four foundations of mindfulness can develop peaceful wellbeing. The results showed that after the key informant executives attended the mindfulness courses and practiced mindfulness regularly, they have developed peaceful well-being in all aspects such as physical, psychological, behavioral, and intellectual by applying 12 mindfulness-based activities. The development of wellbeing, in the conclusion of this study, also includes various tools to support the continuing practice, including the handout of guided mindfulness practice, VDO clips about mindfulness practice, the online dhamma channel, and mobile applications to support regular mindfulness-based practices.

Keywords: executive, mindfulness activities, stress, wellbeing

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Authors: Nassrin Badrkhani

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Current paper was designed aiming at finding the impacts of play therapy on the development of mentally retarded students in elementary school. The sample included 191 elementary students from 5 classes. Sixty students were chosen from each class, and based on their learning capabilities, they were further assigned into similar control and treatment groups. Then, five groups received treatments with special types of games, instruments, and methods for two months. The teacher-made instruments in literature, math, and science were adopted after their content validity had been confirmed by experienced teachers. The findings were analyzed in both descriptive, including mean, median, and standard deviation, and interpretive levels, using covariance analysis in SPSS. The results were indicative of the fact that play therapy (individual and group games) was positively effective in mentally retarded students’ educational development. Moreover, regarding P ˂0/001, it was found that group games were more influential than individual ones. It was also clear that the students’ gender played no role in this kind of treatment. Therefore, it is highly recommended to implement play therapy as a part of the educational curriculum for mentally retarded pupils.

Keywords: development, education, learning, play therapy, student, teacher

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Authors: William Marquis, Fang Dong

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There has been a growing concern in recent years over the economic and social effects of adult video gaming. It has been estimated that the number of people who played video games during the COVID-19 pandemic is close to three billion, and there is evidence that this form of entertainment is here to stay. Many people are concerned that this growing use of time could crowd out time that could be spent on alternative forms of entertainment with family, friends, sports, and other social activities that build community. For example, recent studies of children suggest that playing videogames crowds out time that could be spent on homework, watching TV, or in other social activities. Similar studies of adults have shown that video gaming is negatively associated with earnings, time spent at work, and socializing with others. The primary objective of this paper is to examine how time adults spend on video gaming could displace time they could spend working and on activities that enhance their health and well-being. We use data from the American Time Use Survey (ATUS), maintained by the Bureau of Labor Statistics, to analyze the effects of time-use decisions on three measures of well-being. We pool the ATUS Well-being Module for multiple years, 2010, 2012, 2013, and 2021, along with the ATUS Activity and Who files for these years. This pooled data set provides three broad measures of well-being, e.g., health, life satisfaction, and emotional well-being. Seven variants of each are used as a dependent variable in different multivariate regressions. We add to the existing literature in the following ways. First, we investigate whether the time adults spend in video gaming crowds out time spent working or in social activities that promote health and life satisfaction. Second, we investigate the relationship between adult gaming and their emotional well-being, also known as negative or positive affect, a factor that is related to depression, health, and labor market productivity. The results of this study suggest that the time adult gamers spend on video gaming has no effect on their supply of labor, a negligible effect on their time spent socializing and studying, and mixed effects on their emotional well-being, such as increasing feelings of pain and reducing feelings of happiness and stress.

Keywords: online gaming, health, social capital, emotional wellbeing

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Authors: Mikiale Gebreslase Gebremariam, Chai Huaqi, Tesfay Gebretsdkan Gebremichael, Dawit Nega Bekele

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In recent years, brownfield redevelopment projects (BRPs) have contributed to the overarching paradigm of the United Nations 2030 agendas. In the present circumstance, most developed nations adopted BRPs, an efficacious urban policy tool. However, in developing and some advanced countries, BRPs are lacking due to limitations of awareness, policy tools, and financial capability for cleaning up brownfield sites. For example, the growth and development of Ethiopian cities were achieved at the cost of poor urban planning, including no community consultations and excessive urbanization for future growth. The demand for land resources is more and more urgent as the result of an intermigration to major cities and towns for socio-economic reasons and population growth. In the past, the development mode of spreading major cities has made horizontal urbanizations stretching outwards. Expansion in search of more land resources, while the outer cities are growing, the inner cities are polluted by environmental pollution. It is noteworthy that the rapid development of cities has not brought about an increase in people's happiness index. Thus, the proposed management framework for managing brownfields in Ethiopia as a lesson to the developing nation facing similar challenges and growth will add immense value in solving the problems and give insights into brownfield land utilization. Under the umbrella of the grey incidence decision-making model and with the consideration of multiple stakeholders and tight environmental and economic constraints, the proposed management framework integrates different criteria from economic, social, environmental, technical, and risk aspects into the grey incidence decision-making model and gives useful guidance to manage brownfields in Ethiopia. Furthermore, it will contribute to the future development of the social economy and the missions of the 2030 UN sustainable development goals.

Keywords: Brownfields, environmental sustainability, Ethiopia, grey-incidence decision-making, sustainable urban development

Procedia PDF Downloads 91

Authors: Tamas Madl

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Sudden cardiac death (SCD) constitutes a large proportion of cardiovascular mortalities, provides little advance warning, and the risk is difficult to recognize based on ubiquitous, low cost medical equipment such as the standard, 12-lead, ten second ECG. Autonomic abnormalities have been shown to be strongly predictive of SCD risk; yet current methods are not trivially applicable to the brevity and low temporal and electrical resolution of standard ECGs. Here, we build horizontal visibility graph representations of very short inter-beat interval time series, and perform unsuper- vised representation learning in order to convert these variable size objects into fixed-length vectors preserving similarity rela- tions. We show that such representations facilitate classification into healthy vs. at-risk patients on two different datasets, the Mul- tiparameter Intelligent Monitoring in Intensive Care II and the PhysioNet Sudden Cardiac Death Holter Database. Our results suggest that graph representation learning of heartbeat interval time series facilitates robust classification even in sequences as short as ten seconds.

Keywords: sudden cardiac death, heart rate variability, ECG analysis, time series classification

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Authors: Muna Shafiq

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Growth in international immigration is a reflection of increased migration patterns in Canada and in other parts of the world. Canada continues to promote itself as a bilingual country, yet the bilingual French and English population numbers do not reflect this platform. Each province’s integration policies focus only on second language learning of either English or French. Moreover, since English Canadians outnumber French Canadians, maintaining, much less increasing, English-French bilingualism appears unrealistic. One solution to increasing Canadian bilingualism requires creating intercultural communication initiatives between youth in Quebec and the rest of Canada. Specifically, the focus is on active, experiential learning, where intercultural competencies develop outside traditional classroom settings. The target groups are Generation Y Millennials and Generation Z Linksters, the next generations in the career and parenthood lines. Today, Canada’s education system, like many others, must continually renegotiate lines between programs it offers its immigrant and native communities. While some purists or right-wing nationalists would disagree, the survival of bilingualism in Canada has little to do with reducing immigration. Children and youth immigrants play a valuable role in increasing Canada’s French and English speaking communities. For instance, a focus on more immersion, over core French education programs for immigrant children and youth would not only increase bilingual rates; it would develop meaningful intercultural attachments between Canadians. Moreover, a vigilant increase of funding in French immersion programs is critical, as are new initiatives that focus on experiential language learning for students in French and English language programs. A favorable argument supports the premise that other than French-speaking students in Québec and elsewhere in Canada, second and third generation immigrant students are excellent ambassadors to promote bilingualism in Canada. Most already speak another language at home and understand the value of speaking more than one language in their adopted communities. Their dialogue and participation in experiential language exchange workshops are necessary. If the proposed exchanges take place inter-provincially, the momentum to increase collective regional voices increases. This regional collectivity can unite Canadians differently than nation-targeted initiatives. The results from an experiential youth exchange organized in 2017 between students at the crossroads of Generation Y and Generation Z in Vancouver and Quebec City respectively offer a promising starting point in assessing the strength of bringing together different regional voices to promote bilingualism. Code-switching between standard, international French Vancouver students, learn in the classroom versus more regional forms of Quebec French spoken locally created regional connectivity between students. The exchange was equally rewarding for both groups. Increasing their appreciation for each other’s regional differences allowed them to contribute actively to their social and emotional development. Within a sociolinguistic frame, this proposed model of experiential learning does not focus on hands-on work experience. However, the benefits of such exchanges are as valuable as work experience initiatives developed in experiential education. Students who actively code switch between French and English in real, not simulated contexts appreciate bilingualism more meaningfully and experience its value in concrete terms.

Keywords: experiential learning, intercultural communication, social and emotional learning, sociolinguistic code-switching

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Authors: Ricky Leung

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Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

Procedia PDF Downloads 89

Authors: Yasàlu Haruna

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In every transaction, the use of language is central regardless of form or complexity if any meaning is expected to be harvested therefrom. Students constituting a population group in the learning landscape of Nigeria occupy a central position with a propensity to excel or otherwise in the context of communication, especially in the learning process and social interaction. The nature or quantum of anxiety or confidence in speaking a second language is not only peculiar to societies where the second language is not an official language but to a degree, the linguistic gap created by adoption and adaptation syndrome manifests in created anxiety or lack of confidence especially where mastery of a spoken language becomes a major challenge. This paper explores the manner in which linguistic complexity and cultural barriers combine to widen the adaptation and adoption gap. In much the same way, typical issues of pronouncement, intonation and accent difficulties are vital variables that explain the root cause of anxiety. Using a combination of primary and secondary sources of data expressed in questionnaires, key informant interviews and other available data, the paper concludes that the non-integration of anxiety possibility into the education delivery framework has left a lot to be needed in cultivating second language speakers among students of Nigerian Colleges of Education. In addition, cultural barriers and the absence of integration interfaces in the course of learning within and outside the classroom contribute to further widening the gap. Again, colleagues/mates/conversation partners' mastery of a second language remains a contributory factor largely due to the quality of the preparatory school system in many parts of the country. The paper recommends that national policies and frameworks must be reviewed to consider integration windows where culture and conversation partner deficiencies can be remedied through educational events such as debates, quizzes and symposia; improvements can be attained while commercial advertisements are tailored towards seeking for adoption of second language in commerce and major cultural activities.

Keywords: cultural barriers, integration, college of education and adaptation, second language

Procedia PDF Downloads 92

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

Procedia PDF Downloads 60

Authors: Valery Yakubovich, Shuping wu

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Organization theorists have long claimed that organizational innovations are nontechnological, in part because they are unpatentable. The claim rests on the assumption that organizational innovations are abstract ideas embodied in persons and contexts rather than in context-free practical tools. However, over the last three decades, organizational knowledge has been increasingly embodied in digital tools which, in principle, can be patented. To provide the first empirical evidence regarding the patentability of organizational innovations, we trained two machine learning algorithms to identify a population of 205,434 patent applications for organizational technologies (OrgTech) and, among them, 141,285 applications that use organizational innovations accumulated over the 20th century. Our event history analysis of the probability of patenting an OrgTech invention shows that ideas from organizational innovations decrease the probability of patent allowance unless they describe a practical tool. We conclude that the present-day digital transformation places organizational innovations in the realm of high tech and turns the debate about organizational technologies into the challenge of designing practical organizational tools that embody big ideas about organizing. We outline an agenda for patent-based research on OrgTech as an emerging phenomenon.

Keywords: organizational innovation, organizational technology, high tech, patents, machine learning

Procedia PDF Downloads 122

Authors: Jan Schmitt, Sophie Fischer

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After the Corona pandemic, climate change is a further, long-lasting challenge the society must deal with. An ongoing climate change need to be prevented. Nevertheless, the adoption tothe already changed climate conditionshas to be focused in many sectors. Recently, the decisive role of the economic sector with high value added can be seen in the Corona crisis. Hence, manufacturing industry as such a sector, needs to be prepared for climate change and adaption. Several examples from the manufacturing industry show the importance of a strategic effort in this field: The outsourcing of a major parts of the value chain to suppliers in other countries and optimizing procurement logistics in a time-, storage- and cost-efficient manner within a network of global value creation, can lead vulnerable impacts due to climate-related disruptions. E.g. the total damage costs after the 2011 flood disaster in Thailand, including costs for delivery failures, were estimated at 45 billion US dollars worldwide. German car manufacturers were also affected by supply bottlenecks andhave close its plant in Thailand for a short time. Another OEM must reduce the production output. In this contribution, a game-based learning approach is presented, which should enable manufacturing companies to derive their own strategies for climate adaption out of a mix of different actions. Based on data from a regional study of small, medium and large manufacturing companies in Mainfranken, a strongly industrialized region of northern Bavaria (Germany) the game-based learning approach is designed. Out of this, the actual state of efforts due to climate adaption is evaluated. First, the results are used to collect single actions for manufacturing companies and second, further actions can be identified. Then, a variety of climate adaption activities can be clustered according to the scope of activity of the company. The combination of different actions e.g. the renewal of the building envelope with regard to thermal insulation, its benefits and drawbacks leads to a specific strategy for climate adaption for each company. Within the game-based approach, the players take on different roles in a fictionalcompany and discuss the order and the characteristics of each action taken into their climate adaption strategy. Different indicators such as economic, ecologic and stakeholder satisfaction compare the success of the respective measures in a competitive format with other virtual companies deriving their own strategy. A "play through" climate change scenarios with targeted adaptation actions illustrate the impact of different actions and their combination onthefictional company.

Keywords: business strategy, climate change, climate adaption, game-based learning

Procedia PDF Downloads 207

Authors: Yael Leibovitch

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Teaching academic writing is a source of concern for secondary schools. Many students struggle to meet the basic standards of literacy while teacher confidence in this arena remains low. These issues are compounded by the conventionally prescriptive character of writing instruction, which fails to engage student writers. At the same time, a growing body of research on dialogic teaching has highlighted the powerful role of talk in student learning. With the intent of enhancing pedagogical capability, this paper shares finding from a co-inquiry case study that investigated how teachers think about and negotiate classroom discourse to position students as effective academic writers and thinkers. Using a range of qualitative methods, this project closely documents the iterative collaboration of educators as they sought to create more opportunities for dialogic engagement. More specifically, it triangulates both teacher and student data regarding the efficacy of interdependent thinking and collaborative reasoning as organizing principals for literacy learning. Findings indicate that a dialogic teaching repertoire helps to develop the cognitive and metacognitive skills of adolescent writers. In addition, they underscore the importance of sustained professional collaboration to the uptake of new writing pedagogies.

Keywords: dialogic teaching, writing, teacher professional development, student literacy

Procedia PDF Downloads 213